Exploring the Depths: Machine Learning Applications in Bioinformatics

EasyChair Preprint 12447

Abstract

The field of bioinformatics has undergone a revolution with the advent of machine learning techniques, offering unprecedented opportunities for understanding complex biological systems. This paper explores the diverse applications of machine learning in bioinformatics, focusing on the integration of computational and biological sciences to unravel the intricacies of biological data. Machine learning algorithms have been instrumental in deciphering genomic sequences, predicting protein structures and functions, analyzing gene expression patterns, and understanding molecular interactions. By leveraging large-scale datasets, advanced algorithms, and powerful computational resources, researchers can now extract meaningful insights from biological data with remarkable accuracy and efficiency. This paper discusses various machine learning methodologies employed in bioinformatics, including supervised learning for classification and regression tasks, unsupervised learning for clustering and dimensionality reduction, and deep learning for extracting intricate patterns from high-dimensional data. Additionally, it explores the challenges and opportunities associated with integrating machine learning into bioinformatics workflows, such as data quality, interpretability, and scalability.

Keyphrases: Bioinformatics, genomics, machine learning

@booklet{EasyChair:12447,
  author    = {Ahmed Azlan and Jane Elsa},
  title     = {Exploring the Depths: Machine Learning Applications in Bioinformatics},
  howpublished = {EasyChair Preprint 12447},
  year      = {EasyChair, 2024}}