Departments

Administrative Divisions

1. Research Division
2. OPS Division
3. Software Development Division

Functional Divisions

The department is functionally grouped into the following divisions, comprising select individuals from each administrative division, being grouped according to the academic/clinical/research areas they work in. These functional divisions allow people with different skills to work towards common goals.

1. Computational Biology
   • Genomics
   • Structural biology
2. Computational Imaging
   • Pathomics and Pathology Image Processing
   • Radiomics, Radiation Oncology, Radiology and Other related Image Processing
   • Other Images
3. Health Records (EMR/EHR and others)

Computational Resource Access for Scientific Computing Applications
The department provides access to the computing resources at its disposal, for research and academic use, to all bona fide members of all TMC units, units affiliated to DAE, collaborators of TMC faculty, and any other not-for-profit academic/research organization. The access policy is detailed in this document.

If your use case is not listed or you need more guidance, you may contact the department at digitalcomputonco.dept@actrec.gov.in

The Department manages a High Performance Computing Centre with the following resources

1. High Performance Cluster Nodes

These are primarily CPU-based nodes (with a few GPU nodes) meant for genomics, bioinformatics, and other omic analysis. Image analysis and other GPU-intensive applications can be performed on the GPU nodes. For access to this compute resources, bona fide users need to fill out this form:https://forms.gle/QV8ixvXjASjuQaxr7 The following clusters is currently available

• Bioinfinix Cluster This is the latest cluster established in 2025, and is the primary cluster to which access can be provided, subject to the availability of free resources.

GPU Server for DL/AI/ML/Image processing

These are multi-GPU servers with the latest NVIDIA GPUs, specifically designed for deep learning, AI/ML, and Image processing applications where GPU-based processing is essential. For access to these computer resources for bona fide users, contact us at digitalcomputonco[dot]dept[at]actrec[dot]gov[dot]in

• HPE Apollo Gen-10 8x A100 Computing Server: 8x A100 accelerators, 128 CPU cores, 2TB RAM, 30TB NVME SSD with a combined performance of 5 petaflops for AI workloads.
• 8x H200 Computing Server: in-process

Scientific AI/ML Model Deployment

• In addition to the scientific compute resources for research, the department manages model deployment and hosting environments on virtual machines using on-premise servers and HCI with GPUs for deploying AI/ML models, websites, and other user-facing applications. The following services/models are deployed for use within TMC or with public access:

Medical & Scientific Data Storage
The department offers short-term and long-term enterprise-grade storage for laboratories, departments, and staff from within TMC to support them in their academic and research pursuits. If you are a bona fide TMC employee/faculty/student, please get in touch with us with your requirement at digitalcomputonco[dot]dept[at]actrec[dot]gov[dot]in

Glass slide Digitization/Whole slide Scanning Service
Digitization of standard Glass slides for brightfield imaging is provided as a paid service in coordination with the Computational Pathology Laboratory, for academic usage only, to researchers within TMC, DAE, as well as researchers outside TMC/from other organizations. For researchers within TMC, please use the following requisition system for raising a request for slide digitization http://10.100.36.253/actapps/WSI.aspx. For researchers outside TMC, please contact us at tmccomputpath[at]actrec[dot]gov[dot]in or digitalcomputonco[dot]dept[at]actrec[dot]gov[dot]in

Data Analytics & Consulting Services
The department provides analytical services for computational biology, computational imaging, AI/machine learning/deep learning projects to bona fide TMC students/faculty, and employees. If you have a project where you need AI/ML/DL/bioinformatics expertise, please get in touch with us at digitalcomputonco[dot]dept[at]actrec[dot]gov[dot]in. A requisitioning system is in the works

Collaborative Projects
If you wish to collaborate with us, you may please contact the individual staff member on their email addresses mentioned below, or the department at digitalcomputonco[dot]dept[at]actrec[dot]gov[dot]in

1. PhD in Healthcare/Oncology Data Science & AI/ML:

The department plans to conduct a PhD program with a focus on Healthcare Data Science and AI/ML. Candidates will be selected through the GATE, and other entrance examinations as per TMC guidelines, spanning areas of Computational Biology, Bioinformatics, Computational Image Processing, Computational processing of EHR, etc., for Healthcare applications.

