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Find video protocols related to scientific articles indexed in Pubmed.
Procyanidins protects against oxidative damage and cognitive deficits after traumatic brain injury.
Brain Inj
PUBLISHED: 10-04-2014
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Abstract Primary objective: Oxidative stress is the principal factor in traumatic brain injury (TBI) that initiates the events that result in protracted neuronal dysfunction and remodeling. Importantly, antioxidants can protect the brain against oxidative damage and modulate the capacity of the brain to cope with synaptic dysfunction and cognitive impairment. Research design: To date, however, no studies have investigated the effects of procyanidins (PC) on cognitive deficits after TBI. Methods and Procedures: In the present study, rats with controlled cortical impact (CCI) were used to investigate the protective effects of procyanidins. Main outcomes and results: The results showed that procyanidins reduced the level of malondialdehyde (MDA) and elevated the level of glutathione (GSH) and the activity of superoxide dismutase (SOD). In addition, treatment with procyanidins, which elevated the levels of brain-derived neurotropic factor (BDNF), phosphorylation-cAMP-response element binding protein (pCREB), total CREB, and cyclic AMP (cAMP), improved cognitive performance in the Morris water maze after TBI. Conclusions: These results suggest that procyanidins appear to counteract oxidative damage and behavioral dysfunction after TBI through antioxidant activity and the up-regulation of cAMP/CREB signaling.
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Using methyl as substituted-radical in n-phen enhances the anticancer activities of [(DMF)Cu(n-phen)(NO3(-))2].
J. Inorg. Biochem.
PUBLISHED: 08-07-2014
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In order to seek better ligand for anticancer drug, we choose 1,10-phenanthroline (phen) and 2,9-dimethyl-1,10-phenanthroline (2,9-dmp) as predominant ligands, and synthetize two complexes:[(DMF)Cu(phen)(NO3)2] (1) and [(DMF)Cu(2,9-dmp)(NO3)2] (2) (DMF is dimethyl formamide). As for the five kinds of cancer cells, including A-549, Bel-7402, HCT-8, MDCK and L-1210 cells, our complexes showed higher inhibition ratio compared with anticancer drug 5-Fu (fluorouracil), ligand phenanthroline and Cu(NO3)2. It's worth noting that complex 2's anticancer activity is much more efficient than that of complex 1. This is because there are di-substituted-methyl in 2,9-dmp. By calculating, we found ?complexes
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Hypoxia inducible factor-1? expression is associated with hippocampal apoptosis during epileptogenesis.
Brain Res.
PUBLISHED: 08-01-2014
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Cell apoptosis can cause hippocampal neuronal loss after epileptic seizures. Hypoxia inducible factor (HIF)-1? is an important factor mediating apoptosis after brain injuries, such as cerebral ischemia and traumatic brain injures, but little research has been done on its role in the lithium chloride-pilocarpine induced epileptic model. Here, we used a rat model of pilocarpine-induced status epilepticus (SE) to investigate HIF-1? expression and apoptosis in the hippocampus, and to explore their relationship during epileptogenesis. 120 male Sprague Dawley (SD) rats were treated with lithium chloride-pilocarpine injections and divided into an experimental group (administered by MK-801) and a positive control group (administered by saline). Then the HIF-1? expression and hippocampal apoptosis were investigated by histological confirmation and western blotting at 24h, 3d, 7d and 14d, respectively. The results showed that the administration of MK-801 significantly reduced (P<0.05) HIF-1? expression and hippocampal apoptosis during epileptogenesis in comparison with the positive control. Moreover, the expression of HIF-1? and hippocampal apoptosis presented significant time-dependent changes (P<0.01) within 2 weeks, and their positive correlation (P<0.05) analyzed by Pearson?s correlation analysis. Meanwhile, the HIF-1? immunostained cells were distributed in accord with TUNEL immunostained cells and Caspase-3 immunopositive cells in the hippocampus. These results indicate that the HIF-1? expression is associated with hippocampal apoptosis, and suggest that HIF-1? is an important factor during epileptogenesis.
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A pre-injury high ethanol intake in rats promotes brain edema following traumatic brain injury.
Br J Neurosurg
PUBLISHED: 05-09-2014
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Abstract Drinking is a risk factor for traumatic brain injury (TBI), and ethanol can aggravate the outcome by promoting brain edema. The mechanism involved is not fully understood. It has been confirmed that aquaporin-4 (AQP4) and vascular endothelial growth factor (VEGF) play pivotal roles in cytotoxic/vasogenic brain edema individually, and both of these proteins are downstream regulatory factors of hypoxia-inducible factor-1? (HIF-1?). In this study, we used a fluid percussion injury (FPI) model in rats to determine the effects of acute ethanol intake on the expression levels of HIF-1?, AQP4, and VEGF prior to FPI. The animals were sacrificed 1, 2, 3, and 4 days post-injury. We found that the expression levels of HIF-1? and AQP4 were significantly upregulated in the ethanol-pretreated groups, whereas the VEGF expression level was not. In addition, there was a positive correlation between HIF-1? and AQP4. The results of this study indicate that cytotoxic brain edema may play an important role in the early stage of FPI in ethanol-pre-treated animals and that HIF-1? and AQP4 might be involved.
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Piecewise-constant and low-rank approximation for identification of recurrent copy number variations.
Bioinformatics
PUBLISHED: 03-17-2014
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The post-genome era sees urgent need for more novel approaches to extracting useful information from the huge amount of genetic data. The identification of recurrent copy number variations (CNVs) from array-based comparative genomic hybridization (aCGH) data can help understand complex diseases, such as cancer. Most of the previous computational methods focused on single-sample analysis or statistical testing based on the results of single-sample analysis. Finding recurrent CNVs from multi-sample data remains a challenging topic worth further study.
