An Human Islet Cell RNA-Seq for Genome-Wide Genotype Deepsec Framework Using Deep Learning Based Diabetes Prediction
- Title
- An Human Islet Cell RNA-Seq for Genome-Wide Genotype Deepsec Framework Using Deep Learning Based Diabetes Prediction
- Creator
- Padmaja K.; Mukhopadhyay D.
- Description
- Evaluating the tissues responsible for complicated human illnesses is important to rank significance of genetic revision connected to features. In order to make predictions about the regulatory functions of geneticsvariations athwart wide range of epigenetic changes, this article introduces a Convolutional neural network (CNN) model upgraded filters and Deepsec framework incorporated with comprehensive ENCODE and Roadmap consortia have compiled a human epigenetic map that indicates specificity to certain tissues or cell types. Deepsec framework integrates transcription factors, histone modification markers, and RNA accessibility maps to comprehensively evaluate the consequences of non-coding alterations on the most important components, even for uncommon variations or novel mutations. By using trait-associated loci and more than 30 different human pancreatic islets and their subsets of cells sorted using fluorescence-activated cell sorting, annotations of epigenetic profiling were obtained (FACS) on a genome-wide scale. The proposed model, used '1492' publicly available GWAS datasets. My team presented that deepsec framework does epigenetic annotations found important GWAS associations and uncover regulatory loci from background signals when exposed to CNN-based analysis, offering fresh intuition underlying nadir causes of type 2diabetes. The suggested approaches are anticipated to be extensively used in downstream GWAS analysis, making it possible to assess non-coding variations and conduct downstream GWAS analysis 2023 IEEE.
- Source
- 2023 International Conference on Network, Multimedia and Information Technology, NMITCON 2023
- Date
- 2023-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- CNN; Deep Learning; Diabetes mellitus (DM); ENCODE; epigenomic; FACS; GWAS
- Coverage
- Padmaja K., Christ (Deemed to Be University), Department of Cse, Bangalore, India; Mukhopadhyay D., Christ (Deemed to Be University), Department of Cse, Bangalore, India
- Rights
- Restricted Access
- Relation
- ISBN: 979-835030082-6
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Padmaja K.; Mukhopadhyay D., “An Human Islet Cell RNA-Seq for Genome-Wide Genotype Deepsec Framework Using Deep Learning Based Diabetes Prediction,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 26, 2025, https://archives.christuniversity.in/items/show/19783.