Bioinformatics and AI combine computational methods with biological data to study genes, proteins, cells, and disease. These tools help researchers interpret complex datasets and prioritize targets for diagnostics and drug development.
Biological knowledge graphs are structured networks that represent biological entities and the relationships among them.
The use of explainable or interpretable artificial intelligence methods to identify or characterize biomarkers.
Genomic and protein language models are machine-learning models trained on DNA, RNA, or protein sequences to learn their patterns and representations.
Genomic variant calling is the computational process of identifying genetic differences in sequencing data relative to a reference genome.
Immunoinformatics uses computational and data-analysis methods to study immune-system processes and support tasks such as epitope prediction.
In-silico drug discovery uses computational methods to identify, design, and optimize potential drug candidates.
The study of microbial communities associated with humans using approaches such as metagenomic sequencing, profiling, and computational analysis.
Software platforms and computational frameworks that combine data from multiple omics domains for joint analysis.
Pangenomics studies the full set of genes or genomic sequences found across a group of organisms, including sequences shared by all members and those present in only some.
Computational methods that predict a protein’s three-dimensional structure or design new proteins with specified sequences, structures, or functions.
Spatial transcriptomics measures gene expression while retaining its location in tissue, and single-cell RNA sequencing profiles RNA in individual cells.
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