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Saurav Mallik
Postdoctoral fellow at Biomedical Informatics
Artificial Intelligence
Bioinformatics
Gene Expression And Cancer Classification
Healthcare Technology
Machine Learning
2.8K
Citations
29
H-index
0
Patents
99
Publications
0
Grants
$0
Total Funding
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Research Contributions
Developed novel deep learning models for brain tumor status prediction using MRI data.
Developed and validated an explanation-driven deep learning model for predicting brain tumor status using MRI image data, contributing to improved diagnostic accuracy.
Frontiers in Genetics (2022)
Evaluated supervised methods for identifying differentially methylated regions in Illumina methylation arrays.
Conducted a comparative study of supervised methods for identifying differentially methylated regions, providing insights into the optimal approaches for analyzing methylation data.
Briefings in Bioinformatics (2018)
Investigated the role of HIV Nef protein in modulating cellular and exosomal miRNA profiles.
Studied the impact of the HIV Nef protein on miRNA profiles in human monocytic cells and exosomes, contributing to understanding of HIV pathogenesis.
Journal of Extracellular Vesicles (2014)
Developed a novel framework for cancer classification using hybrid feature selection.
Proposed a new framework for cancer classification using a consecutive utilization of hybrid feature selection, improving the accuracy of cancer type prediction.
BMC Bioinformatics (2023)
Explored the application of machine learning for autism detection using MRI brain images.
Applied a hybrid deep CNN with DM-Resnet classifier for autism detection from MRI brain images, contributing to early diagnosis.
IEEE Access (2023)
Integrated gene expression and methylation data for tumor prediction using association rule mining.
Developed an association rule mining-based approach to integrate gene expression and methylation data for improved tumor prediction.
IEEE Conference on Bioinformatics and Biomedicine (2013)
Developed a novel graph topology-based GO-similarity measure for signature detection from multi-omics data.
Proposed a novel graph topology-based GO-similarity measure for signature detection from multi-omics data, improving the accuracy of biomarker discovery.
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2020)
Research Highlights
Dr. Saurav Mallik is a postdoctoral fellow at Harvard University specializing in applying machine learning and AI to cancer research and diagnostics. His work focuses on developing innovative computational methods for biomarker discovery and disease prediction using multi-omics data.
Research Areas
Artificial IntelligenceBioinformaticsGene Expression And Cancer ClassificationHealthcare TechnologyMachine Learning
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