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[A report of first fatal case of H10N8 avian influenza virus pneumonia in the world].
Zhonghua Wei Zhong Bing Ji Jiu Yi Xue
PUBLISHED: 02-15-2014
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To report the treatment process of the first case of human pneumonia resulted from H10N8 avian influenza virus infection in the world for providing the data for clinical diagnosis and treatment.
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Transcriptome profile of human neuroblastoma cells in the hypomagnetic field.
Sci China Life Sci
PUBLISHED: 01-15-2014
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Research has shown that the hypomagnetic field (HMF) can affect embryo development, cell proliferation, learning and memory, and in vitro tubulin assembly. In the present study, we aimed to elucidate the molecular mechanism by which the HMF exerts its effect, by comparing the transcriptome profiles of human neuroblastoma cells exposed to either the HMF or the geomagnetic field. A total of 2464 differentially expressed genes (DEGs) were identified, 216 of which were up-regulated and 2248 of which were down-regulated after exposure to the HMF. These DEGs were found to be significantly clustered into several key processes, namely macromolecule localization, protein transport, RNA processing, and brain function. Seventeen DEGs were verified by real-time quantitative PCR, and the expression levels of nine of these DEGs were measured every 6 h. Most notably, MAPK1 and CRY2, showed significant up- and down-regulation, respectively, during the first 6 h of HMF exposure, which suggests involvement of the MAPK pathway and cryptochrome in the early bio-HMF response. Our results provide insights into the molecular mechanisms underlying the observed biological effects of the HMF.
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A Combinatorial Perspective of the Protein Inference Problem.
IEEE/ACM Trans Comput Biol Bioinform
PUBLISHED: 09-18-2013
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In a shotgun proteomics experiment, proteins are the most biologically meaningful output. The success of proteomics studies depends on the ability to accurately and efficiently identify proteins. Many methods have been proposed to facilitate the identification of proteins from peptide identification results. However, the relationship between protein identification and peptide identification has not been thoroughly explained before. In this paper, we devote ourselves to a combinatorial perspective of the protein inference problem. We employ combinatorial mathematics to calculate the conditional protein probabilities (Protein probability means the probability that a protein is correctly identified) under three assumptions, which lead to a lower bound, an upper bound and an empirical estimation of protein probabilities, respectively. The combinatorial perspective enables us to obtain an analytical expression for protein inference. Our method achieves comparable results with ProteinProphet in a more efficient manner in experiments on two datasets of standard protein mixtures and two datasets of real samples. Based on our model, we study the impact of unique peptides and degenerate peptides (degenerate peptides are peptides shared by at least two proteins) on protein probabilities. Meanwhile, we also study the relationship between our model and ProteinProphet.
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Bioinformatic analysis of data generated from MALDI mass spectrometry for biomarker discovery.
Top Curr Chem
PUBLISHED: 06-14-2013
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In this chapter we first describe the applications of matrix-assisted laser desorption/ionization (MALDI) mass spectrometry (MS) in biomarker discovery. After a summary of the general analysis pipeline of MALDI MS data, each step of the pipeline will be elaborated in detail. In particular we try to provide a categorization of existing solutions with the hope that the reader can obtain a global picture on this topic. In addition we show how to apply such an analysis pipeline in protein and glycan profiling for biomarker discovery and for a deeper understanding of diseases. Finally we discuss the limitations of current analysis methods and the perspectives of future research.
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Multisample aCGH data analysis via total variation and spectral regularization.
IEEE/ACM Trans Comput Biol Bioinform
PUBLISHED: 05-25-2013
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DNA copy number variation (CNV) accounts for a large proportion of genetic variation. One commonly used approach to detecting CNVs is array-based comparative genomic hybridization (aCGH). Although many methods have been proposed to analyze aCGH data, it is not clear how to combine information from multiple samples to improve CNV detection. In this paper, we propose to use a matrix to approximate the multisample aCGH data and minimize the total variation of each sample as well as the nuclear norm of the whole matrix. In this way, we can make use of the smoothness property of each sample and the correlation among multiple samples simultaneously in a convex optimization framework. We also developed an efficient and scalable algorithm to handle large-scale data. Experiments demonstrate that the proposed method outperforms the state-of-the-art techniques under a wide range of scenarios and it is capable of processing large data sets with millions of probes.
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HapBoost: a fast approach to boosting haplotype association analyses in genome-wide association studies.
IEEE/ACM Trans Comput Biol Bioinform
PUBLISHED: 05-25-2013
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Genome-wide association study (GWAS) has been successful in identifying genetic variants that are associated with complex human diseases. In GWAS, multilocus association analyses through linkage disequilibrium (LD), named haplotype-based analyses, may have greater power than single-locus analyses for detecting disease susceptibility loci. However, the large number of SNPs genotyped in GWAS poses great computational challenges in the detection of haplotype associations. We present a fast method named HapBoost for finding haplotype associations, which can be applied to quickly screen the whole genome. The effectiveness of HapBoost is demonstrated by using both synthetic and real data sets. The experimental results show that the proposed approach can achieve comparably accurate results while it performs much faster than existing methods.
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Genetically engineered bone marrow mesenchymal stem cells improve functional outcome in a rat model of epilepsy.
Brain Res.
