DataSheet1_Adjustment of p-value expression to ontology using machine learning for genetic prediction, prioritization, interaction, and its validation in glomerular disease.PDF

The results of gene expression analysis based on p-value can be extracted and sorted by their absolute statistical significance and then applied to multiple similarity scores of their gene ontology (GO) terms to promote the combination and adjustment of these scores as essential predictive tasks for understanding biological/clinical pathways. The latter allows the possibility to assess whether certain aspects of gene function may be associated with other varieties of genes, to evaluate regulation, and to link them into networks that prioritize candidate genes for classification by applying machine learning techniques. We then detect significant genetic interactions based on our algorithm to validate the results. Finally, based on specifically selected tissues according to their normalized gene expression and frequencies of occurrence from their different biological and clinical inputs, a reported classification of genes under the subject category has validated the abstract (glomerular diseases) as a case study.</p

No. of genes implicated in different levels of Methylation.

<p>No. of genes implicated in different levels of Methylation.</p

Search filters to retrieve the data.

<p>Various search option that includes search by GENE-ID, GENE SYMBOL, PROTEIN-ID, MECHANISM, DISEASE, TRANSCRIPTION FACTOR, GENE ONTOLOGIES, MIRNA, METHYLATION, DRUG DETAILS, COPY NUMBER VARIATION, SOMATIC MUTATIONS.</p

Identified major transcription factors in early colorectal cancer progression.

<p>¹The JASPAR IDs correspond to the transcription factors from JASPAR database</p><p>²The Pubmed IDs/ Experimental Databases column contains the information for literature references and databases created on experimentally validated data for their association with colorectal cancer.</p><p>Identified major transcription factors in early colorectal cancer progression.</p

Pre-processing and normalization of DNA microarray data.

<p>2a shows the distribution of microarray files before normalization and 2b explains the uniform distribution obtained after implementing normalization i.e. removal of noise from data.</p

CRC Supplementary Data

This file contains network motifs being used in this study. Two kinds of network motifs were included, one is randomly generated and other annotated ones

Functional enrichment and annotation analyses.

<p>The 631 differentially expressed genes were subjected to manual curation and annotation analyses for their involvement in diverse biological pathways, molecular functions and cellular components.</p

The methodology applied for recognizing biomarkers in colorectal cancer.

<p>Study initiated with the characterization of differentially expressed genes in colorectal cancer dataset and their transcriptional regulation. Important interactions and network patterns were identified from the CRC pathway and eventually functional enrichment was executed for key players in the disease progression.</p

Identified network motifs from colorectal cancer pathway.

<p>Some 4 and 5 node sub-graphs have been symbolized with gene names and their interactions if any. If the given interaction in the pathway was found to be missing, it is depicted as unknown (black coloured arrow).</p

Identification of differential expression.

<p>Significance analysis of microarrays (SAM) and volcano plot were generated for detecting the differentially expressed genes in the early colorectal cancer dataset. In SAM, 631 significant genes were identified for their over or under expression in the diseased state whereas the volcano plot evidently elucidates the differentially expressed genes with red spots having signal log ratio (SLR)>2 or SLR<2.</p