2. Post-Doctoral Program in Healthcare/Oncology Data Science & AI/ML:

The department plans to run a post-doctoral program spanning areas of Computational Biology, Bioinformatics, Computational Image Processing, Computational processing of EHR, etc., for healthcare applications.

3. Observer, Internships, Short-term Fellowships

The department will host the following short-term fellowships/internships (weeks to months):

• For engineering/data science/bioinformatics/AI/ML students under the guidance of the Research Division Staff.
• For graduate, postgraduate, and super-speciality medical students, and practicing medical doctors
4. Long-term fellowships

The department will start 2-year HBNI fellowships & 1-year TMC fellowships in partnership with existing medical departments for post-graduate and super-speciality doctors having completed their qualifying degree.

Projects & Areas of Active Research

• Baid, U., Rane, S. U., Talbar, S., Gupta, S., Thakur, M. H., Moiyadi, A., & Mahajan, A. (2020). Overall Survival Prediction in Glioblastoma With Radiomic Features Using Machine Learning. Frontiers in Computational Neuroscience, 14, 513481. https://doi.org/10.3389/fncom.2020.00061
• Baid, U., Talbar, S., Rane, S., Gupta, S., Thakur, M. H., Moiyadi, A., Sable, N., Akolkar, M., & Mahajan, A. (2020). A Novel Approach for Fully Automatic Intra-Tumor Segmentation With 3D U-Net Architecture for Gliomas. Frontiers in Computational Neuroscience, 14, 495068. https://doi.org/10.3389/fncom.2020.00010
• Anand, D., Yashashwi, K., Kumar, N., Rane, S., Gann, P. H., & Sethi, A. (2021). Weakly supervised learning on unannotated H&E-stained slides predicts BRAF mutation in thyroid cancer with high accuracy. The Journal of Pathology, 255(3), 232-242. https://doi.org/10.1002/path.5773
• Patil, A., Diwakar, H., Sawant, J., Kurian, N. C., Yadav, S., Rane, S., Bameta, T., & Sethi, A. (2022). Efficient quality control of whole slide pathology images with human-in-the-loop training. Journal of Pathology Informatics, 14, 100306. https://doi.org/10.1016/j.jpi.2023.100306
• Gadewal N, Patidar D, Natu A, Gupta S, Bastikar V. In silico screening of phytochemicals against chromatin modifier, SETD7 for remodeling of the immunosuppressive tumor microenvironment in renal cancer. Molecular Diversity. 2024 Nov 27.
• Gadewal N, Natu A, Sen S, Rauniyar S, Bastikar V, Gupta S. Integrative epigenome-transcriptome analysis unravels cancer-specific over-expressed genes potentially regulating immune microenvironment in clear cell renal cell carcinoma. Biochimica et Biophysica Acta (BBA) - General Subjects. 2024 May;1868(5):130596.
• Dey P, Gadewal N, De A. Pathogenic HER3 dimerization domain mutations create a structural bias towards un-conventional EGFR-HER3 signalling axis in breast cancer. International Journal of Biological Macromolecules. 2023 Jul 1; 242:124765.
• Shah SG, Mandloi T, Kunte P, Natu A, Rashid M, Reddy D, Gadewal N, Gupta S. HISTome2: a database of histone proteins, modifiers for multiple organisms and epidrugs. Epigenetics & Chromatin. 2020 Aug 3;13(1):31.
• Bhattacharya S, Reddy D, Jani V, Gadewal N, Shah S, Reddy R, Bose K, Sonavane U, Joshi R, Gupta S*. Histone isoform H2A1H promotes attainment of distinct physiological states by altering chromatin dynamics. Epigenetics & Chromatin. 2017 Oct 18; 10(1):48.