PUBLISHED: 05-18-2013
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Bone marrow mesenchymal stem cells (BMSCs) hold a great promising approach for the treatment of epilepsy owing to their distinctive characteristics and multi-potency. However, there is little research focusing on the multi-potency of BMSCs in the treatment of epilepsy, the present study was designed to examine the influence of genetically engineered BMSCs (GE-BMSCs) on the functional outcome in a rat model of epilepsy. First, Hes1 gene of BMSCs was genetically engineered by RNA interference (RNAi), and then the GABAergic differentiation of GE-BMSCs was tested in vitro. Second, the lithium chloride-pilocarpine induced epileptic rats were administrated with the GE-BMSCs, the behavioral observation and electroencephalography (EEG) monitoring was employed to analyze the functional outcome on the epileptic model at different time points (day 7, day 14, day 21 and day 28), followed by histological verification. In vitro test showed that Hes1 silencing could promote BMSCs to differentiate into GABAergic neuron-like cells. In vivo test showed that GE-BMSCs graft could further improve the functional recovery of the epileptic rats, and the GABAergic differentiation of grafted GE-BMSCs was correlated with the functional recovery. Taken together, these data suggest that GE-BMSCs can improve the functional outcome in a rat model of epilepsy.
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Ultrasound bio-microscopic image segmentation for evaluation of zebrafish cardiac function.
IEEE Trans Ultrason Ferroelectr Freq Control
PUBLISHED: 04-04-2013
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Zebrafish can fully regenerate their myocardium after ventricular resection without evidence of scars. This extraordinary regenerative ability provides an excellent model system to study the activation of the regenerative potential for human heart tissue. In addition to the morphology, it is vital to understand the cardiac function of zebrafish. To characterize adult zebrafish cardiac function, an ultrasound biomicroscope (UBM) was customized for real-time imaging of the zebrafish heart (about 1 mm in diameter) at a resolution of around 37 ?m. Moreover, we developed an image segmentation algorithm to track the cardiac boundary and measure the dynamic size of the zebrafish heart for further quantification of zebrafish cardiac function. The effectiveness and accuracy of the proposed segmentation algorithm were verified on a tissuemimicking phantom and in vivo zebrafish echocardiography. The quantitative evaluation demonstrated that the accuracy of the proposed algorithm is comparable to the manual delineation by experts.
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Clinical characteristics and prognostic analysis of recurrent hemangiopericytoma in the central nervous system: a review of 46 cases.
J. Neurooncol.
PUBLISHED: 03-20-2013
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Central nervous system hemangiopericytoma (HPC) is a malignant vascularized mesenchymal tumor with a high rate of recurrence. Because of its rarity, few clinical characteristics and prognostic analysis information regarding recurrent HPC exist for doctors to pursue optimal outcomes. Forty-six recurrent HPC cases treated at our hospital between 2004 and 2012 were compiled into a single database based on a retrospective review of patient records, which were used to summarize the clinical characteristics. The mean survival of the recurrent HPC patients in our cohort was 41.6 ± 4.4 months, with 1-, 2-, 3-, and 4-year survival rates of 80.4, 65.2, 59.2, and 53.8 %, respectively. Thirty patients (65.2 %) suffered their first tumor recurrence, with a mean survival of 36.9 ± 4.1 months. Sixteen patients (34.8 %) suffered a second or further tumor recurrence, with a mean survival of 39.7 ± 7.0 months. Eighteen patients (39.1 %) died of all causes during the follow-up period, with a mean survival of 14.2 ± 5.6 months. Univariate and multivariate regression analyses showed that factors associated with good prognosis included recurrence age over 35 years, an interval between the first and second recurrence of more than 1 year and a clear boundary of the recurrent tumor. Gross total resection with adjuvant external beam radiotherapy could independently delay tumor recurrence of the second or more times and prolong the postoperative survival; thus, this strategy should be pursued as the initial treatment.
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The complete compositional epistasis detection in genome-wide association studies.
BMC Genet.
PUBLISHED: 02-06-2013
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The detection of epistasis among genetic markers is of great interest in genome-wide association studies (GWAS). In recent years, much research has been devoted to find disease-associated epistasis in GWAS. However, due to the high computational cost involved, most methods focus on specific epistasis models, making the potential loss of power when the underlying epistasis models are not examined in these analyses.
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Using microPET imaging in quantitative verification of the acupuncture effect in ischemia stroke treatment.
Sci Rep
PUBLISHED: 01-02-2013
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Acupuncture has been indispensable in Chinese medicine. However, its function still remains elusive. This paper studies the effect of acupuncture in ischemia stroke treatment using the Sprague Dawley rat animal model. We induced focal cerebral ischemia in rats using the middle cerebral artery occlusion (MCAO) procedure. For each rat in the real acupuncture group (n = 63), the sham acupoint treatment group (n = 62), and the blank control group (n = 30), we acquired 3-D fluorodeoxyglucose-microPET images at baseline, after MCAO, and after treatment, respectively. Then, we measured the changes of the injury-volume in the right hemisphere of these rats. The measurements showed that real acupuncture slightly reduced the injury-volume, sham acupoint treatment increased the injury-volume, and blank control had no obvious effect in reducing the injury-volume. Statistical tests also confirmed that acupuncture was more effective than random stimulus in improving the metabolic recovery after stroke.
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Identifying disease-associated SNP clusters via contiguous outlier detection.
Bioinformatics
PUBLISHED: 07-22-2011
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Although genome-wide association studies (GWAS) have identified many disease-susceptibility single-nucleotide polymorphisms (SNPs), these findings can only explain a small portion of genetic contributions to complex diseases, which is known as the missing heritability. A possible explanation is that genetic variants with small effects have not been detected. The chance is < 8 that a causal SNP will be directly genotyped. The effects of its neighboring SNPs may be too weak to be detected due to the effect decay caused by imperfect linkage disequilibrium. Moreover, it is still challenging to detect a causal SNP with a small effect even if it has been directly genotyped.
