Biography

 Work Experience:

Senior Research Scientist (2021-23) 

St. Jude Children's Research Hospital (Memphis, TN, U.S.A.)

Description: To have more translational impact, I moved to St. Jude Children's Research Hospital. Here, my aim was to identify sensors, adapters, and effector molecules inducing cell death as a first line of defense (innate immunology) when exposed to dangerous pathogens like bacteria, viruses, fungi, and different pathogenic stimuli (including cancer). To achieve this goal, I design and develop data-driven integrative multiomics frameworks to narrow the search space of biomarkers, which are further validated through in-vitro and in-vivo techniques. I have mastered the skills to curate and integrate different data modalities, including micro arrays, RNA-Seq, whole genome CRISPR screens and single-cell transcriptomics for this purpose.

Featured information:

1. Mall, Bynigeri et al. "Pancancer transcriptomic profiling identifies key PANoptosis makers as therapeutic targets for oncology." NAR Cancer (2022): zcac033.

2. Karki*, Lee*, Mall et al. "ZBP1-dependent inflammatory cell death, PANoptosis, and cytokine storm disrupt IFN therapeutic efficacy during coronavirus infection." Science Immunology (2022): eabo6294.

3. Sundaram*,Pandian*,Mall, et al. "NLRP12-PANoptosome activates PANoptosis and pathology in response to heme and PAMPs." Cell (2023).

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Research Scientist (U.S. equivalent Assistant Professor) (2018-21)

Qatar Computing Research Institute (Doha, Qatar)

 Description: Focused energy on amalgamation of the expertise gained during postdoctoral work by moving towards problems related to drug sensitivity, drug repurposing and precision medicine. In a first project, I utilized RGBM as a key component in our network-based framework for identification of driver master regulators (MRs) for immune-excluded (cold tumors) phenotype at a pancancer scale leading to identification of several master regulators coherently associated with the immune-silent phenotype along with identification of NOTCH signaling pathway as a therapeutic target to possibly readjust the immunosuppressive tumor microenvironment.

Secondly, lead a team of 4 people (Team Resham) in the CTD2-BeatAML Challenge to predict quantitative ex-vivo drug-sensitivity tailored to individual treatments by targeting chemotherapeutic agents based on genomic variants, gene expression, and clinical data (multi-omics). We achieved the top position during the leaderboard phase and were among the top 10 teams during the validation phase.

We recently devised a data-driven machine learning framework to identify repurposable FDA approved drugs for the SAR-COV-2 virus when high-quality 3d-structure of the viral proteins are not available. Identified drugs such as Ritonavir (potent against SARS-COV-2 as shown by Pfizer) and Brilacidin which have been fast tracked by FDA for clinical trial. (US17/804,408 patent pending) 

Featured Information:

https://www.synapse.org/#!Synapse:syn20940518/wiki/602389

Mall, Raghvendra, et al. "A Modelling Framework for Embedding-based Predictions for Compound-Viral Protein Activity." Bioinformatics (2021).

Assessment of network module identification across complex diseases. Nature methods, 16(9), 2019, 843-852.

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 Postdoctoral Researcher (2016-18)

Qatar Computing Research Institute (Doha, Qatar)

Description:  Focused efforts on the application of data-driven ML techniques for problems in computational biology in particular network biology.

1. Developed novel approaches for understanding gene regulation using bulk RNA-Seq data with a focus on cancer. 

2. Designed models for gene regulatory network inference (RGBM), differential network analysis (DiffNet), and disease module identification (DREAM Challenge Consortium). 

3. RGBM has been utilized for identifying key driver master regulators (transcription factors) differentiating various sub types of glioblastomas, characterizing FGFR3-TACC3 gene fusions in glioblastomas, understanding the molecular landscape of gliomas with Neurofibromatosis type 1 (NF1).

4. One of the first groups to showcase the efficiency of deep learning for sequence-based models for protein feature prediction problems including protein solubility (PaRSnIP, DeepSol) and protein crystallization propensity (DeepCrystal).

Featured Publications:

1. RGBM: regularized gradient boosting machines for identification of the transcriptional regulators of discrete glioma subtypes. Nucleic acids research, 2018, 46(7), e39-e39.

2. A metabolic function of FGFR3-TACC3 gene fusions in cancer. Nature, 2018, 553(7687), 222-227.

3. DeepSol: a deep learning framework for sequence-based protein solubility prediction. Bioinformatics, 34(15), 2605-2613.