@inproceedings{bib_A_Si_2025, AUTHOR = {Srivastava, Akansha and Krishnanunni, Vinod Palakkad }, TITLE = {A Single-Cell Network Approach to Decode Metabolic Regulation in Gynecologic and Breast Cancers}, BOOKTITLE = {npj Systems Biology and Applications}. YEAR = {2025}}

Cancer metabolism is characterized by significant heterogeneity, presenting challenges for treatment efficacy and patient outcomes. Understanding this heterogeneity and its regulatory mechanisms at single-cell resolution is crucial for developing personalized therapeutic strategies. In this study, we employed a single-cell network approach to characterize malignant heterogeneity in gynecologic and breast cancers, focusing on the transcriptional regulatory mechanisms driving metabolic alterations. By leveraging single-cell RNA sequencing (scRNA-seq) data, we assessed the metabolic pathway activities and inferred cancer-specific protein-protein interactomes (PPI) and gene regulatory networks (GRNs). We explored the crosstalk between these networks to identify key alterations in metabolic regulation. Clustering cells by metabolic pathways revealed tumor heterogeneity across cancers, highlighting variations in oxidative phosphorylation, glycolysis, cholesterol, fatty acid, hormone, amino acid, and redox metabolism. Our analysis identified metabolic modules associated with these pathways, along with their key transcriptional regulators. These findings provide insights into the complex interplay between metabolic rewiring and transcriptional regulation in gynecologic and breast cancers, paving the way for potential targeted therapeutic strategies in precision oncology. Furthermore, this pipeline for dissecting coregulatory metabolic networks can be broadly applied to decipher metabolic regulation in any disease at single-cell resolution.

@inproceedings{bib_Cont_2025, AUTHOR = {Chauhan, Ekansh and V, Jawahar C and Krishnanunni, Vinod Palakkad and Sharma, Anila and Sharma, Amit and Nishadham, Vikas and Vrajlal, Padariya Karan and Ghughtyal, Asha and Kumar, Ankur and Gupta, Gurudutt and Mehta, Anurag }, TITLE = {Contrasting Low and High-Resolution Features for HER2 Scoring using Deep Learning}, BOOKTITLE = {Journal of Pathology Informatics}. YEAR = {2025}}

Breast cancer, the most common malignancy among women, requires precise detection and classification for effective treatment. Among immunohistochemistry (IHC) biomarkers, HER2 plays a critical role in guiding therapy decisions. In particular, a recent clinical trial has shown that 3-way classification of HER2 (0, low, and high) using IHC is essential for identifying patients with HER2 low expression who may benefit from new targeted therapies. However, traditional IHC classification relies on the expertise of pathologists, making it labor-intensive and prone to significant inter-observer variability. To address these challenges, this study introduces the India Pathology Breast Cancer Dataset, comprising HER2 IHC slides from 500 patients, with a primary focus on automating 3-way HER2 classification. Evaluation of multiple deep learning models revealed that an end-to-end ConvNeXt network using low-resolution IHC images achieved an F1 score of 83.52%, representing an improvement of 5.35% over patch-based methods. Class-wise F1 scores were 75.6% for HER2-0, 82.4% for HER2-low, and 91.5% for HER2-high, indicating the challenge in distinguishing HER2-0 and HER2-low cases. This study highlights the potential of simple yet effective deep learning techniques to significantly improve accuracy and reproducibility in breast cancer classification, supporting their integration into clinical workflows for better patient outcomes.

@inproceedings{bib_Lupu_2024, AUTHOR = {Sharma, Amit and Chauhan, Ekansh and Uppin, Megha S and Rajasekhar, Liza and V, Jawahar C and Krishnanunni, Vinod Palakkad }, TITLE = {Lupus Nephritis Subtype Classification with only Slide Level labels}, BOOKTITLE = {Medical Imaging with Deep Learning}. YEAR = {2024}}

Lupus Nephritis classification has historically relied on labor-intensive and meticulous glomerular-level labeling of renal structures in whole slide images (WSIs). However, this approach presents a formidable challenge due to its tedious and resource-intensive nature, limiting its scalability and practicality in clinical settings. In response to this challenge, our work introduces a novel methodology that utilizes only slide-level labels, eliminating the need for granular glomerular-level labeling. A comprehensive multi-stained lupus nephritis digital histopathology WSI dataset was created from the Indian population, which is the largest of its kind. LupusNet, a deep learning MIL-based model, was developed to classify LN subtypes. The results underscore its effectiveness, achieving an AUC score of 91.0%, an F1 score of 77.3%, and an accuracy of 81.1% on our dataset in distinguishing membranous and diffused classes of LN

@inproceedings{bib_IPD-_2024, AUTHOR = {Chauhan, Ekansh and Sharma, Amit and Uppin, Megha Saha and Kondamadugu, Manasa and V, Jawahar C and Krishnanunni, Vinod Palakkad }, TITLE = {IPD-Brain: An Indian histopathology dataset for glioma subtype classification}, BOOKTITLE = {Scientific Data}. YEAR = {2024}}

The efective management of brain tumors relies on precise typing, subtyping, and grading. We present the IPD-Brain Dataset, a crucial resource for the neuropathological community, comprising 547 high-resolution H&E stained slides from 367 patients for the study of glioma subtypes and immunohistochemical biomarkers. Scanned at 40x magnifcation, this dataset is one of the largest in Asia, specifcally focusing on the Indian demographics. It encompasses detailed clinical annotations, including patient age, sex, radiological fndings, diagnosis, CNS WHO grade, and IHC biomarker status (IDH1R132H, ATRX and TP53 along with proliferation index, Ki67), providing a rich foundation for research. The dataset is open for public access and is designed for various applications, from machine learning model training to the exploration of regional and ethnic disease variations. Preliminary validations utilizing Multiple Instance Learning for tasks such as glioma subtype classifcation and IHC biomarker identifcation underscore its potential to signifcantly contribute to global collaboration in brain tumor research, enhancing diagnostic precision and understanding of glioma variability across diferent populations.