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A hidden two-locus disease association pattern in genome-wide association studies.
BMC Bioinformatics
PUBLISHED: 05-14-2011
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Recent association analyses in genome-wide association studies (GWAS) mainly focus on single-locus association tests (marginal tests) and two-locus interaction detections. These analysis methods have provided strong evidence of associations between genetics variances and complex diseases. However, there exists a type of association pattern, which often occurs within local regions in the genome and is unlikely to be detected by either marginal tests or interaction tests. This association pattern involves a group of correlated single-nucleotide polymorphisms (SNPs). The correlation among SNPs can lead to weak marginal effects and the interaction does not play a role in this association pattern. This phenomenon is due to the existence of unfaithfulness: the marginal effects of correlated SNPs do not express their significant joint effects faithfully due to the correlation cancelation.
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GBOOST: a GPU-based tool for detecting gene-gene interactions in genome-wide case control studies.
Bioinformatics
PUBLISHED: 03-03-2011
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Collecting millions of genetic variations is feasible with the advanced genotyping technology. With a huge amount of genetic variations data in hand, developing efficient algorithms to carry out the gene-gene interaction analysis in a timely manner has become one of the key problems in genome-wide association studies (GWAS). Boolean operation-based screening and testing (BOOST), a recent work in GWAS, completes gene-gene interaction analysis in 2.5 days on a desktop computer. Compared with central processing units (CPUs), graphic processing units (GPUs) are highly parallel hardware and provide massive computing resources. We are, therefore, motivated to use GPUs to further speed up the analysis of gene-gene interactions.
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The choice of null distributions for detecting gene-gene interactions in genome-wide association studies.
BMC Bioinformatics
PUBLISHED: 02-15-2011
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In genome-wide association studies (GWAS), the number of single-nucleotide polymorphisms (SNPs) typically ranges between 500,000 and 1,000,000. Accordingly, detecting gene-gene interactions in GWAS is computationally challenging because it involves hundreds of billions of SNP pairs. Stage-wise strategies are often used to overcome the computational difficulty. In the first stage, fast screening methods (e.g. Tuning ReliefF) are applied to reduce the whole SNP set to a small subset. In the second stage, sophisticated modeling methods (e.g., multifactor-dimensionality reduction (MDR)) are applied to the subset of SNPs to identify interesting interaction models and the corresponding interaction patterns. In the third stage, the significance of the identified interaction patterns is evaluated by hypothesis testing.
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Motif-All: discovering all phosphorylation motifs.
BMC Bioinformatics
PUBLISHED: 02-15-2011
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Phosphorylation motifs represent common patterns around the phosphorylation site. The discovery of such kinds of motifs reveals the underlying regulation mechanism and facilitates the prediction of unknown phosphorylation event. To date, people have gathered large amounts of phosphorylation data, making it possible to perform substrate-driven motif discovery using data mining techniques.
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Score regularization for peptide identification.
BMC Bioinformatics
PUBLISHED: 02-15-2011
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Peptide identification from tandem mass spectrometry (MS/MS) data is one of the most important problems in computational proteomics. This technique relies heavily on the accurate assessment of the quality of peptide-spectrum matches (PSMs). However, current MS technology and PSM scoring algorithm are far from perfect, leading to the generation of incorrect peptide-spectrum pairs. Thus, it is critical to develop new post-processing techniques that can distinguish true identifications from false identifications effectively.
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White matter impairment in heroin addicts undergoing methadone maintenance treatment and prolonged abstinence: a preliminary DTI study.
Neurosci. Lett.
PUBLISHED: 01-23-2011
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Methadone maintenance treatment (MMT) might cause the impairments of neuropsychological and neurotransmitter function in opioid addicts. Whether long-term MMT could lead to the impairment of white matter (WM) in heroin addiction brain is unclear. This study compared the WM integrity in the bilateral frontal lobe, temporal lobe, splenium and genu of corpus collasum (CC) between MMT patients (n=13), former heroin addicts (n=11) in prolonged abstinence (PA), and healthy control subjects (n=15) using diffusion tensor imaging (DTI). Fractional anisotropy (FA), apparent diffusion coefficient (ADC) and eigenvalues (?(?), ?(||)) were measured. The correlation between DTI measures and accumulated former heroin dose, total methadone consumption, and PA duration were determined. Although the PA subjects showed no difference in DTI measures relative to the controls, the extensive correlations between the former heroin consumption and the DTI measures were noted. The MMT subjects showed a decreased FA values in the left genu, as well as the increased ADC and ?(?) values in the left splenium of CC in comparison to the controls. Compared with the PA, the MMT subjects had a significantly increased ADC value in the bilateral splenium of CC. Importantly, the methadone dosage used in the MMT group was correlated with the FA value in the left splenium of CC and in the right frontal lobe. Our preliminary results suggest that methadone plays a role in the impairment of WM integrity in heroin users on long-term MMT and the normalization of WM injury may occur during abstinence.
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A partial set covering model for protein mixture identification using mass spectrometry data.
