[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121390-en":3,"doc-seo-121390-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121390,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","HybridPPI - A Hybrid Machine Learning Framework for Protein-Protein Interaction Prediction","Protein-protein interactions (PPIs) play a central role in cellular functions and disease mechanisms, making reliable PPI prediction critical for drug discovery and systems biology. Experimental assays are costly and prone to false positives and negatives, while existing computational approaches often depend on unimodal features or limited model classes, reducing generalization. HybridPPI addresses these gaps by integrating multi-view features from sequence, structure, and network sources and combining SVM, RF, CNN, and LSTM via stacking with gradient boosting, achieving stronger predictive performance and robustness.","HybridPPI: A Hybrid Machine Learning Framework for Protein-Protein Interaction Prediction  \n1Desidi Narsimha Reddy, 2Dr Pinagadi Venkateswararao, 3Dr M. Sree Vani, 4Vodapelli Pranathi, 5Dr Anitha Patil  \nData Consultant (Data Governance, Data Analytics, EPM: enterprise performance management, AI&ML) Soniks  \nConsulting LLC, USA.  \nComputer Science and Engineering, CVR College of Engineering Hyderabad Department ofCSE, Bvrit Hyderabad College of Engineering for Women, Hyderabad Department ofCSE, KL University, Hyderabad, Telangana, India  \nArticle history:  \nReceived Jan 23, 2025 Revised Apr 10, 2025 Accepted May 10, 2025  \nKeyword:  \nProtein-Protein Interaction, Hybrid Machine Learning, Ensemble Learning, MultiView Feature Integration, PPI Prediction  \nCorresponding Author:  \nProtein-protein interactions (PPIs) are key to cellular functions and disease mechanisms and are crucial for drug discovery and systems biology. Though experimental approaches, including yeast two-hybrid systems, provide informative discoveries, they are time-consuming, costly, and frequently yield significant false-positive rates. Newer computational tools, including DeepPPI and PIPR, have demonstrated their potential, but their reliance on single-modal features or specific machine-learning models limits their generalization and robustness. These limitations highlight the need for an enhanced framework that assimilates different types of features while integrating a diverse array of machine learning models to exploit the strengths offered by each model class. In this paper, we present a hybrid machine learning framework, HybridPPI, to effectively incorporate the power of sequence-based, structure-based, and network-based features based on wellknown ensemble learning techniques to predict PPIs. Our proposed algorithm is a stacking ensemble of multiple models (Support Vector Machines (SVM), Random Forest (RF), Convolutional Neural Networks (CNN), and Long Short-Term Memory Networks (LSTM)), with Gradient Boosting as the metamodel. Results show that HybridPPI (94.5% accuracy, 95.2% precision, and Area Under Curve of 0.97) outperforms the most advanced methods, indicating its robustness for PPI prediction. This scalable and generalizable framework can accommodate various biological applications. HybridPPI overcomes significant shortcomings of current methodologies and contributes to biological discovery.  \nCopyright © 2025 Institute of Advanced Engineering and Science.  \nAll rights reserved.  \nDesidi Narsimha Reddy,  \nData Consultant (Data Governance, Data Analytics, EPM: enterprise performance management, AI&ML) Soniks Consulting LLC, USA  \nEmail: [dn.narsimha@gmail.com](dn.narsimha@gmail.com)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nProtein-protein interactions (PPIs) have become essential for studying almost any field of cellular processes, with applications spanning from drug discovery to elucidating disease mechanisms and systems biology. With the birth of large-scale genomic data, the precision prediction of PPIs has become more significant than ever. On the contrary, classical experimental approaches (e.g., yeast two-hybrid systems and affinity purification) are expensive, time-consuming, and have high false positive and negative rates. This, in turn, has sparked the evolution of computational methods using machine and deep learning techniques to solve the scalability and accuracy issues.  \nRecent work with machine learning in predicting protein-protein interactions (PPIs) has explored the utility of sequence and structure features as inputs using the DeepPPI [1] and PIPR [2] datasets. Nevertheless, these methods heavily depend on unimodal features or particular models, hindering the generalization of diverse datasets. In addition, the multi-view features, including sequence, structure, and network properties, are still hardly integrated into a unified model for PPI prediction, which is another significant limitation in predictive modeling. We need","cbCaisU4k7D4A1nl","https://ap.wps.com/l/cbCaisU4k7D4A1nl","pdf",888913,1,12,"English","en",105,"# Introduction\n## Motivation and limitations of existing experimental and computational methods\n## Objectives of the HybridPPI framework\n# Related Work\n## Challenges and research gaps\n# Proposed Methodology\n## Multi-view feature integration pipeline\n## Feature selection strategy\n## Stacking ensemble design and model components","[{\"question\":\"Why are PPIs important in biological research?\",\"answer\":\"PPIs are essential for understanding cellular processes, disease mechanisms, and for advancing drug discovery and systems biology analyses.\"},{\"question\":\"What limitations do existing methods have for PPI prediction?\",\"answer\":\"Many computational approaches rely on unimodal features or specific machine-learning models, which restrict generalization across diverse datasets and reduce robustness.\"},{\"question\":\"How does HybridPPI improve prediction performance?\",\"answer\":\"HybridPPI fuses sequence-, structure-, and network-based features and uses a stacking ensemble combining SVM, Random Forest, CNN, and LSTM with gradient boosting as the metamodel.\"}]","HybridPPI - A Hybrid Machine Learning Framework for Protein-Protein Interaction Prediction | PDF",1785735436,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"hybridppi-a-hybrid-machine-learning-framework-for-protein-protein-interaction-prediction","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/hybridppi-a-hybrid-machine-learning-framework-for-protein-protein-interaction-prediction/121390/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why are PPIs important in biological research?","Question",{"text":75,"@type":76},"PPIs are essential for understanding cellular processes, disease mechanisms, and for advancing drug discovery and systems biology analyses.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What limitations do existing methods have for PPI prediction?",{"text":80,"@type":76},"Many computational approaches rely on unimodal features or specific machine-learning models, which restrict generalization across diverse datasets and reduce robustness.",{"name":82,"@type":73,"acceptedAnswer":83},"How does HybridPPI improve prediction performance?",{"text":84,"@type":76},"HybridPPI fuses sequence-, structure-, and network-based features and uses a stacking ensemble combining SVM, Random Forest, CNN, and LSTM with gradient boosting as the metamodel.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]