Sabyasachi Patra

Scopus Publications

A systematic review on efficient and scalable algorithms for finding protein complexes in protein-protein interaction networks
Sabyasachi Patra, Tushar Ranjan Sahoo, Swati Vipsita, Anjali Mohapatra
Network Modeling Analysis in Health Informatics and Bioinformatics, 2026
Efficient and Secure Blockchain Based IoT e-Healthcare System
Madhu Chouhan, Sabyasachi Patra, Rakesh Kumar Lenka
SN Computer Science, 2026
A Review on Efficient and Scalable Graph-Based Clustering Algorithms for Protein Complex Identification in PPI Networks
Sabyasachi Patra, Tushar Ranjan Sahoo
Proteins Structure Function and Bioinformatics, 2026
Network clustering is employed in bioinformatics and data mining studies to investigate the structural and functional properties of protein–protein interaction (PPI) networks. In multiple studies over the past two decades, network clustering has proven valuable for uncovering functional modules and elucidating the functions of previously undiscovered proteins. Protein complexes are vital cellular components that play a crucial role in generating biological activity. Experimental techniques have inherent limitations in inferring protein complexes. Given these constraints, numerous computational methods have emerged over the past decade for predicting protein complexes. Typically, these methods take the input PPI data and generate predicted protein complexes as output subnetworks. Most of these methods have shown encouraging outcomes in predicting protein complexes. Prediction is challenging for sparse, small, and overlapping complexes. New strategies should include explicit knowledge about the biological characteristics of proteins to increase performance. Furthermore, specific issues should be considered more effectively in the future while developing new complex prediction algorithms. The bioinformatics community has developed various techniques for clustering PPI networks, which we identified, analyzed, and compared in this paper. This review evaluates various graph clustering algorithms for protein complex identification, facilitating the benchmarking of existing methods, identifying limitations, motivating the development of novel computational tools, and ultimately improving biological insight and therapeutic progress. Through the assessment of strengths and limitations, researchers may develop efficient and scalable algorithms designed explicitly for biological data, integrating graph‐based methodologies with machine learning and deep learning approaches. This study is an invaluable tool for new researchers in the area to recognize upcoming trends, including dynamic PPI networks and temporal complex identification.
An Optimized Adaptive Feature Weighting Framework for Accurate Prediction of Cardiovascular Disease
Subham Kumar Padhy, Anjali Mohapatra, Sabyasachi Patra
SN Computer Science, 2026
Prediction of Heart Disease Based on Weighted Combination of Binary Classifier
Aditya Mishra, Sabyasachi Patra
Communications in Computer and Information Science, 2026
Heart Sound Classification Using Ensemble-Learning and Multi-Feature Representations
Subhra Mohanty, Sabyasachi Patra
SN Computer Science, 2026
X-CBNet: An Explainable Effective Deep Learning Framework Based on Spectrograms for Predicting Valvular Disorder using PCG Signals
Subham Kumar Padhy, Anjali Mohapatra, Sabyasachi Patra
Journal of Transformative Technologies and Sustainable Development, 2025
Valvular Heart Disease (VHD), caused by malfunctioning heart valves, poses significant diagnostic challenges due to the complexity of heart sound patterns and variability in clinical presentations. Traditional auscultation methods are subjective, and existing automated models often function as black boxes, offering limited insight into the reasoning behind predictions. Therefore, there is a pressing need for accurate, interpretable, and multi-class diagnostic tools to aid clinicians in early and reliable detection of VHD using phonocardiogram (PCG) signals. X-CBNet, a convolutional neural network (CNN) followed by a bidirectional long short-term memory (Bi-LSTM) network framework, is employed in this research to harness deep learning’s capability to achieve high diagnostic accuracy while ensuring interpretability through explainable AI methods. Melspectrograms are used to capture essential features of the phonocardiograms. The proposed model is designed as a five-class classifier distinguishing between aortic stenosis, mitral stenosis, mitral regurgitation, mitral valve prolapse, and normal heart sounds. Gradient-weighted class activation mapping (Grad-CAM) is utilized for explainability, generating heatmaps that visualize model decision-making. The proposed X-CBNet model achieved an overall accuracy of 99.15%. Per-class accuracies are: Normal 99.15%, Aortic Stenosis 98.43%, Mitral Regurgitation 98.79%, Mitral Stenosis 99.58%, and Mitral Valve Prolapse 99.80%. The model also achieved a macro-average AUC of 0.99 and a micro-average AUC of 0.98, while maintaining a misclassification rate of less than 1%. The Grad-CAM heatmaps further validate model decisions by highlighting class-discriminative features. The robust performance demonstrates that the proposed model is suitable for real-time clinical applications, offering enhanced transparency through the integration of explainable AI.