IEEE/ACM Trans Comput Biol Bioinform
PUBLISHED: 01-15-2011
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Protein identification is a key and essential step in mass spectrometry (MS) based proteome research. To date, there are many protein identification strategies that employ either MS data or MS/MS data for database searching. While MS-based methods provide wider coverage than MS/MS-based methods, their identification accuracy is lower since MS data have less information than MS/MS data. Thus, it is desired to design more sophisticated algorithms that achieve higher identification accuracy using MS data. Peptide Mass Fingerprinting (PMF) has been widely used to identify single purified proteins from MS data for many years. In this paper, we extend this technology to protein mixture identification. First, we formulate the problem of protein mixture identification as a Partial Set Covering (PSC) problem. Then, we present several algorithms that can solve the PSC problem efficiently. Finally, we extend the partial set covering model to both MS/MS data and the combination of MS data and MS/MS data. The experimental results on simulated data and real data demonstrate the advantages of our method: 1) it outperforms previous MS-based approaches significantly; 2) it is useful in the MS/MS-based protein inference; and 3) it combines MS data and MS/MS data in a unified model such that the identification performance is further improved.
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Brain fMRI and craving response to heroin-related cues in patients on methadone maintenance treatment.
Am J Drug Alcohol Abuse
PUBLISHED: 01-11-2011
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To investigate the subjective craving and brain response to heroin-related cues in former heroin addicts on long-term methadone maintenance treatment.
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MiR-124 regulates early neurogenesis in the optic vesicle and forebrain, targeting NeuroD1.
Nucleic Acids Res.
PUBLISHED: 12-03-2010
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MicroRNAs (miRNAs) are involved in the fine control of cell proliferation and differentiation during the development of the nervous system. MiR-124, a neural specific miRNA, is expressed from the beginning of eye development in Xenopus, and has been shown to repress cell proliferation in the optic cup, however, its role at earlier developmental stages is unclear. Here, we show that this miRNA exerts a different role in cell proliferation at the optic vesicle stage, the stage which precedes optic cup formation. We show that miR-124 is both necessary and sufficient to promote cell proliferation and repress neurogenesis at the optic vesicle stage, playing an anti-neural role. Loss of miR-124 upregulates expression of neural markers NCAM, N-tubulin while gain of miR-124 downregulates these genes. Furthermore, miR-124 interacts with a conserved miR-124 binding site in the 3-UTR of NeuroD1 and negatively regulates expression of the proneural marker NeuroD1, a bHLH transcription factor for neuronal differentiation. The miR-124-induced effect on cell proliferation can be antagonized by NeuroD1. These results reveal a novel regulatory role of miR-124 in neural development and uncover a previously unknown interaction between NeuroD1 and miR-124.
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Detecting two-locus associations allowing for interactions in genome-wide association studies.
Bioinformatics
PUBLISHED: 08-24-2010
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Genome-wide association studies (GWASs) aim to identify genetic susceptibility to complex diseases by assaying and analyzing hundreds of thousands of single nucleotide polymorphisms (SNPs). Although traditional single-locus statistical tests have identified many genetic determinants of susceptibility, those findings cannot completely explain genetic contributions to complex diseases. Marchini and coauthors demonstrated the importance of testing two-locus associations allowing for interactions through a wide range of simulation studies. However, such a test is computationally demanding as we need to test hundreds of billions of SNP pairs in GWAS. Here, we provide a method to address this computational burden for dichotomous phenotypes.
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BOOST: A fast approach to detecting gene-gene interactions in genome-wide case-control studies.
Am. J. Hum. Genet.
PUBLISHED: 05-10-2010
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Gene-gene interactions have long been recognized to be fundamentally important for understanding genetic causes of complex disease traits. At present, identifying gene-gene interactions from genome-wide case-control studies is computationally and methodologically challenging. In this paper, we introduce a simple but powerful method, named "BOolean Operation-based Screening and Testing" (BOOST). For the discovery of unknown gene-gene interactions that underlie complex diseases, BOOST allows examination of all pairwise interactions in genome-wide case-control studies in a remarkably fast manner. We have carried out interaction analyses on seven data sets from the Wellcome Trust Case Control Consortium (WTCCC). Each analysis took less than 60 hr to completely evaluate all pairs of roughly 360,000 SNPs on a standard 3.0 GHz desktop with 4G memory running the Windows XP system. The interaction patterns identified from the type 1 diabetes data set display significant difference from those identified from the rheumatoid arthritis data set, although both data sets share a very similar hit region in the WTCCC report. BOOST has also identified some disease-associated interactions between genes in the major histocompatibility complex region in the type 1 diabetes data set. We believe that our method can serve as a computationally and statistically useful tool in the coming era of large-scale interaction mapping in genome-wide case-control studies.
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Stable feature selection for biomarker discovery.
Comput Biol Chem
PUBLISHED: 05-09-2010
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Feature selection techniques have been used as the workhorse in biomarker discovery applications for a long time. Surprisingly, the stability of feature selection with respect to sampling variations has long been under-considered. It is only until recently that this issue has received more and more attention. In this article, we review existing stable feature selection methods for biomarker discovery using a generic hierarchical framework. We have two objectives: (1) providing an overview on this new yet fast growing topic for a convenient reference; (2) categorizing existing methods under an expandable framework for future research and development.
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Altered small-world brain functional networks and duration of heroin use in male abstinent heroin-dependent individuals.
Neurosci. Lett.
PUBLISHED: 04-14-2010
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Although previous studies reported addiction-related alteration in resting-state brain connectivity, it is unclear whether these resting-state connectivity alterations were associated with chronic heroin use. In the current study, graph theory analysis (GTA) was applied to detect abnormal topological properties in heroin-dependent individuals. Several statistical parameters, such as degree (D), clustering coefficient (C) and shortest absolute path length (L), were included to test whether or not there was significant correlation between these parameters and the duration of heroin use. Our results demonstrated abnormal topological properties in several brain regions among our heroin-dependent subjects. Some of these regions are key areas of drug addiction-related circuits (control, reward, motivation/drive and memory), while others are involved in stress regulation. In addition, the duration of heroin use was positively correlated with the parameter D in the right parahippocampal gyrus, left putamen and bilateral cerebellum, but negatively correlated with the parameter L in the same regions. Our findings suggested that there is abnormal functional organization in heroin-dependent individuals and that the duration of heroin use is a critical factor leading to the altered brain connectivity.
