{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/heter-lp-a-heterogeneous-label-propagation","title":"Heter-LP: A heterogeneous label propagation algorithm and its application in drug repositioning","arxiv_id":"1611.02945","date":"2016-11-08","proceeding":null,"authors":["Maryam Lotfi Shahreza","Nasser Ghadiri","Seyed Rasul Mossavi","Jaleh Varshosaz","James Green"],"abstract":"Drug repositioning offers an effective solution to drug discovery, saving\nboth time and resources by finding new indications for existing drugs.\nTypically, a drug takes effect via its protein targets in the cell. As a\nresult, it is necessary for drug development studies to conduct an\ninvestigation into the interrelationships of drugs, protein targets, and\ndiseases. Although previous studies have made a strong case for the\neffectiveness of integrative network-based methods for predicting these\ninterrelationships, little progress has been achieved in this regard within\ndrug repositioning research. Moreover, the interactions of new drugs and\ntargets (lacking any known targets and drugs, respectively) cannot be\naccurately predicted by most established methods. In this paper, we propose a\nnovel semi-supervised heterogeneous label propagation algorithm named Heter-LP,\nwhich applies both local as well as global network features for data\nintegration. To predict drug-target, disease-target, and drug-disease\nassociations, we use information about drugs, diseases, and targets as\ncollected from multiple sources at different levels. Our algorithm integrates\nthese various types of data into a heterogeneous network and implements a label\npropagation algorithm to find new interactions. Statistical analyses of 10-fold\ncross-validation results and experimental analysis support the effectiveness of\nthe proposed algorithm.","url_abs":"http://arxiv.org/abs/1611.02945v1","url_pdf":"http://arxiv.org/pdf/1611.02945v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"heter-lp-a-heterogeneous-label-propagation","repo_url":"https://github.com/dkrlab/Heter-LP-code","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"data-integration","task_name":"Data Integration"},{"task_slug":"drug-discovery","task_name":"Drug Discovery"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}