Data Patterns
Data Patterns - Related Videos
06:38
Pattern-based Search of Epigenomic Data Using GeNemo
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2017
Unlike DNA sequence data, epigenomic data are not readily subjected to text-based searches. Presented here are the procedures to use an upgraded version of GeNemo, a web-based bioinformatics tool, to conduct pattern-based searches for similarities in epigenomic data comparing available online databases including Encyclopedia of DNA Elements with user's data.
14:27
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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Cited by 73 •
2013
Multivariate techniques including principal component analysis (PCA) have been used to identify signature patterns of regional change in functional brain images. We have developed an algorithm to identify reproducible network biomarkers for the diagnosis of neurodegenerative disorders, assessment of disease progression, and objective evaluation of treatment effects in patient populations.
01:30
Data Interpretation
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2026
Research papers often present findings in the form of numerical tables, statistical outputs, and graphical representations. However, numerical results alone do not convey meaning unless researchers examine and place them in context. Data interpretation is the systematic process through which researchers transform raw information into clear, meaningful insights. It begins only after researchers have collected data through experiments, surveys, or observational studies. In their unprocessed form,...
13:51
Cross-Modal Multivariate Pattern Analysis
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Cited by 6 •
2011
Classical multivariate pattern analysis predicts sensory stimuli a subject perceives from neural activity in the corresponding cortices (e.g. visual stimuli from activity in visual cortex). Here, we apply pattern analysis cross-modally and show that sound- and touch-implying visual stimuli can be predicted from activity in auditory and somatosensory cortices, respectively.
08:18
Visualizing Motion Patterns in Acupuncture Manipulation
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Cited by 6 •
2016
Here, we present a protocol for using the Acupuncture Manipulation Education System (AMES) in the training of acupuncture manipulation skills using phantom acupoints.
FAQs
Different visual features support different interpretations. An increasing or decreasing run suggests a trend, while repeated values or recurring arrangements indicate repetition. Correlation highlights a relationship between quantities, clusters show observations concentrated in a region, and outliers identify results that differ noticeably from the rest. Separating these features helps mathematicians compare groups, evaluate relationships, or describe a model.
Choosing a table, graph, or sequence changes what can be noticed. Tables support direct comparison of values, graphs make increases, decreases, clusters, and outliers easier to see, and sequences can expose repetition or a rule across positions. This organization does not change the observations; it makes their mathematical relationships easier to inspect and communicate.
A recurring relationship can be examined across organized observations to determine how quantities relate. If the same kind of change or arrangement appears repeatedly, mathematicians can express that relationship as a formula or model rather than treating each value separately. The resulting representation supports comparison, summarizes the evidence, and can be used to make reasoned predictions from the identified structure.
Begin by arranging observations in a table, graph, or sequence. Next, inspect whether values increase, decrease, repeat, cluster, or include possible outliers. Then compare the relevant quantities and describe the relationship visible in the arrangement. Finally, use the supported pattern to classify information, communicate a conclusion, or develop a formula or model.
Within statistics and probability, these patterns help summarize evidence and examine relationships. In scientific research, they support interpretation of observed information, while everyday decision-making uses them to compare results and judge what the available data suggest. The same reasoning can connect formal mathematical analysis with practical choices, provided conclusions remain tied to the observed arrangement.
An outlier should be recognized as a result that differs from the rest, not automatically treated as the main trend. Its presence can affect how a relationship is evaluated and how conclusions are communicated. Marking such observations separately helps preserve the distinction between the general pattern and unusual results when summarizing evidence or comparing groups.