Machine Learning + Genome-Wide Analysis: Predicting Gene Family Functions (In Silico Approach)

Описание: Machine learning is transforming genome-wide analysis by enabling accurate prediction of gene family functions using in silico approaches. In this video, we explore how computational models integrate genomic features such as conserved domains, gene structure, expression profiles, phylogeny, and protein properties to predict functional roles of gene families across plant and other genomes.

You will learn the complete workflow—from genome data retrieval and gene family identification to feature extraction and machine learning model training (Random Forest, SVM, Neural Networks). Real-world examples from plant genomes demonstrate how AI-driven predictions help uncover regulatory roles, stress-response genes, and evolutionary patterns without wet-lab experiments.

This tutorial is ideal for students, researchers, and bioinformatics enthusiasts interested in genome-wide analysis, functional genomics, and AI applications in biology.

📌 Topics Covered:

Genome-wide identification of gene families

Feature engineering from genomic and protein data

Machine learning models for function prediction

In silico validation strategies

Applications in plant genomics and systems biology

🔑 Keywords

Machine learning genomics, genome wide analysis, gene family prediction, in silico genomics, AI in bioinformatics, functional genomics, plant genome analysis, gene annotation, random forest genomics, deep learning biology, protein feature extraction, comparative genomics, bioinformatics tutorial, computational biology

#️⃣ Hashtags

#MachineLearning
#GenomeWideAnalysis
#GeneFamily
#InSilicoBiology
#Bioinformatics
#FunctionalGenomics
#AIinBiology
#PlantGenomics
#ComputationalBiology
#SystemsBiology