A5018639463- Bioinformatics and Genomic Networks
- Gene expression and cancer classification
- Statistical Methods and Inference
- Bayesian Methods and Mixture Models
- Gene Regulatory Network Analysis
- Cancer Genomics and Diagnostics
- Gaussian Processes and Bayesian Inference
- Neural Networks and Applications
- Healthcare professionals’ stress and burnout
- Physical Activity and Health
- Health disparities and outcomes
- COVID-19 and Mental Health
- Lung Cancer Treatments and Mutations
- COVID-19 Pandemic Impacts
- RNA modifications and cancer
- Ferroptosis and cancer prognosis
- Advanced Graph Neural Networks
- Stock Market Forecasting Methods
- Financial Risk and Volatility Modeling
- Single-cell and spatial transcriptomics
- Cardiac Health and Mental Health
- Monetary Policy and Economic Impact
- Energy Load and Power Forecasting
- Glutathione Transferases and Polymorphisms
- Hydrological Forecasting Using AI
Harvard University
2023-2025
University of South Carolina
2025
Burdwan Medical College & Hospital
2022
University of Chicago
2017-2021
European Society of Anaesthesiology
2020
Indian Statistical Institute
2016
Netaji Subhas University of Technology
2014
Abstract Inference and analysis of gene regulatory networks (GRNs) require software that integrates multi-omic data from various sources. The Network Zoo (netZoo; netzoo.github.io) is a collection open-source methods to infer GRNs, conduct differential network analyses, estimate community structure, explore the transitions between biological states. netZoo builds on our ongoing development methods, harmonizing implementations in computing languages allow better integration these tools into...
Lung adenocarcinoma (LUAD) has been observed to have significant sex differences in incidence, prognosis, and response therapy. However, the molecular mechanisms responsible for these disparities not investigated extensively.
Abstract Lung adenocarcinoma (LUAD) exhibits differences between the sexes in incidence, prognosis, and therapy, suggesting underexplored molecular mechanisms. We conducted an integrative multi-omics analysis using Clinical Proteomic Tumor Analysis Consortium (CPTAC) The Cancer Genome Atlas (TCGA) datasets to contrast transcriptomes proteomes sexes. used TIGER analyze TCGA-LUAD expression data found sex-biased activity of transcription factors (TFs); we PTM-SEA with CPTAC-LUAD proteomics...
Abstract Background: The rising incidence of lung cancer among individuals without a history smoking highlights the need to explore biological mechanisms underlying this phenomenon. Our study aims identify gene regulatory that drive risk never-smokers by analyzing how networks differ between with and cancer, depending on their history. Methods: We used RNA-Seq data from Lung Tissue Research Consortium (LTRC) collected via TOPMed, comprising non-cancerous tissue samples 344 non-small cell...
1 Abstract Computational methods in biology can infer large molecular interaction networks from multiple data sources and at different resolutions, creating unprecedented opportunities to explore the mechanisms driving complex biological phenomena. Networks be built represent distinct conditions compared uncover graph-level differences—such as when comparing patterns of gene-gene interactions that change between states. Given importance graph comparison problem, there is a clear growing need...
Compared to men, women often develop COPD at an earlier age with worse respiratory symptoms despite lower smoking exposure. However, most preventive, and therapeutic strategies ignore biological sex differences in COPD. Our goal was better understand sex-specific gene regulatory processes lung tissue the molecular basis for onset severity. We analyzed expression DNA methylation data from 747 individuals Lung Tissue Research Consortium (LTRC), 85 independent dataset. identified...
Lung adenocarcinoma (LUAD) has been observed to have significant sex differences in incidence, prognosis, and response therapy. However, the molecular mechanisms responsible for these disparities not investigated extensively. Sample-specific gene regulatory network methods were used analyze RNA sequencing data from non-cancerous human lung samples The Genotype Tissue Expression Project (GTEx) primary tumor Cancer Genome Atlas (TCGA); results validated on independent data. We observe that...
Abstract Sex differences in lung adenocarcinoma (LUAD) are evident incidence rates, prognostic outcomes, and therapy responses, yet the underlying molecular mechanisms driving these disparities remain underexplored. In this study, we conducted a comprehensive proteogenomic analysis encompassing 38 females 73 males with LUAD from Clinical Proteomic Tumor Analysis Consortium (CPTAC) dataset. Employing Transcription Inference using Gene Expression Regulatory data (TIGER), inferred...
Gene regulatory networks (GRNs) are effective tools for inferring complex interactions between molecules that regulate biological processes and hence can provide insights into drivers of systems. Inferring coexpression is a critical element GRN inference, as the correlation expression patterns may indicate genes coregulated by common factors. However, methods estimate generally derive an aggregate network representing mean properties population so fail to fully capture heterogeneity....
Abstract There is increasing recognition that the sex chromosomes, X and Y, play an important role in health disease goes beyond determination of biological sex. Loss Y chromosome (LOY) blood, which occurs naturally aging men, has been found to be a driver cardiac fibrosis heart failure mortality. LOY also most solid tumors males often associated with worse survival, suggesting may give tumor cells growth or survival advantage. We analyzed lung adenocarcinoma (LUAD) using both bulk...
Technological advances in sequencing and computation have allowed deep exploration of the molecular basis diseases. Biological networks proven to be a useful framework for interrogating omics data modeling regulatory gene protein interactions. Large collaborative projects, such as The Cancer Genome Atlas (TCGA), provided rich resource building validating new computational methods resulting plethora open-source software downloading, pre-processing, analyzing those data. However, an end-to-end...
Gene regulatory networks (GRNs) are effective tools for inferring complex interactions between molecules that regulate biological processes and hence can provide insights into drivers of systems. Inferring co-expression is a critical element GRN inference as the correlation expression patterns may indicate genes coregulated by common factors. However, methods estimate generally derive an aggregate network representing mean properties population so fail to fully capture heterogeneity. To...
The SARIMA time series model is fitted to the monthly average maximum and minimum temperature data sets collected at Giridih, India for years 1990-2011. From time-series plots, we observe that patterns of both are quite different; contain sharp peaks in almost all while it not true hence modeled separately (also sake simplicity). models selected based on observing autocorrelation function (ACF) partial (PACF) series. parameters obtained by using likelihood method with help three tests [i.e.,...
Background: COVID-19 pandemic is negatively affecting the mental health of medical professionals as well students, they stand in frontline. Medical education recognized stressful across globe and hour present crisis, students have to stay back home continue their studies online. Aims: The aim study compare emerging evidence effects outbreak on assess awareness other studying a College Eastern India. Materials Methods: This cross-sectional observational was conducted period 3 months after...
Its conceptual appeal and effectiveness has made latent factor modeling an indispensable tool for multivariate analysis. Despite its popularity across many fields, there are outstanding methodological challenges that have hampered practical deployments. One major challenge is the selection of number factors, which exacerbated dynamic models, where factors can disappear, emerge, and/or reoccur over time. Existing tools assume a fixed may provide misguided representation data mechanism,...
Aging is the primary risk factor for many individual cancer types, including lung adenocarcinoma (LUAD). To understand how aging-related alterations in regulation of key cellular processes might affect LUAD and survival outcomes, we built (person)-specific gene regulatory networks integrating expression, transcription protein-protein interaction, sequence motif data, using PANDA/LIONESS algorithms, both non-cancerous tissue samples from Genotype Tissue Expression (GTEx) project The Cancer...