Life sciences
10 topics
Life sciences
Bioinformatics
Bioinformatics applies computational and statistical methods to biological data — DNA sequences, protein structures, gene expression. Bayesian inference and graph algorithms are among its central tools.
Topics in this field
Comparative Genomics
Measuring evolutionary divergence between genomes through synteny, substitution rates, and neutrality tests.
Genome-Wide Association Studies
Statistical methods for linking genetic variants to traits or diseases across the entire genome.
Hidden Markov Models in Bioinformatics
Probabilistic sequence models for gene finding, CpG island detection, and profile-based database search.
Motif Finding
Statistical and probabilistic methods for discovering recurring sequence patterns in DNA and protein data.
Network Biology
Graph-theoretic analysis of protein-protein interaction networks, hub genes, and community structure.
Phylogenetics
Inferring evolutionary trees from molecular sequences using substitution models and likelihood methods.
Protein Structure Prediction
Energy minimization, force fields, and deep learning approaches for predicting 3D protein structure from sequence.
RNA-Seq Differential Expression
Statistical modelling of read counts to identify genes that change expression between conditions.
Sequence Alignment
Dynamic programming algorithms for aligning DNA, RNA, and protein sequences to find optimal matches.
Single-Cell RNA Sequencing
Dimensionality reduction, clustering, and trajectory inference for high-dimensional single-cell transcriptomic data.