AI-Powered Predictive Models for Genome Sequencing: a Bioinformatics Approach

EasyChair Preprint 14905

Abstract

Advancements in genome sequencing technologies have significantly increased the volume and complexity of genomic data. To address the challenges of interpreting vast amounts of sequencing information, AI-powered predictive models have emerged as a transformative solution. This approach leverages machine learning algorithms and bioinformatics techniques to enhance the accuracy and efficiency of genome analysis. By integrating data from various sources, such as high-throughput sequencing and functional genomics, AI models can predict genetic variants, identify biomarkers, and uncover novel insights into genomic functions and disease mechanisms. This paper explores the development and application of AI-powered predictive models in genome sequencing, highlighting their potential to revolutionize genomics research and personalized medicine. We discuss methodologies, case studies, and future directions for implementing these models in bioinformatics.

Keyphrases: Bioinformatics, genome sequencing, machine learning, personalized medicine

@booklet{EasyChair:14905,
  author    = {Adaan Ahsun},
  title     = {AI-Powered Predictive Models for Genome Sequencing: a Bioinformatics Approach},
  howpublished = {EasyChair Preprint 14905},
  year      = {EasyChair, 2024}}