arXiv subjects

Jeyakumar Natarajan

Publications and source records attributed to Jeyakumar Natarajan.

2 recordsLinked to original sources

arXiv2025

Unraveling the Biomarker Prospects of High-Altitude Diseases: Insights from Biomolecular Event Network Constructed using Text Mining

High-altitude diseases (HAD), encompassing acute mountain sickness (AMS), high-altitude cerebral edema (HACE), and high-altitude pulmonary edema (HAPE), are triggered by hypobaric hypoxia at elevations above 2,500 meters. These conditions pose significant health risks, yet the molecular mechanisms remain insufficiently understood. In this study, we developed a biomolecular event extraction pipeline integrating supervised machine learning with feature-based and multiscale Laplacian graph kernels to analyze 7,847 curated HAD-related abstracts from PubMed. We extracted over 150 unique biomolecular events including gene expression, regulation, binding, and localization and constructed a weighted, undirected biomolecular event network comprising 97 nodes and 153 edges. Using the PageRank algorithm, we prioritized key biomolecules based on their centrality within the event network. The top-ranked proteins included Erythropoietin (EPO) (0.0163), Vascular endothelial growth factor (VEGF) (0.0148), Hypoxia-inducible factor 1 (HIF-1) alpha (0.0136), Endothelial PAS Domain Protein 1 (EPAS1) and Angiotensin-Converting Enzyme (ACE) (0.0119), Egl nine homolog 1 (EGLN1), Endothelin 1 (ET-1), and 70 kilodalton heat shock protein (Hsp70)(0.0118), all of which play crucial roles in oxygen sensing, vascular remodeling, erythropoiesis, and blood pressure regulation. Subnetwork analysis revealed three major functional clusters centered on hypoxia response, inflammation, and stress adaptation pathways. Our integrative approach demonstrates the utility of large-scale text mining and graph-based analysis to uncover mechanistic insights and prioritize potential biomarkers for high-altitude disease.

cs.IR↗

arXiv2023

Literature Based Discovery (LBD): Towards Hypothesis Generation and Knowledge Discovery in Biomedical Text Mining

Biomedical knowledge is growing in an astounding pace with a majority of this knowledge is represented as scientific publications. Text mining tools and methods represents automatic approaches for extracting hidden patterns and trends from this semi structured and unstructured data. In Biomedical Text mining, Literature Based Discovery (LBD) is the process of automatically discovering novel associations between medical terms otherwise mentioned in disjoint literature sets. LBD approaches proven to be successfully reducing the discovery time of potential associations that are hidden in the vast amount of scientific literature. The process focuses on creating concept profiles for medical terms such as a disease or symptom and connecting it with a drug and treatment based on the statistical significance of the shared profiles. This knowledge discovery approach introduced in 1989 still remains as a core task in text mining. Currently the ABC principle based two approaches namely open discovery and closed discovery are mostly explored in LBD process. This review starts with general introduction about text mining followed by biomedical text mining and introduces various literature resources such as MEDLINE, UMLS, MESH, and SemMedDB. This is followed by brief introduction of the core ABC principle and its associated two approaches open discovery and closed discovery in LBD process. This review also discusses the deep learning applications in LBD by reviewing the role of transformer models and neural networks based LBD models and its future aspects. Finally, reviews the key biomedical discoveries generated through LBD approaches in biomedicine and conclude with the current limitations and future directions of LBD.

cs.IR↗