Bioinformatics Tools for Precision Medicine: Integrating Genomic Data with Machine Learning

EasyChair Preprint 12339

Abstract

Precision medicine promises to revolutionize healthcare by tailoring treatments to individual patients based on their unique genetic makeup. Genomic data is pivotal in this paradigm shift, offering insights into the molecular mechanisms underlying diseases and guiding personalized therapeutic strategies. However, the complexity and vastness of genomic information present significant challenges for effective analysis and interpretation. Bioinformatics tools leveraging machine learning techniques have emerged as indispensable resources for extracting meaningful insights from genomic data in the context of precision medicine. This review explores integrating genomic data with machine learning algorithms, highlighting their applications in disease diagnosis, prognosis, and treatment selection. We discuss various types of genomic data, including DNA sequencing, gene expression profiles, and epigenetic modifications, and elucidate how machine learning models can effectively analyze and interpret these data to inform clinical decision-making. We discuss popular machine learning algorithms such as support vector machines, random forests, and deep learning architectures, elucidating their strengths and weaknesses in handling different types of genomic data. Additionally, we explore data preprocessing techniques, feature selection methods, and model evaluation strategies crucial for ensuring the robustness and reliability of machine learning-based analyses.

Keyphrases: Bioinformatics, genomic data, precision medicine

@booklet{EasyChair:12339,
  author    = {Jane Elsa and Ezra John},
  title     = {Bioinformatics Tools for Precision Medicine: Integrating Genomic Data with Machine Learning},
  howpublished = {EasyChair Preprint 12339},
  year      = {EasyChair, 2024}}