Biology subjects

Borole, P.

Publications and source records attributed to Borole, P..

3 recordsLinked to original sources

bioRxiv2025

Expanding the definition of MHC Class I peptide binding promiscuity to support vaccine discovery across cancers with CARMEN

Promiscuity in T-cell antigen landscapes refers to the dual flexibility of peptides binding multiple MHC alleles and MHC alleles presenting diverse arrays of peptides. By understanding how neoantigens are shared across varied HLA backgrounds, promiscuity analysis can inform the selection of cancer-vaccine targets that reach a wider segment of the population and help refine patient stratification for diverse immunotherapies. We expand the concept of promiscuity to encompass peptides, MHC alleles, individuals, populations, and genomic regions. Our CARMEN database release harmonizes data from 72 publications (2,323 samples) across tissue types, with a focus on cancer. Using Gibbs clustering and dimensionality reduction (UMAP), we systematically map promiscuity and immunological versatility across these biological levels. Gene and mutation analysis reveals recurrent cancer mutations in highly promiscuous genomic regions, highly mutated cancer genes that avoid presented regions, and sheds light on genomic regions important to response to immunotherapy.

bioinformatics↗

bioRxiv2025

Can interpretability and accuracy coexist in cancer survival analysis?

Survival analysis refers to statistical procedures used to analyze data that focuses on the time until an event occurs, such as death in cancer patients. Traditionally, the linear Cox Proportional Hazards (CPH) model is widely used due to its inherent interpretability. CPH model help identify key disease-associated factors (through feature weights), providing insights into patient risk of death. However, their reliance on linear assumptions limits their ability to capture the complex, non-linear relationships present in real-world data. To overcome this, more advanced models, such as neural networks, have been introduced, offering significantly improved predictive accuracy. However, these gains come at the expense of interpretability, which is essential for clinical trust and practical application. To address the trade-off between predictive accuracy and interpretability in survival analysis, we propose ConSurv, a concept bottleneck model that maintains state-of-the-art performance while providing transparent and interpretable insights. Using gene expression and clinical data from breast cancer patients, ConSurv captures complex feature interactions and predicts patient risk. By offering clear, biologically meaningful explanations for each prediction, ConSurv attempts to build trust among clinicians and researchers in using the model for informed decision-making.

bioinformatics↗

bioRxiv2023

Building Trust in Deep Learning-based Immune Response Predictors with Interpretable Explanations

The ability to predict whether a peptide will get presented on Major Histocompatibility Complex (MHC) class I molecules has profound implications in designing vaccines. Numerous deep learning-based predictors for peptide presentation on MHC class I molecules exist with high levels of accuracy. However, these MHC class I predictors are treated as black-box functions, providing little insight into their decision making. To build turst in these predictors, it is crucial to understand the rationale behind their decisions with human-interpretable explanations. We present MHCXAI, eXplainable AI (XAI) techniques to help interpret the outputs from MHC class I predictors in terms of input peptide features. In our experiments, we explain the outputs of four state-of-the-art MHC class I predictors over a large dataset of peptides and MHC alleles. Additionally, we evaluate the reliability of the explanations by comparing against ground truth and checking their robustness. MHCXAI seeks to increase understanding of deep learning-based predictors in the immune response domain and build trust with validated explanations

bioinformatics↗