Hidden Markov Models Explained: Bioinformatics Breakthroughs in Gene Prediction and Protein Analysis

Hidden Markov Models Bioinformatics Machine Learning Gene Prediction Protein Structure
Dive into the world of Hidden Markov Models (HMMs) and their groundbreaking applications in bioinformatics. This video explores how HMMs, once a mathematical tool for stochastic processes, have become essential for solving complex biological sequence problems.
Discover the three core problems of HMMs: evaluation, decoding, and learning, along with powerful algorithms like Viterbi and Baum-Welch that power these solutions.
Learn about key bioinformatics domains where HMMs shine:
- Transmembrane protein prediction for drug discovery
- Gene finding with tools like GENSCAN and AUGUSTUS
- Multiple sequence alignment using Profile HMMs in resources like Pfam and HMMER
- CpG island prediction for epigenetic insights
- Copy number variation detection for genetic disease studies
We'll discuss the strengths of HMMs, such as their statistical rigor and interpretability, while addressing limitations like computational demands and linearity assumptions.
Finally, explore how integrating HMMs with next-generation sequencing and advanced ML will shape the future of computational biology, genome annotation, and precision medicine.
Based on a recent review in Genes & Diseases, this video breaks down the theory and real-world impact of HMMs in bioinformatics research.

Видео Hidden Markov Models Explained: Bioinformatics Breakthroughs in Gene Prediction and Protein Analysis канала Timeless Content