Work Experience:
Principal Scientist (promoted to Director) (2023-25)
Biotechnology Research Center, Technology Innovation Institute (Abu Dhabi, U.A.E.)
Description: Tried my hands to setup a computational biology group in government backed organisation. I was the second hire to Executive Director and built talent pipeline of 10+ computational biologists. Together we built strategic plans, workflows with budget definition + monitored the same. During the same time established collaboration with Caltech and Burjeel Cancer Institute for novel CAR-T cell therapy. I visited Caltech for knowledge acquisition and technology transfer in protein design. Secured recurrent funding of AED 2.5 million annually for the CAR-T cell therapy project. We designed fine-tuned protein language model based solutions such as VISH-Pred, PAMPHLATE and MATE-Pred for peptide toxicity, peptide-HLA interactions and TCR-epitope interaction prediction respectively. Each of these tools outperform sota by over 10% on key quality metrics like MCC and F1-score. I was also lucky to be part of tremendous team which was undertaking comprehensive immune-transcriptome-microbiome multi-omics driven identification of biomarkers and stratification strategies for patients with colorectal cancer. I designed the multi-omics driven mICRoScore combining microbiome and immune signature to determine patients with excellent survival in large colorectal cancer cohort, published in Nature Medicine.
Featured Information:
Mall, Raghvendra, et al. "VISH-Pred: an ensemble of fine-tuned ESM models for protein toxicity prediction." Briefings in Bioinformatics 25.4 (2024): bbae270.
Goffinet, Mall, et al. "System and Method for Predicting Protein Binding Using a Multi-Modal Prediction Model." U.S. Patent Application No. 18/959,940.
Roelands, Ruppen, Ahmed, Mall et al. "An integrated tumor, immune and microbiome atlas of colon cancer." Nature medicine 29.5 (2023): 1273-1286.
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
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).
------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
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:
1. https://www.synapse.org/#!Synapse:syn20940518/wiki/602389
2. Mall, Raghvendra, et al. "A Modelling Framework for Embedding-based Predictions for Compound-Viral Protein Activity." Bioinformatics (2021).
3. Assessment of network module identification across complex diseases. Nature methods, 16(9), 2019, 843-852.
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------
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.
Developed novel approaches for understanding gene regulation using bulk RNA-Seq data with a focus on cancer.
Designed models for gene regulatory network inference (RGBM), differential network analysis (DiffNet), and disease module identification (DREAM Challenge Consortium).
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).
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.
2004: Secondary
South Point High School
India
- Percentage :- 91.5%
Teaching Assitant for Artifical Neural Networks course under guidance of Prof. Johan Suykens during Spring 2014 at KU Leuven.
Responsibilities included conducting lab sessions and correcting reports.
Teaching Assitant for Artifical Neural Networks course under guidance of Prof. Johan Suykens during Spring 2013 at KU Leuven.
Responsibilities included conducting lab sessions and correcting reports.
Worked as Research Associate at Qatar Computing Research Institute, Doha, Qatar from July 2013 till October 2013.
Worked as Research Intern at Microsoft India R & D Private Limited, Bangalore from August 2011 to January 2012.
Worked as Research Assistant under the guidance of Dr. Vikram Pudi from February 2011 to July 2011.
Worked as Research Intern at INRIA, Loria lab, France under the guidance of Prof. Jean-Charles Lamirel from August 2010 to January 2011.
Teaching Assitant for Software Engineering course under guidance of Dr. Kirti Garg during Spring, 2010 at IIIT-H.
Responsibilities included conducting lab sessions and maintaing the software engineering portal.
Teaching Assitant for Science 1 course under guidance of Prof. Harjinder Singh during Monsoon 2009 at IIIT-H.
Responsibilities included conducting tutorial sessions and correcting examinations.
Teaching Assitant for Artitifical Intelligence course under guidance of Prof. Rajeev Sanghal during Spring 2009 at IIIT-H.
Responsibilities included conducting lab and tutorial sessions.
4800+ citations on Google Scholar.
SuperNova of Q2 2023 at Biotechnology Research Center in Technology Innovation Institute, Abu Dhabi, U.A.E.
Winner of Anti-PD1 Dream Challenge
Senior IEEE Member
Finished doctorate with Summa Cum Laude.
Doctorate funded by European Research Council.
Appeared in Dean's List for 6 out of 8 semesters.
Bagged Undergraduate Research Award in 2010.
Secured Rank 70 in WBJEE 2006 (out of 100,000).
Secured Rank 1772 in AIEEE 2006 (out of 600,000).
Programming Languages C, C++, R (advanced), Java(Basic), Julia(Basic)
Scripting Languages MATLAB, Python (advanced), Unix Shell
Tools & Libraries Pytorch, Keras, Tensorflow, Cytoscape, Gephi, Networkx, Huggingface, PEFT, FAISS
Cloud Platform AWS, Google Cloud Platform
Web Tools HTML, CSS, JavaScript, PHP
Database Technologies MySQL, Sqlite
Operating Systems GNU/Linux (Fedora, OpenSuse, Ubuntu, CentOS), Windows
Miscellaneous Tools Vim, Visual Studio, LaTex, VSCode
Bioinformatics, Computational Data Analysis, Machine Learning, Networked Life, Social Network Analysis, Practical Machine Learning, Exploratory Data Analysis, Data Warehousing and Data Mining, Medical Informatics, Pattern Recognition, Web Data and Knowledge Management, Artitifical Intelligence and Experiment for Improvement.
Computer Programming, Principles of Programming Languages, Data Structures, Algorithms, Theory of Computation, Software Engineering.
Computer Organization, Operating Systems, Database Managememnt, Computer Networks.
Cognitive Science, Quantum Mechanics, Computational Methods in Electronic Structures, Discrete Mathematics, Maths-I, Maths-II, Numerical Analysis.
English, Hindi, Bengali, French (Notions)