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Optimization-based peptide mass fingerprinting for protein mixture identification.
J. Comput. Biol.
PUBLISHED: 04-10-2010
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In current proteome research, the most widely used method for protein mixture identification is probably peptide sequencing. Peptide sequencing is based on tandem mass spectrometry (MS/MS) data. The disadvantage is that MS/MS data only sequences a limited number of peptides and leaves many more peptides uncovered. Peptide mass fingerprinting (PMF) has been widely used to identify single purified proteins from single-stage MS data. Unfortunately, this technique is less accurate than the peptide sequencing method and cannot handle protein mixtures, which hampers the widespread use of PMF. In this article, we tackle the problem of protein mixture identification from an optimization point of view. We show that some simple heuristics can find good solutions to the optimization problem. As a result, we obtain much better identification results than previous methods. Through a comprehensive simulation study, we identify a set of limiting factors that hinder the performance of PMF-based protein mixture identification. We argue that it is feasible to remove these limitations and PMF can be a powerful tool in the analysis of protein mixtures, especially in the identification of low-abundance proteins, which are less likely to be sequenced by MS/MS scanning.
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A regularized method for peptide quantification.
J. Proteome Res.
PUBLISHED: 03-06-2010
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Peptide abundance estimation is generally the first step in protein quantification. In peptide abundance estimation, peptide overlapping and peak intensity variation are two challenges. The main objective of this paper is to estimate peptide abundance by taking advantage of peptide isotopic distribution and smoothness of peptide elution profile. Our method proposes to solve the peptide overlapping problem and provides a way to control the variance of estimation. We compare our method with a commonly used method on simulated data sets and two real data sets of standard protein mixtures. The results show that our method achieves more accurate estimation of peptide abundance on different samples. In our method, there is a variance-related parameter. Considering the well-known trade-off between the variance and the bias of estimation, we should not only focus on reducing the variance in real applications. A suggestion about parameter selection is given based on the discussion of variance and bias. Matlab source codes and detailed experimental results are available at http://bioinformatics.ust.hk/PeptideQuant/peptidequant.htm.
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A recombination hotspot in a schizophrenia-associated region of GABRB2.
PLoS ONE
PUBLISHED: 01-28-2010
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Schizophrenia is a major disorder with complex genetic mechanisms. Earlier, population genetic studies revealed the occurrence of strong positive selection in the GABRB2 gene encoding the beta(2) subunit of GABA(A) receptors, within a segment of 3,551 bp harboring twenty-nine single nucleotide polymorphisms (SNPs) and containing schizophrenia-associated SNPs and haplotypes.
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Identifying main effects and epistatic interactions from large-scale SNP data via adaptive group Lasso.
BMC Bioinformatics
PUBLISHED: 01-18-2010
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Single nucleotide polymorphism (SNP) based association studies aim at identifying SNPs associated with phenotypes, for example, complex diseases. The associated SNPs may influence the disease risk individually (main effects) or behave jointly (epistatic interactions). For the analysis of high throughput data, the main difficulty is that the number of SNPs far exceeds the number of samples. This difficulty is amplified when identifying interactions.
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Combining spatial and temporal information to explore resting-state networks changes in abstinent heroin-dependent individuals.
Neurosci. Lett.
PUBLISHED: 01-15-2010
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Majority of previous heroin fMRI studies focused on abnormal brain function in heroin-dependent individuals. However, few fMRI studies focused on the resting-state abnormalities in heroin-dependent individuals and assessed the relationship between the resting-state functional connectivity changes and duration of heroin use. In the present study, discrete cosine transform (DCT) was employed to explore spatial distribution of low frequency BOLD oscillations in heroin-dependent individuals and healthy subjects during resting-state; meanwhile resting-state functional connectivity analysis was used to investigate the temporal signatures of overlapping brain regions obtained in DCT analysis among these two groups. Main finding of the present study is that the default mode network (DMN) and rostral anterior cingulate cortex (rACC) network of heroin-dependent individuals were changed compared with healthy subjects. More importantly, these changes negatively correlated with duration of heroin use. These resting-state functional abnormalities in heroin-dependent individuals provided evidence for abnormal functional organization in heroin-dependent individuals, such as functional impairments in decision-making and inhibitory control.
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Predictive rule inference for epistatic interaction detection in genome-wide association studies.
Bioinformatics
PUBLISHED: 10-30-2009
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Under the current era of genome-wide association study (GWAS), finding epistatic interactions in the large volume of SNP data is a challenging and unsolved issue. Few of previous studies could handle genome-wide data due to the difficulties in searching the combinatorially explosive search space and statistically evaluating high-order epistatic interactions given the limited number of samples. In this work, we propose a novel learning approach (SNPRuler) based on the predictive rule inference to find disease-associated epistatic interactions.
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Improving peptide identification with single-stage mass spectrum peaks.
Bioinformatics
PUBLISHED: 08-18-2009
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Database searching is the major peptide identification method in shotgun proteomics. It searches tandem mass spectrometry (MS/MS) spectra against a protein database to identify target peptides. The success of such a database searching method relies on a scoring algorithm that can evaluate the quality of peptide-spectrum matches (PSMs) accurately. However, current scoring algorithms frequently generate inaccurate assignments due to variations and noises in the MS/MS spectra. To address this issue, we like to improve peptide identification by using additional information from other data sources.
