Unveiling Hidden Sequences: Harnessing the Viterbi Algorithm for Bioinformatics Predictions

Abstract

Context: In bioinformatics, decoding hidden states in sequences, such as predicting gene sequences, is critical for understanding biological processes. Hidden Markov Models (HMMs) are widely used for this purpose.

Problem: Accurately identifying the sequence of hidden states from observed data remains challenging due to the complexity and variability of biological sequences.

Approach: This essay explores the application of the Viterbi algorithm, a dynamic programming technique, for decoding the most probable sequence of hidden states in a synthetic dataset. The study involves creating an artificial dataset, encoding observations, implementing the Viterbi algorithm, and evaluating performance using cross-validation metrics (accuracy, precision, and recall).

Results: The Viterbi algorithm demonstrated variability in performance across different folds, with accuracy decreasing from 0.70 to 0.35, precision fluctuating significantly, and recall showing a final spike to 1.0. Visualizations of probability values and back pointers provided insights into the model’s decision-making process.

Conclusions: The Viterbi algorithm effectively identifies state sequences in synthetic data, but its performance varies with data complexity. Improvements in data quality, model parameters, and advanced techniques are suggested for enhanced predictive capabilities…