Explainable models using transcription factor binding and epigenome patterns at promoters reveal disease-associated genes and their regulators in the context of cell-types
Chandra, O.; Pramanik, D.; Gautam, S.; Sharma, M.; Dubey, N.; Mahato, B.; Orlov, Y. L.; Kumar, V.
Show abstract
Understanding genome-wide epigenetic regulation of diseases is important in establishing pathogenic factors and could aid in disease diagnosis, prognosis, and therapeutics. In this study, we have utilized transcription factors (TFs) and co-factor profiles (n=823) as features in machine learning models to link them to various diseases. Further, along with TFs and co-factor profiles, histone modifications ChIP-seq (n = 621), cap analysis gene expression (CAGE) tags (n = 255), and DNase hypersensitivity profiles (n = 255) as features allowed for the modeling of association of coding and non-coding genes to diseases. Such predicted associations could be independently validated using genome-wide association data and survival analysis. However, the unique aspect of our approach is that it highlights the link between TF binding patterns and diseases in the context of cell types. Besides highlighting relevant TF-binding in known cell-types associated with diseases, it also provided their surprising link with TFs expressed in immune cells and other seemingly non-related cells. Further investigation revealed such links to be genuine and potentially useful for prognosis, further revealing the need to deconvolve a set of known genes associated with diseases.
Matching journals
The top 9 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Integrating Bioinformatics and Artificial Intelligence Methods to identify disruptive STAT1 variants impacting Protein Stability and Function 96%
- Integrated Analysis of Tissue-specific Gene Expression in Diabetes by Tensor Decomposition Can Identify Possible Associated Diseases. 95%
- Structural variability, expression profile and pharmacogenetics properties of TMPRSS2 gene as a potential target for COVID-19 therapy 94%
Similar papers in this journal
- Analysis of Pan-Omics Data in Human Interactome Network (APODHIN) 95%
- Tensor decomposition-Based Unsupervised Feature Extraction Applied to Single-Cell Gene Expression Analysis 95%
- Identification of Platform-Independent Diagnostic Biomarker Panel for Hepatocellular Carcinoma using Large-scale Transcriptomics Data 94%
Similar papers in this journal
- Finding disease modules for cancer and COVID-19 in gene co-expression networks with the Core&Peel method 96%
- Discovering Key Transcriptomic Regulators in Pancreatic Ductal Adenocarcinoma using Dirichlet Process Gaussian Mixture Model 95%
- Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning 95%
Similar papers in this journal
- Unveiling epigenetic regulatory elements associated with breast cancer development 97%
- From miRNA target gene network to miRNA function: miR-375 might regulate apoptosis and actin dynamics in the heart muscle via Rho-GTPases-dependent pathways 95%
- Regulation Network of Colorectal Cancer Specific Enhancers in Progression of Colorectal Cancer 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.