Area of research
Hematology · Computational Theory and Mathematics
Research interest
Research interests include Biology, Chromatin, Computational biology, CTCF, Genetics, and Lung cancer.
Binding domain mutations provide insight into CTCF’s relationship with chromatin and its contribution to gene regulation
Self-supervised learning reveals clinically relevant histomorphological patterns for therapeutic strategies in colon cancer
The stress response regulator HSF1 modulates natural killer cell anti-tumour immunity
Cell-type-specific prediction of 3D chromatin organization enables high-throughput in silico genetic screening
Inflammation in the tumor-adjacent lung as a predictor of clinical outcome in lung adenocarcinoma
H3K27ac bookmarking promotes rapid post-mitotic activation of the pluripotent stem cell program without impacting 3D chromatin reorganization
Deep Learning and Pathomics Analyses Reveal Cell Nuclei as Important Features for Mutation Prediction of BRAF-Mutated Melanomas
Targeting Mitochondrial Structure Sensitizes Acute Myeloid Leukemia to Venetoclax Treatment
A Deep Learning Framework for Predicting Response to Therapy in Cancer
Machine learning and data mining frameworks for predicting drug response in cancer: An overview and a novel in silico screening process based on association rule mining
NSD2 overexpression drives clustered chromatin and transcriptional changes in a subset of insulated domains
MAPK pathway and B cells overactivation in multiple sclerosis revealed by phosphoproteomics and genomic analysis
EpiMethylTag: simultaneous detection of ATAC-seq or ChIP-seq signals with DNA methylation
Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning
Stratification of TAD boundaries reveals preferential insulation of super-enhancers by strong boundaries