NDLI: Protein features fusion using attributed network embedding for predicting protein-protein interaction

Content Provider	Springer Nature : BioMed Central
Author	Cao, Mei-Yuan Zainudin, Suhaila Daud, Kauthar Mohd
Abstract	Background Protein-protein interactions (PPIs) hold significant importance in biology, with precise PPI prediction as a pivotal factor in comprehending cellular processes and facilitating drug design. However, experimental determination of PPIs is laborious, time-consuming, and often constrained by technical limitations. Methods We introduce a new node representation method based on initial information fusion, called FFANE, which amalgamates PPI networks and protein sequence data to enhance the precision of PPIs’ prediction. A Gaussian kernel similarity matrix is initially established by leveraging protein structural resemblances. Concurrently, protein sequence similarities are gauged using the Levenshtein distance, enabling the capture of diverse protein attributes. Subsequently, to construct an initial information matrix, these two feature matrices are merged by employing weighted fusion to achieve an organic amalgamation of structural and sequence details. To gain a more profound understanding of the amalgamated features, a Stacked Autoencoder (SAE) is employed for encoding learning, thereby yielding more representative feature representations. Ultimately, classification models are trained to predict PPIs by using the well-learned fusion feature. Results When employing 5-fold cross-validation experiments on SVM, our proposed method achieved average accuracies of 94.28%, 97.69%, and 84.05% in terms of Saccharomyces cerevisiae, Homo sapiens, and Helicobacter pylori datasets, respectively. Conclusion Experimental findings across various authentic datasets validate the efficacy and superiority of this fusion feature representation approach, underscoring its potential value in bioinformatics.
Related Links	https://bmcgenomics.biomedcentral.com/counter/pdf/10.1186/s12864-024-10361-8.pdf
Ending Page	15
Page Count	15
Starting Page	1
File Format	HTM / HTML
ISSN	14712164
DOI	10.1186/s12864-024-10361-8
Journal	BMC Genomics
Issue Number	1
Volume Number	25
Language	English
Publisher	BioMed Central
Publisher Date	2024-05-13
Access Restriction	Open
Subject Keyword	Life Sciences Microarrays Proteomics Animal Genetics and Genomics Microbial Genetics and Genomics Plant Genetics and Genomics Protein-protein interaction prediction Protein sequences Feature fusion learning Gaussian kernel Levenshtein distance
Content Type	Text
Resource Type	Article
Subject	Biotechnology Genetics
Journal Impact Factor	3.5/2023
5-Year Journal Impact Factor	4.1/2023

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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