About Bioinformatics in Cancer Research

The utility of bioinformatics to cancer research has expanded greatly in the past decade with the introduction of large-scale data repositories such as The Cancer Genome Atlas (TCGA). Large publicly available data sources such as these combined with recent technological advances in single cell sequencing, spatial transcriptomics and proteomics, digital pathology, structure prediction of protein and antibody structure, CRISPR based screens, and CAR-T cell based therapies have yielded a deluge of multimodal data that requires modern algorithms, machine learning techniques related to deep learning and computer vision, and modelling techniques to yield breakthroughs both at the bench and in the oncology clinic.

This content collection is dedicated to research related to bioinformatics in cancer published via the F1000Research platform. We welcome submissions (e.g., research articles, brief reports, method articles, and reviews) on computational cancer research, including but not limited to, digital pathology and computer vision, sequence analysis, structural biology related to therapeutic development, survival prediction, plus deep learning and computational approaches that study the following areas: the interface between the microbiome and the immune system, the tumour-immune interface, intra and extra tumoral cell-cell crosstalk, prediction of ideal therapy combinations and mechanistic basic biology. Research that is time-sensitive and requires rapid publication is particularly encouraged for submission.

Keywords: Cancer Bioinformatics; Structural Biology; Digital Pathology; Tumour Immunology; Sequence Analysis; Therapeutic Development

Submission deadline: This Collection is now closed for submissions.

This collection is part of the Bioinformatics and Oncology Gateways.

Any questions about this collection? Please get in contact directly with research@f1000.com.