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The role of miR-124a in early development of the Xenopus eye.
Mech. Dev.
PUBLISHED: 07-27-2009
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It has been reported that miR-124a is abundant in the central nervous system including the eye, and is related to neurogenesis in several species. However, the role of miR-124a in the eye remains unclear. In this study, we show that the expression of miR-124a in Xenopus laevis begins along the neural fold, including the protruding eye anlagen, at a low level at around stage 18; its expression level gradually increases in the neural tube and the eye as embryos develop into later stages and then maintains at a high level in eye to adult stages. Microinjection of a miR-124a precursor at the 8-cell stage leads to malformation of the optic nerve and optic cup, indicating the importance of maintaining low levels of miR-124a during early embryonic development. In addition, miR-124a overexpression markedly down regulates the expression of its predicted targets Lhx2, Hairy2, Gli3, NeuroD1 and Otx2 in/around the eye anlagen, and the interaction of miR-124a with the 3 UTR of Lhx2 represses gene expression as shown by luciferase assays. Moreover, excess miR-124a inhibits cell proliferation in the eye of Xenopus embryos during retinogenesis. These results indicate that miR-124a acts as a post-transcriptional regulator in the genetic network controlling eye morphogenesis and neurogenesis. The mechanism of miR-124as early interaction with the genetic network may also persist in its later role in the maturing and adult eye and brain.
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SNPHarvester: a filtering-based approach for detecting epistatic interactions in genome-wide association studies.
Bioinformatics
PUBLISHED: 03-18-2009
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Hundreds of thousands of single nucleotide polymorphisms (SNPs) are available for genome-wide association (GWA) studies nowadays. The epistatic interactions of SNPs are believed to be very important in determining individual susceptibility to complex diseases. However, existing methods for SNP interaction discovery either suffer from high computation complexity or perform poorly when marginal effects of disease loci are weak or absent. Hence, it is desirable to develop an effective method to search epistatic interactions in genome-wide scale.
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Semi-supervised protein subcellular localization.
BMC Bioinformatics
PUBLISHED: 01-30-2009
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Protein subcellular localization is concerned with predicting the location of a protein within a cell using computational method. The location information can indicate key functionalities of proteins. Accurate predictions of subcellular localizations of protein can aid the prediction of protein function and genome annotation, as well as the identification of drug targets. Computational methods based on machine learning, such as support vector machine approaches, have already been widely used in the prediction of protein subcellular localization. However, a major drawback of these machine learning-based approaches is that a large amount of data should be labeled in order to let the prediction system learn a classifier of good generalization ability. However, in real world cases, it is laborious, expensive and time-consuming to experimentally determine the subcellular localization of a protein and prepare instances of labeled data.
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Misexpression of miR-196a induces eye anomaly in Xenopus laevis.
Brain Res. Bull.
PUBLISHED: 01-13-2009
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miR-196a is located in the posterior trunk and plays a role in limb development. Here we show that miR-196a is able to induce eye anomaly in Xenopus laevis. Microinjection of synthetic miRNA precursor molecule for mammalian miR-196a into Xenopus embryo is sufficient for miR-196a overexpression during early development. The misexpression of miR-196a in anterior embryo led to dose-dependent eye anomalies, especially size reduction. In addition, the expression of ET, Rx1, Six3, Pax6, Lhx2, Optx2 and Ath5 in eye field or optic cup was also down-regulated. These results indicate that miR-196a can target gene(s) in the genetic network involved in eye formation, providing a potential tool for studying the mechanisms of eye development and diseases.
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MegaSNPHunter: a learning approach to detect disease predisposition SNPs and high level interactions in genome wide association study.
BMC Bioinformatics
PUBLISHED: 01-09-2009
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The interactions of multiple single nucleotide polymorphisms (SNPs) are highly hypothesized to affect an individuals susceptibility to complex diseases. Although many works have been done to identify and quantify the importance of multi-SNP interactions, few of them could handle the genome wide data due to the combinatorial explosive search space and the difficulty to statistically evaluate the high-order interactions given limited samples.
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Comparison of public peak detection algorithms for MALDI mass spectrometry data analysis.
BMC Bioinformatics
PUBLISHED: 01-06-2009
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In mass spectrometry (MS) based proteomic data analysis, peak detection is an essential step for subsequent analysis. Recently, there has been significant progress in the development of various peak detection algorithms. However, neither a comprehensive survey nor an experimental comparison of these algorithms is yet available. The main objective of this paper is to provide such a survey and to compare the performance of single spectrum based peak detection methods.
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On feature motion decorrelation in ultrasound speckle tracking.
IEEE Trans Med Imaging
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Speckle tracking methods refer to motion tracking methods based on speckle patterns in ultrasound images. They are commonly used in ultrasound based elasticity imaging techniques to reveal mechanical properties of tissues for clinical diagnosis. In speckle tracking, feature motion decorrelation exists when speckle patterns are not identical before and after tissue motion and deformation. Feature motion decorrelation violates the underlying assumption of most speckle tracking methods. Consequently, the estimation accuracy of current methods is greatly limited. In this paper, two types of speckle pattern variations, the geometric transformation and the intensity change of speckle patterns, are studied. We show that a coupled filtering method is able to compensate for both types of variations. It provides accurate strain estimations even when tissue deformation or rotation is extremely large. We also show that in most cases, an affine warping method that only compensates for the geometric transformation is able to achieve a similar performance as the coupled filtering method. Feature motion decorrelation in B-mode images is also studied. Finally, we show that in typical elastography studies, speckle tracking methods without modeling local shearing or rotation will fail when tissue deformation is large.