A lightweight efficientNetB3 explainable model for enhancing prediction of cardiac arrhythmia using ECG signals
Subham Kumar Padhy, Anjali Mohapatra, Sabyasachi Patra
Network Modeling Analysis in Health Informatics and Bioinformatics, 2025
WE-XAI: explainable AI for CVD prediction using weighted feature selection and ensemble classifiers
Subham Kumar Padhy, Anjali Mohapatra, Sabyasachi Patra
Network Modeling Analysis in Health Informatics and Bioinformatics, 2025
Integration of Multi-feature Analysis with Lightweight CNN Model for Heart Sound Classification
Subhra Mohanty, Sabyasachi Patra
Communications in Computer and Information Science, 2025
Enhanced Hybrid Framework for Detection of Cardiovascular Disease Based on Adaptive Neural Network and Fuzzy Inference System
Subham Kumar Padhy, Anjali Mohapatra, Sabyasachi Patra
Procedia Computer Science, 2025
Classification of Cardiovascular Disease Information System Using Machine Learning Approaches
Subham Kumar Padhy, Anjali Mohapatra, Sabyasachi Patra
Studies in Computational Intelligence, 2024
Centrality Measures and Their Applications in Network Analysis: Unveiling Important Elements and Their Impact
Trilochan Rout, Anjali Mohapatra, Madhabananda Kar, Sabyasachi Patra, Dillip Muduly
Procedia Computer Science, 2024
Decision tree classifier based on topological characteristics of subgraph for the mining of protein complexes from large scale PPI networks
Tushar Ranjan Sahoo, Sabyasachi Patra, Swati Vipsita
Computational Biology and Chemistry, 2023
Complex Prediction in Large PPI Networks Using Expansion and Stripe of Core Cliques
Tushar Ranjan Sahoo, Swati Vipsita, Sabyasachi Patra
Interdisciplinary Sciences Computational Life Sciences, 2023
Heart Sound Classification Based on MFCC Feature Extraction and Long-Short Term Neural Networks
Subham Kumar Padhy, Anjali Mohapatra, Sabyasachi Patra
2023 2nd International Conference on Ambient Intelligence in Health Care Icaihc 2023, 2023
Analysis of Deep Learning Approaches to Predict Cardiac Arrhythmia in ECG Signals
Subham Kumar Padhy, Saurav Mahalik, Himanshu Nayak, Sabyasachi Patra, Anjali Mohapatra
IEEE Region 10 Humanitarian Technology Conference R10 Htc, 2023
Performance evaluation of priority Queueing assisted IoST-Fog-Blockchain framework in Geospatial Cloud Environment
Soubhagya Ranjan Mallick, Veena Goswami, Rakesh Kumar Lenka, Sabyasachi Patra, Vinay Kumar, Rabindra K. Barik
2nd International Conference on Microwave Optical and Communication Engineering Icmoce 2023, 2023
Protein complex prediction based on dense sub-graph merging
Tushar Ranjan Sahoo, Swati Vipsita, Sabyasachi Patra
International Journal of Data Mining and Bioinformatics, 2022
Discovery of network motifs based on induced subgraphs using a dynamic expansion tree
Sabyasachi Patra
Computational Biology and Chemistry, 2021
Protein complex prediction in interaction network based on network motif
Sabyasachi Patra, Anjali Mohapatra
Computational Biology and Chemistry, 2020
TriRNSC: Triclustering of gene expression microarray data using restricted neighbourhood search
Bhawani Sankar Biswal, Sabyasachi Patra, Anjali Mohapatra, Swati Vipsita
Iet Systems Biology, 2020
Review of tools and algorithms for network motif discovery in biological networks
Sabyasachi Patra, Anjali Mohapatra
Iet Systems Biology, 2020
Detection of intent-matched questions using machine learning and deep learning techniques
Srikant Kumar, Anjali Mohapatra, Sabyasachi Patra, Sunakshi Mamgain
Proceedings 2019 International Conference on Information Technology Icit 2019, 2019
Disjoint motif discovery in biological network using pattern join method
Sabyasachi Patra, Anjali Mohapatra
Iet Systems Biology, 2019
Application of dynamic expansion tree for finding large network motifs in biological networks
Sabyasachi Patra, Anjali Mohapatra
Peerj, 2019
Motif discovery in biological network using expansion tree
Sabyasachi Patra, Anjali Mohapatra
Journal of Bioinformatics and Computational Biology, 2018
Clustering of proteins in interaction networks based on motif features
Sabyasachi Patra, Anjali Mohapatra
International Conference on Bioinformatics and Systems Biology Bsb 2018, 2018
Dimension reduction of feature vectors using WPCA for robust speaker identification system
Sabyasachi Patra, Subhendu Kumar Acharya
International Conference on Recent Trends in Information Technology Icrtit 2011, 2011