Biology subjects

Bharadhwaj, V. S.

Publications and source records attributed to Bharadhwaj, V. S..

5 recordsLinked to original sources

bioRxiv2025

Unraveling the Co-Morbidity between COVID-19 and Neurodegenerative Diseases Through Multi-scale Graph Analysis: A Systematic Investigation of Biological Databases and Text Mining

The COVID-19 pandemic has generated a vast volume of research, yet much of it focuses on individual diseases, overlooking complex comorbidity relationships. While extensive literature exists on both neurodegenerative diseases (NDDs), such as Alzheimers and Parkinsons, and COVID-19, their intersection remains underexplored. Co-morbidity modeling is crucial, particularly for hospitalized patients often presenting with multiple conditions. This study investigates the interplay between COVID-19 and NDDs by integrating knowledge graphs (KGs) built from curated biomedical datasets and text mining tools. We performed comprehensive analyses--including path analysis, phenotype coverage, and mapping of cellular and genetic factors--across multiple KGs, such as PrimeKG, DrugBank, OpenTargets, and those generated via natural language processing (NLP) methods. Our findings reveal notable variability in graph density and connectivity, with each KG offering unique insights into molecular and phenotypic links between COVID-19 and NDDs. Key genetic and inflammatory markers, especially immune response genes, consistently appeared across graphs, suggesting a shared pathogenic basis. By unifying structured biological data with unstructured textual evidence, we enhance co-morbidity modeling and improve recall in identifying mechanisms underlying COVID-19- NDD interactions. This integrative framework supports the development of a co-morbidity hypothesis database aimed at facilitating therapeutic target discovery. All data, methods, and instructions for accessing the co-morbidity hypothesis database are publicly available at: https://github.com/SCAI-BIO/covid-NDD-comorbidity-NLP.

bioinformatics↗

bioRxiv2024

SynDRep: A Knowledge Graph-Enhanced Tool based on Synergistic Partner Prediction for Drug Repurposing

MotivationDrug repurposing is gaining interest due to its high cost-effectiveness, low risks, and improved patient outcomes. However, most drug repurposing methods depend on drug-disease-target semantic connections of a single drug rather than insights from drug combination data. In this study, we propose SynDRep, a novel drug repurposing tool based on enriching knowledge graphs (KG) with drug combination effects. It predicts the synergistic drug partner with a commonly prescribed drug for the target disease, leveraging graph embedding and machine learning techniques. This partner drug is then repurposed as a single agent for this disease by exploring pathways between them in KG. ResultsHolE was the best-performing embedding model (with 84.58% of true predictions for all relations), and random forest emerged as the best ML model with an ROC-AUC value of 0.796. Some of our selected candidates, such as miconazole and albendazole for Alzheimers disease, have been validated through literature, while others lack either a clear pathway or literature evidence for their use for the disease of interest. Therefore, complementing SynDRep with more specialized KG, and additional training data, would enhance its efficacy and offer cost-effective and timely solutions for patients. Availability and ImplementationSynDRep is available as an open-source Python package at https://github.com/SynDRep/SynDRep under the Apache 2.0 License.

bioinformatics↗

bioRxiv2021

DecoPath: A web application for decoding pathway enrichment analysis

The past two decades have brought a steady growth of pathway databases and pathway enrichment methods. However, the advent of pathway data has not been accompanied by an improvement with regards to interoperability across databases, thus, hampering the use of pathway knowledge from multiple databases for pathway enrichment analyses. While integrative databases have attempted to address this issue by collating pathway knowledge from multiple resources, these approaches do not account for redundant information across them. On the other hand, the majority of studies that employ pathway enrichment analyses still rely upon a single database, though the use of another resource could yield differing results, which is similarly the case when different pathway enrichment methods are employed. These shortcomings call for approaches that investigate the differences and agreements across databases and enrichment methods as their selection in the experimental design of a pathway analysis can be a crucial first step in ensuring the results of such an analysis are meaningful. Here we present DecoPath, a web application to assist in the interpretation of the results of pathway enrichment analysis. DecoPath provides an ecosystem to run pathway enrichment analysis or directly upload results and facilitate the interpretation of these results with custom visualizations that highlight the consensus and/or discrepancies at the pathway- and gene-levels. DecoPath is available at https://decopath.scai.fraunhofer.de and its source code and documentation can be found on GitHub at https://github.com/DecoPath/DecoPath.

bioinformatics↗

bioRxiv2020

Using predictive machine learning models for drug response simulation by calibrating patient-specific pathway signatures

The utility of pathway signatures lies in their capability to determine whether a specific pathway or biological process is dysregulated in a given patient. These signatures have been widely used in machine learning (ML) methods for a variety of applications including precision medicine, drug repurposing, and drug discovery. In this work, we leverage highly predictive ML models for drug response simulation in individual patients by calibrating the pathway activity scores of disease samples. Using these ML models and a novel scoring algorithm to modify the signatures of patients, we evaluate whether a given sample that was formerly classified as diseased, could be predicted as normal following drug treatment simulation. We then use this technique as a proxy for the identification of potential drug candidates. Furthermore, we demonstrate the ability of our methodology to successfully identify approved and clinically investigated drugs for three different cancers. We also show how this approach can deconvolute a drugs mechanism of action and propose combination therapies. Taken together, our methodology could be promising to support clinical decision-making in personalized medicine by simulating a drugs effect on a given patient.

bioinformatics↗

bioRxiv2020

CLEP: A Hybrid Data- and Knowledge- Driven Framework for Generating Patient Representations

As machine learning and artificial intelligence become more useful in the interpretation of biomedical data, their utility depends on the data used to train them. Due to the complexity and high dimensionality of biomedical data, there is a need for approaches that combine prior knowledge around known biological interactions with patient data. Here, we present CLEP, a novel approach that generates new patient representations by leveraging both prior knowledge and patient-level data. First, given a patient-level dataset and a knowledge graph containing relations across features that can be mapped to the dataset, CLEP incorporates patients into the knowledge graph as new nodes connected to their most characteristic features. Next, CLEP employs knowledge graph embedding models to generate new patient representations that can ultimately be used for a variety of downstream tasks, ranging from clustering to classification. We demonstrate how using new patient representations generated by CLEP significantly improves performance in classifying between patients and healthy controls for a variety of machine learning models, as compared to the use of the original transcriptomics data. Furthermore, we also show how incorporating patients into a knowledge graph can foster the interpretation and identification of biological features characteristic of a specific disease or patient subgroup. Finally, we released CLEP as an open source Python package together with examples and documentation.

bioinformatics↗