Pathway Prediction
Pathway Prediction - Related Videos
02:27
Predicting Molecular Geometry
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2020
VSEPR Theory for Determination of Electron Pair Geometries The following procedure uses VSEPR theory to determine the electron pair geometries and the molecular structures: Write the Lewis structure of the molecule or polyatomic ion. Count the number of electron groups (lone pairs and bonds) around the central atom. A single, double, or triple bond counts as one region of electron density. Identify the electron-pair geometry based on the number of electron groups: linear, trigonal planar,...
03:08
Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
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2025
The current work describes a protocol for running the Pathway2Targets algorithm, an R script that predicts and prioritizes therapeutic targets based on the profile of intracellular signaling pathways generated by comparing case versus control samples from a bulk RNA-sequencing experiment.
14:56
An Experimental Paradigm for the Prediction of Post-Operative Pain (PPOP)
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Cited by 109 •
2010
Diffuse noxious inhibitory control, temporal summation and wound hyperalgesia testing are demonstrated in the obstetric patient. These tests evaluate inhibitory and excitatory mechanisms of pain processing and are here utilized to evaluate endogenous analgesia at different time-points during pregnancy and the peripartum period to help reveal individual s risk for persistent pain.
16:41
A Protocol for Computer-Based Protein Structure and Function Prediction
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Cited by 78 •
2011
Guidelines for computer based structural and functional characterization of protein using the I-TASSER pipeline is described. Starting from query protein sequence, 3D models are generated using multiple threading alignments and iterative structural assembly simulations. Functional inferences are thereafter drawn based on matches to proteins with known structure and functions.
01:24
Other Glycolytic Pathways
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2025
The pentose phosphate pathway (PPP) operates in parallel with glycolysis, facilitating the metabolism of both pentoses and glucose. This pathway consists of two distinct phases: the oxidative and non-oxidative phases. While it does not directly generate ATP, the intermediates formed during the process can integrate into glycolysis, contributing to cellular energy metabolism when required.Oxidative Phase: NADPH ProductionThe oxidative phase of the pentose phosphate pathway is primarily...
FAQs
Pathway prediction combines evidence from several molecular measurement layers with information stored in curated pathway databases. Genomic data can indicate possible molecular components, while transcriptomic and proteomic measurements show changes in activity or abundance. Interaction data adds relationships among molecules. Integrating these sources helps distinguish isolated molecular changes from coordinated processes associated with a biological phenotype.
Statistical models evaluate whether observed molecular patterns support particular pathways, whereas network-based algorithms examine how molecules connect through interaction structures. Both approaches help rank candidate explanations rather than simply listing altered genes or proteins. Their results can reveal likely molecular connections and organize experimental observations into testable systems-level hypotheses.
When known pathway components appear connected but an intermediate step is absent from the available data, computational analysis can infer a plausible missing connection from interaction information, database knowledge, or coordinated molecular patterns. These inferred steps remain hypotheses rather than confirmed mechanisms. Ranking them by supporting evidence helps researchers prioritize which relationships require experimental testing.
A typical workflow begins with high-throughput genomic, transcriptomic, proteomic, or interaction measurements. Researchers then compare the observed information with curated pathway databases and apply statistical or network-based algorithms. The resulting pathways and molecular connections are ranked according to their inferred relevance to the phenotype. Candidate predictions can subsequently guide experimental validation.
High-throughput experiments often produce large collections of molecular measurements that are difficult to interpret individually. Pathway prediction groups these observations into connected biological processes, making it easier to relate molecular changes to cellular functions or responses to signals. This process supports hypothesis generation by identifying pathway-level patterns that may not be apparent from single measurements.
In disease research, pathway prediction can connect molecular measurements with candidate disease mechanisms and highlight biological processes associated with an observed phenotype. In drug development, those predicted processes may suggest molecular targets for further study. The approach also contributes to precision medicine and functional genomics by linking individual or experimental molecular profiles to pathway-level interpretations.