@inproceedings{bib_AI-A_2023, AUTHOR = {Jayant, Talwar Vivek and V, Jawahar C and Krishnanunni, Vinod Palakkad and Singh, Pragya and Mukhia, Nirza and Shetty, Anupama and Birur, Praveen and Desai, Karishma M. and Sunkavall, Chinnababu and Varma, Konala S. and Sethuraman, Ramanathan }, TITLE = {AI-Assisted Screening of Oral Potentially Malignant Disorders Using Smartphone-Based Photographic Images}, BOOKTITLE = {Cancers}. YEAR = {2023}}

The prevalence of oral potentially malignant disorders (OPMDs) and oral cancer is surging in low- and middle-income countries. A lack of resources for population screening in remote locations delays the detection of these lesions in the early stages and contributes to higher mortality and a poor quality of life. Digital imaging and artificial intelligence (AI) are promising tools for cancer screening. This study aimed to evaluate the utility of AI-based techniques for detecting OPMDs in the Indian population using photographic images of oral cavities captured using a smartphone. A dataset comprising 1120 suspicious and 1058 non-suspicious oral cavity photographic images taken by trained front-line healthcare workers (FHWs) was used for evaluating the performance of different deep learning models based on convolution (DenseNets) and Transformer (Swin) architectures. The best- performing model was also tested on an additional independent test set comprising 440 photographic images taken by untrained FHWs (set I). DenseNet201 and Swin Transformer (base) models show high classification performance with an F1-score of 0.84 (CI 0.79–0.89) and 0.83 (CI 0.78–0.88) on the internal test set, respectively. However, the performance of models decreases on test set I, which has considerable variation in the image quality, with the best F1-score of 0.73 (CI 0.67–0.78) obtained using DenseNet201. The proposed AI model has the potential to identify suspicious and non-suspicious oral lesions using photographic images. This simplified image-based AI solution can assist in screening, early detection, and prompt referral for OPMDs.

@inproceedings{bib_Iden_2023, AUTHOR = {Srivastava, Akansha and Krishnanunni, Vinod Palakkad }, TITLE = {Identification and Characterization of Metabolic Subtypes of Endometrial Cancer Using a Systems-Level Approach }, BOOKTITLE = {Metabolites}. YEAR = {2023}}

Endometrial cancer (EC) is the most common gynecological cancer worldwide. Understanding metabolic adaptation and its heterogeneity in tumor tissues may provide new insights and help in cancer diagnosis, prognosis, and treatment. In this study, we investigated metabolic alterations of EC to understand the variations in metabolism within tumor samples. Integration of transcriptomics data of EC (RNA-Seq) and the human genome-scale metabolic network was performed to identify the metabolic subtypes of EC and uncover the underlying dysregulated metabolic pathways and reporter metabolites in each subtype. The relationship between metabolic subtypes and clinical variables was explored. Further, we correlated the metabolic changes occurring at the transcriptome level with the genomic alterations. Based on metabolic profile, EC patients were stratified into two subtypes (metabolic subtype-1 and subtype-2) that significantly correlated to patient survival, tumor stages, mutation, and copy number variations. We observed the co-activation of the pentose phosphate pathway, one-carbon metabolism, and genes involved in controlling estrogen levels in metabolic subtype-2, which is linked to poor survival. PNMT and ERBB2 are also upregulated in metabolic subtype-2 samples and present on the same chromosome locus 17q12, which is amplified. PTEN and TP53 mutations show mutually exclusive behavior between subtypes and display a difference in survival. This work identifies metabolic subtypes with distinct characteristics at the transcriptome and genome levels, highlighting the metabolic heterogeneity within EC.

@inproceedings{bib_MolG_2022, AUTHOR = {Bagal, Viraj and Aggarwal, Rishal and Krishnanunni, Vinod Palakkad and U, Deva Priyakumar }, TITLE = {MolGPT: Molecular Generation Using a Transformer-Decoder Model}, BOOKTITLE = {Journal of Chemical Information and Modeling}. YEAR = {2022}}

Application of deep learning techniques for de novo generation of molecules, termed as inverse molecular design, has been gaining enormous traction in drug design. The representation of molecules in SMILES notation as a string of characters enables the usage of state of the art models in natural language processing, such as Transformers, for molecular design in general. Inspired by generative pre-training (GPT) models that have been shown to be successful in generating meaningful text, we train a transformer-decoder on the next token prediction task using masked self-attention for the generation of druglike molecules in this study. We show that our model, MolGPT, performs on par with other previously proposed modern machine learning frameworks for molecular generation in terms of generating valid, unique, and novel molecules. Furthermore, we demonstrate that the model can be trained conditionally to