{"title":"Identification of Proteins and Genes Associated with Hedgehog Signaling Pathway Involved in Neoplasm Formation Using Text-Mining Approach","authors":"","doi":"10.26599/BDMA.2023.9020007","DOIUrl":null,"url":null,"abstract":"Analysis of molecular mechanisms that lead to the development of various types of tumors is essential for biology and medicine, because it may help to find new therapeutic opportunities for cancer treatment and cure including personalized treatment approaches. One of the pathways known to be important for the development of neoplastic diseases and pathological processes is the Hedgehog signaling pathway that normally controls human embryonic development. Systematic accumulation of various types of biological data, including interactions between proteins, regulation of genes transcription, proteomics, and metabolomics experiments results, allows the application of computational analysis of these big data for identification of key molecular mechanisms of certain diseases and pathologies and promising therapeutic targets. The aim of this study is to develop a computational approach for revealing associations between human proteins and genes interacting with the Hedgehog pathway components, as well as for identifying their roles in the development of various types of tumors. We automatically collect sets of abstract texts from the NCBI PubMed bibliographic database. For recognition of the Hedgehog pathway proteins and genes and neoplastic diseases we use a dictionary-based named entity recognition approach, while for all other proteins and genes machine learning method is used. For association extraction, we develop a set of semantic rules. We complete the results of the text analysis with the gene set enrichment analysis. The identified key pathways that may influence the Hedgehog pathway and their roles in tumor development are then verified using the information in the literature.","PeriodicalId":52355,"journal":{"name":"Big Data Mining and Analytics","volume":null,"pages":null},"PeriodicalIF":7.7000,"publicationDate":"2023-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10373000","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Big Data Mining and Analytics","FirstCategoryId":"1093","ListUrlMain":"https://ieeexplore.ieee.org/document/10373000/","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Analysis of molecular mechanisms that lead to the development of various types of tumors is essential for biology and medicine, because it may help to find new therapeutic opportunities for cancer treatment and cure including personalized treatment approaches. One of the pathways known to be important for the development of neoplastic diseases and pathological processes is the Hedgehog signaling pathway that normally controls human embryonic development. Systematic accumulation of various types of biological data, including interactions between proteins, regulation of genes transcription, proteomics, and metabolomics experiments results, allows the application of computational analysis of these big data for identification of key molecular mechanisms of certain diseases and pathologies and promising therapeutic targets. The aim of this study is to develop a computational approach for revealing associations between human proteins and genes interacting with the Hedgehog pathway components, as well as for identifying their roles in the development of various types of tumors. We automatically collect sets of abstract texts from the NCBI PubMed bibliographic database. For recognition of the Hedgehog pathway proteins and genes and neoplastic diseases we use a dictionary-based named entity recognition approach, while for all other proteins and genes machine learning method is used. For association extraction, we develop a set of semantic rules. We complete the results of the text analysis with the gene set enrichment analysis. The identified key pathways that may influence the Hedgehog pathway and their roles in tumor development are then verified using the information in the literature.
期刊介绍:
Big Data Mining and Analytics, a publication by Tsinghua University Press, presents groundbreaking research in the field of big data research and its applications. This comprehensive book delves into the exploration and analysis of vast amounts of data from diverse sources to uncover hidden patterns, correlations, insights, and knowledge.
Featuring the latest developments, research issues, and solutions, this book offers valuable insights into the world of big data. It provides a deep understanding of data mining techniques, data analytics, and their practical applications.
Big Data Mining and Analytics has gained significant recognition and is indexed and abstracted in esteemed platforms such as ESCI, EI, Scopus, DBLP Computer Science, Google Scholar, INSPEC, CSCD, DOAJ, CNKI, and more.
With its wealth of information and its ability to transform the way we perceive and utilize data, this book is a must-read for researchers, professionals, and anyone interested in the field of big data analytics.