Krithika is a Data Scientist and has over 14 years professional work experience and combines her interdisciplinary skills in health data science, informatics and applied statistics for numerous projects.
Krithika employs machine learning, applied statistics, and systems biology techniques for in-depth analysis. Additionally, she applies her data science skills to clean, manage and analyze clinical and genomic data; and develop new algorithms. Her work also involves using cloud computing and high-performance computing (HPC) for analyzing large-scale data analysis of genomic data.
- Participated in the HTAN Data Jamboree, a hackathon-style team event, where we developed as a team, a multi-agent LLM to enable endusers to easily explore and query spatial transcriptomics data located on the HTAN BigQuery and Synapse platforms (Nov 2024). Link: https://github.com/NCI-HTAN-Jamborees/HTANalyzer-LLM)
- The Targetable Molecular Algorithm (TMA) platform, an interactive online tool for improving precision oncology in NSCLC by streamlining NGS-guided molecular testing and bridging gaps in care. Article in JAMIA Open (Nov 2024) Bhuvaneshwar K et al
- Applied data-driven approaches to explore the multi-dimensional landscape of infected lung tissue in coronavirus patients. This proof-of-concept study provides insights into the complex interactions between viral infection, immune response and the lung microbiome in connection with Long COVID. Article in Heliyon (June 2024) Bhuvaneswhar K et al
- Review article in Briefings in Bioinformatics summarizing existing knowledge derived from molecular and cellular data, and described challenges and areas of opportunities in this rapidly evolving field of mental health and neuroscience (March 2024). Bhuvaneswhar K et al. PMID: 38493340
- Publication in Nature Communications (Dec 2022) regarding a large scale consortium effort to develop a #federated learning approach for AI models based on #braincancer #imaging. Federated learning enables big data for rare cancer boundary detection: PMID 36470898
- Publication in Nature Scientific Data (Sep 2022) titled 'Enhancing the REMBRANDT MRI brain cancer collection' . In this paper, we took raw #MRI scans from the REMBRANDT #braincancer collection, and performed volumetric #segmentation to identify subregions of the brain. #Radiomic features were then extracted to represent the MRIs in a #quantitative yet summarized format. Read more here: https://www.linkedin.com/feed/update/urn:li:activity:6942509381666181120/. Link to the paper: Sayah A, Bencheqroun C, Bhuvaneshwar K et al PMID 35701399
- Book chapter: Bioinformatics in Mental Health: Deriving Knowledge from Molecular and Cellular Data (Chapter 11). [Bhuvaneswhar K et al.]. Springer (https://www.springer.com/gp/book/9783030) (2021)
- Publication on the second largest brain cancer collection in Nature Scientific Data (Aug 2018) titled 'The REMBRANDT study, a large collection of genomic data from brain cancer patients'. Gusev Y & Bhuvaneswhar K et al. PMID 30106394 Co-first authors