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Craving correlates with mesolimbic responses to heroin-related cues in short-term abstinence from heroin: an event-related fMRI study.
Brain Res.
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Craving is an important factor in relapse to drug abuse, and cue-induced craving is an especially powerful form of this construct. Neuroimaging methods have been utilized to study drug cue-induced craving and neural correlates in the human brain. However, very few studies have focused on characterizing craving and the neural responses to heroin-related cues in short-term abstinent heroin-dependent patients. Twenty-four heroin-dependent subjects and 20 demographically matched drug-naïve subjects participated in this study. An event-related cue-reactivity paradigm was employed, while changes in blood oxygen level-dependent (BOLD) signals were acquired by functional magnetic resonance imaging (fMRI). The heroin-dependent group reported significantly increased craving following exposure to heroin-related cues. Direct comparison between the two groups showed that brain activation to heroin-related minus neutral cues was significantly greater for the heroin-dependent group in the bilateral nucleus accumbens (NAc), caudate, putamen, amygdala, hippocampus/parahippocampus, midcingulate cortex, dorsolateral prefrontal cortex (DLPFC), orbitofrontal cortex (OFC), medial frontal gyrus (MeFG), midbrain, thalamus, left anterior cingulate cortex (ACC), posterior cingulate cortex (PCC), and subcallosal gyrus. Changes in craving in the heroin-dependent group correlated positively with brain activation in the bilateral NAc, caudate, right putamen, and left ACC. The abstinence duration correlated positively with brain activation in the left caudate and right parahippocampal gyrus. In conclusion, the cue-reactivity paradigm significantly activated neural responses in the mesolimbic dopamine (DA) system and prefrontal cortex (PFC) and induced increased craving in short-term abstinent heroin-dependent patients. We suggest that these response patterns characterize the high vulnerability of relapse in short-term abstinent heroin-dependent subjects.
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Moving Object Detection by Detecting Contiguous Outliers in the Low-Rank Representation.
IEEE Trans Pattern Anal Mach Intell
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Object detection is a fundamental step for automated video analysis in many vision applications. Object detection in a video is usually performed by object detectors or background subtraction techniques. Often, an object detector requires manually labeled examples to train a binary classifier, while background subtraction needs a training sequence that contains no objects to build a background model. To automate the analysis, object detection without a separate training phase becomes a critical task. People have tried to tackle this task by using motion information. But existing motion-based methods are usually limited when coping with complex scenarios such as nonrigid motion and dynamic background. In this paper, we show that above challenges can be addressed in a unified framework named DEtecting Contiguous Outliers in the LOw-rank Representation (DECOLOR). This formulation integrates object detection and background learning into a single process of optimization, which can be solved by an alternating algorithm efficiently. We explain the relations between DECOLOR and other sparsity-based methods. Experiments on both simulated data and real sequences demonstrate that DECOLOR outperforms the state-of-the-art approaches and it can work effectively on a wide range of complex scenarios.
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Protein inference: a review.
Brief. Bioinformatics
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Assembling peptides identified from tandem mass spectra into a list of proteins, referred to as protein inference, is a critical step in proteomics research. Due to the existence of degenerate peptides and one-hit wonders, it is very difficult to determine which proteins are present in the sample. In this paper, we review existing protein inference methods and classify them according to the source of peptide identifications and the principle of algorithms. It is hoped that the readers will gain a good understanding of the current development in this field after reading this review and come up with new protein inference algorithms.
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Peptide reranking with protein-peptide correspondence and precursor peak intensity information.
IEEE/ACM Trans Comput Biol Bioinform
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Searching tandem mass spectra against a protein database has been a mainstream method for peptide identification. Improving peptide identification results by ranking true Peptide-Spectrum Matches (PSMs) over their false counterparts leads to the development of various reranking algorithms. In peptide reranking, discriminative information is essential to distinguish true PSMs from false PSMs. Generally, most peptide reranking methods obtain discriminative information directly from database search scores or by training machine learning models. Information in the protein database and MS1 spectra (i.e., single stage MS spectra) is ignored. In this paper, we propose to use information in the protein database and MS1 spectra to rerank peptide identification results. To quantitatively analyze their effects to peptide reranking results, three peptide reranking methods are proposed: PPMRanker, PPIRanker, and MIRanker. PPMRanker only uses Protein-Peptide Map (PPM) information from the protein database, PPIRanker only uses Precursor Peak Intensity (PPI) information, and MIRanker employs both PPM information and PPI information. According to our experiments on a standard protein mixture data set, a human data set and a mouse data set, PPMRanker and MIRanker achieve better peptide reranking results than PetideProphet, PeptideProphet+NSP (number of sibling peptides) and a score regularization method SRPI. The source codes of PPMRanker, PPIRanker, and MIRanker, and all supplementary documents are available at our website: http://bioinformatics.ust.hk/pepreranking/. Alternatively, these documents can also be downloaded from: http://sourceforge.net/projects/pepreranking/.
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What is Visualize?

JoVE Visualize is a tool created to match the last 5 years of PubMed publications to methods in JoVE's video library.

How does it work?

We use abstracts found on PubMed and match them to JoVE videos to create a list of 10 to 30 related methods videos.

Video X seems to be unrelated to Abstract Y...

In developing our video relationships, we compare around 5 million PubMed articles to our library of over 4,500 methods videos. In some cases the language used in the PubMed abstracts makes matching that content to a JoVE video difficult. In other cases, there happens not to be any content in our video library that is relevant to the topic of a given abstract. In these cases, our algorithms are trying their best to display videos with relevant content, which can sometimes result in matched videos with only a slight relation.