Machine Learning for Biomarker Discovery: Unlocking the Future of Precision Medicine INTRODUCTION Biomarkers are instrumental in the diagnosis of disease , the prediction of patient outcomes, and the development of targeted therapies. The advent of machine learning (ML) has transformed biomarker discovery by facilitating the processing of large datasets, detection of latent patterns, and enhanced predictive accuracy. Conventional biomarker identification relied on experimental approaches , which were time-consuming and expensive . With ML, scientists can process genomic, transcriptomic, proteomic, and metabolomic data quickly , resulting in more accurate and robust biomarker identification. This article ...
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Essential Tools and Databases in Bioinformatics - Part 2 Bioinformatics is a constantly developing discipline allowing researchers to analyze huge biological data sets efficiently . In Part 1 , we discussed key tools for sequence alignment, phylogenetics, gene annotation, protein structure prediction, and microbiome analysis. In this second part, we discuss advanced bioinformatics tools applied in structural bioinformatics, pathway and network analysis, transcriptomics, molecular docking, and machine learning in bioinformatics. 1. Structural bioinformatics Structural bioinformatics is the prediction and analysis of the three-dimensional (3D) structure of biomolecules, which plays an important role in understanding protein function, molecular interactions, and drug design. A number of tools and databases...