Bioinformatics in cancer is the application of computational and statistical methods to biological data in order to understand the molecular basis of cancer and to support its diagnosis and treatment. It combines large-scale data from genomics, transcriptomics, proteomics, and related fields with algorithms and soft…
Bioinformatics in cancer is the application of computational and statistical methods to biological data in order to understand the molecular basis of cancer and to support its diagnosis and treatment. It combines large-scale data from genomics, transcriptomics, proteomics, and related fields with algorithms and software tools that can identify mutations, altered gene and protein expression, and disrupted molecular pathways associated with tumor development and progression. By mining and integrating these datasets, cancer bioinformatics helps reveal the genetic and molecular mechanisms driving malignancy, uncover candidate biomarkers for detection and prognosis, and nominate potential targets for therapy. The approach is central to precision and personalized medicine, where treatment decisions are increasingly guided by the molecular characteristics of an individual tumor. As high-throughput technologies generate ever larger volumes of data, computational analysis has become indispensable for turning raw measurements into clinically meaningful insight. Within the scope of Proteomics and Genomics Research, which addresses the analysis of genes, proteins, and their roles in disease, this page gathers peer-reviewed, open-access research relevant to computational and molecular approaches in cancer and disease, including work applying proteomic and genomic techniques to cancer, diagnostics, and personalized medicine.
5 peer-reviewed articles, ranked by relevance. Each links to its DOI.
The 5 articles above have been cited 38 times in the scholarly literature. Citation data via OpenAlex and Crossref, updated Jun 2026.
A sample of recent works citing this journal's research on Bioinformatics Cancer, linking to each citing work.