V2I3P3

Overcoming Challenges in Bioinformatics: Enhancing Multiple Sequence Alignment, Gene Clustering and Rare Cell Identification

Dr. J. Priyadharshini^1*

Abstract

Bioinformatics plays a crucial role in understanding genetic sequences, gene clustering, and rare cell identification. However, multiple sequence alignment (MSA) often gets trapped in local optima, clustering methods face dispersion issues, and rare cell identification struggles with large datasets. This paper explores techniques to overcome these challenges by leveraging optimized algorithms and dataset preprocessing strategies. The study uses publicly available datasets from NCBI, GEO, and EMBL to validate proposed methods. Additionally, comparative analyses with alternative algorithms such as Hidden Markov Models (HMMs), Particle Swarm Optimization (PSO) and Support Vector Machines (SVMs) for rare cell detection are discussed.

Keywords: Bioinformatics; Multiple Sequence Alignment; Gene Clustering; Rare Cell Identification; Local Optima; Dataset Sources.