Research Statement


My primary research goals are directed towards understanding the complex nature of cancer by computationally modelling cancer omics data. My research is primarily focused on development and application of computational methods integrating cancer omics data so as to enable precision oncology. The methods I develop leverage complex network approaches which helps to bring these multi-dimensional data into a single-analysis framework.

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Selected Publications

Genome Medicine (In press), 2019

GigaScience (In press), 2019

Genome Research, 2017

RECOMB-2014, 2014

Recent Publications

More Publications

PubMed | Google Scholar | Computer Science Bibliography (dblp)

(2019). BAP1 Haploinsufficiency Predicts a Distinct Immunogenic Class of Malignant Peritoneal Mesothelioma. Genome Medicine (In press).

Preprint Dataset Online

(2019). Combinatorial Detection of Conserved Alteration Patterns for Identifying Cancer Subnetworks. GigaScience (In press).

Preprint Code Dataset

(2018). Pathway and network analysis of more than 2,500 whole cancer genomes. (in submission).

Preprint

(2018). Computational prioritization of cancer driver genes for precision oncology. University of British Columbia (PhD Thesis).

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(2018). High-throughput detection of RNA processing in bacteria. BMC Genomics.

PDF Online PubMed

(2018). Deep Genomic Signature for early metastasis prediction in Prostate Cancer. (in submission).

Preprint

(2018). Ultra High-Dimensional Nonlinear Feature Selection for Big Biological Data. IEEE Transactions on Knowledge and Data Engineering.

PDF Online

(2018). Metagenomic and metatranscriptomic analysis of human prostate microbiota from patients with prostate cancer. (in submission).

(2017). HIT'nDRIVE: Patient-Specific Multi-Driver Gene Prioritization for Precision Oncology. Genome Research.

PDF Code Project Slides Video Online PubMed Video2

(2016). BIRC6-targeting as potential therapy for advanced, enzalutamide-resistant prostate cancer. Clinical Cancer Research.

PDF Online PubMed

Softwares

  • HIT’nDRIVE - An algorithm for cancer driver genes prioritization

  • cd-CAP - Combinatorial Detection of Conserved Alteration Patterns for Identifying Cancer Subnetworks

Conferences & Events

More Talks

First Nepal Winter-School on AI
Dec 20, 2018 9:00 AM
Inactivation of BAP1 Predicts a Distinct Immunogenic Class of Malignant Peritoneal Mesothelioma
Sep 28, 2018 9:30 AM
BAP1 Loss Predicts Therapeutic Vulnerability in Malignant Peritoneal Mesothelioma
May 4, 2018 11:00 AM
HIT'nDRIVE: Patient-Specific Multi-Driver Gene Prioritization for Precision Oncology
Nov 5, 2017 11:00 AM
HIT'nDRIVE: Patient-Specific Multi-Driver Gene Prioritization for Precision Oncology
Nov 4, 2017 11:00 AM

Recent Posts

Details in github project Genomic knowledge are often curated in the form of Genes. For example, a geneset of genes mapping to the chromosome locus chr8q24; a geneset of genes known to involve in DNA Repair Module, etc. Similarly, this data structure is also representative of patient-Genes data. This can be thought of as a bipartite graph representing relation between individual Module to a gene. Often bioinformaticians are interested to visualize if there is any kind of relation between the Modules.

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