Resources

Welcome to our News & Resources section, your go-to hub for the latest publications, news, and insights by experts in bioinformatics and AI.

Improved precision oncology question-answering using agentic LLM

Discover how GeneSilico’s gSage revolutionizes LLMs in precision oncology, offering accurate and personalized breast cancer treatment recommendations.

Prof. Debarka Sengupta Honored With Merck Young Scientist Award, 2023

The award ceremony, held in Bangalore on November 24th, 2023, stands as a significant milestone in Prof. Sengupta’s illustrious scientific journey, highlighting his unwavering dedication and outstanding contributions to advancing AI applications in cancer research.

Gene expression based inference of cancer drug sensitivity

Predictive modeling approach to infer treatment response in cancers.

Microfluidic live tracking and transcriptomics of cancer-immune cell doublets link intercellular proximity and gene regulation

Examining how cancer and immune cells interact and influence genes using microfluidic technology.

Decoding critical long non-coding RNA in ovarian cancer epithelial-to-mesenchymal transition

Discover how the lncRNA DNM3OS drives metastasis in ovarian cancer by regulating EMT, revealing new therapeutic possibilities for improving patient outcomes.

Marker-free characterization of full-length transcriptomes of single live circulating tumor cells

Explore unCTC, a cutting-edge R package that improves the detection and analysis of circulating tumor cells from single-cell data, offering new insights into metastatic cancer biology.

Mutational Landscape of the BAP1 Locus Reveals an Intrinsic Control to Regulate the miRNA Network and the Binding of Protein Complexes in Uveal Melanoma

Learn how BAP1 mutations drive metastasis in uveal melanoma and influence cancer progression across multiple tumor types through disrupted protein complexes and miRNA networks.

Dense Dilated Multi-Scale Supervised Attention-Guided Network for histopathology image segmentation

Discover how D2MSA, a cutting-edge deep learning model, revolutionizes histopathology image segmentation to improve cancer diagnosis and assessment.

Pseudo-grading of tumor subpopulations from single-cell transcriptomic data using Phenotype Algebra

Discover how SCellBOW, an innovative scRNA-seq analysis tool, uncovers hidden malignant cell subpopulations and helps predict cancer aggressiveness for tailored treatments.