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Full Description
This volume describes the latest developments in using Artificial Intelligence (AI) and machine learning in the field of genomics. The chapters in this book cover a variety of topics such as ways to derive the biomarker and measure biological and pathological processes from transcriptomic data sets; techniques to infer the function of epigenetics using network-based methodology; a look at how CellOracle can figure out which gene-gene interaction is important; how to find a machine learning approach to figure out how microRNA affects the cardiovascular events; protocols on ways to find how AI/ML approaches can attack somatic variant detection in normal human tissue; and a description on how gene embeddings are powerful tools for predicting unknown functions of genes and drugs. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls.
Thorough and comprehensive, AI/ML-Driven Gene Analysis: Methods and Protocols is a valuable tool for researchers interested in learning more about how cutting-edge methodology for AI/ML is applied to genomics.
Contents
Identification of Clinically Relevant Genes Using Machine Learning Techniques.- Drug Discovery from Gene Expression/Gene Regulation.- AI-Based 3D Heterogenous Network Model for Functional Prediction of Epigenetics.- Functional Prediction of Epitranscriptome.- Inference of Gene-Gene Interaction and Its Function.- Analysis of Chromatin Structure Using Network Approaches: A Step-By-Step Guide.- Tensor Decomposition-Based Unsupervised Feature Extraction Applied to Bioinformatics.- Circulating MicroRNAs as Biomarkers for Cardiovascular Events: Bioinformatics and Machine Learning Approaches.- Large Language Models for Non-Coding RNA Biomarker Discovery in Breast Cancer.- Comprehensive Guide to Gene Expression Analysis from Bulk and Single-Nucleus Transcriptomes.- RNA Structure and Its Function.- AI/ML-Driven Gene Analysis: New Perspectives on Variant Calling in a Human Normal Tissue Using scRNA-seq Data.- Copy Number Variation Analysis.- Graph-Based Gene Embedding.
