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

(in submission), 2019

Genome Medicine, 2019

GigaScience, 2019

Genome Research, 2017

RECOMB-2014, 2014

Recent Publications

More Publications

PubMed | Google Scholar | Computer Science Bibliography (dblp)

(2019). Y-box Binding Protein-1 is Crucial in Acquired Drug Resistance Development in Metastatic Clear-Cell Renal Cell Carcinoma. (in submission).

(2019). Identification of Predictive Gene Signature to Guide Precision Oncology of Clear-Cell Renal Cell Carcinoma. (in submission).

(2019). Well-Differentiated Papillary Mesothelioma of the Peritoneum is Genetically Distinct from Malignant Mesothelioma. (in submission).

Preprint

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

PDF Dataset Online PubMed Dataset2 Research Highlight

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

PDF Code Dataset Online PubMed

(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).

PDF

(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

Software

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

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

  • Group Segmentation and Diversity - Diversity in cooking-fuel choice and group segmentation for promoting cleaner cooking

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

A team of scientists partly funded by the Terry Fox Research Institute has shown that nearly half of all patients with peritoneal mesothelioma, an extremely rare and often fatal form of cancer that originates in the peritoneal lining of the abdomen, may benefit from immunotherapy.

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Our recent study on Peritoneal Mesothelioma (Shrestha et al. Genome Medicine. 2019) was highlighted by Marc Ladanyi et al. Abstract: As trials of immune checkpoint inhibitor (ICI) therapies demonstrate responses in only a minority of pleural mesotheliomas (PlMs) and largely exclude patients with the related peritoneal mesothelioma (PeM), clinicians need predictive biomarkers of response and inclusion of PeM patients in future trials. A new study finds that loss of the deubiquitinase BAP1 in PeM correlates with an inflammatory tumor microenvironment, suggesting that BAP1 status might identify PeM, and possibly PlM, patients who would benefit from ICI therapy.

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Here, I attempted to visualize bibliography in my PhD thesis.

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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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