RETRACTED ARTICLE: Medical image analysis of phosphorylated protein interaction extraction algorithm based on text mining technology
Authors: 
Xiaolei Ma, Yang Lu, Yinan Lu, Zhili PeiAuthors Info & Claims
Pages 10551 - 10579
Abstract
Medical images have been widely used in the analysis of phosphorylated protein interactions, Medical image segmentation separates different regions of a particular meaning in an image. These regions make each region that does not intersect with each other satisfy the consistency of a particular region. In bioinformatics, measurement on protein interaction is one of the key points, which plays a very important role in understanding various biological processes and in the treatment and diagnosis of diseases. The physiological functions of organisms are mainly regulated by proteins in cells, so studying the interaction between proteins becomes the basis for understanding life activities. Using text mining methods to study protein interactions can make full use of the vast literature available to reveal potential knowledge associated with proteins and construct protein interaction networks. Therefore, this paper proposes a method for automatically extracting protein interaction pairs from the literature by studying the extraction principle of protein interactions and their interaction words. At the same time, the method of protein interaction relationship mining is developed. The research and the results obtained in this paper have certain reference value for the study of protein relations in the future.
References
[1]
Atkinson-Abutridy J, Mellish C, and Aitken S Combining information extraction with genetic algorithms for text mining IEEE Intell Syst 2017 19 3 22-30
[2]
Basit AH, Abbasi WA, Asif A, et al. Training large margin host-pathogen protein-protein interaction predictors J Bioinforma Comput Biol 2018 19 4 1850014 18 pages
[3]
Benson DA, Karsch-Mizrachi I, Lipman DJ, et al. GenBank: update Nucleic Acids Res 2004 32 1 D23-D26
[4]
Damanhuri NS, Docherty PD, Chiew YS, et al. A Patient-Specific Airway Branching Model for Mechanically Ventilated Patients Computational & Mathematical Methods in Medicine 2014 2014 2 645732
[5]
Damanhuri NS, Docherty PD, Chiew YS, et al. A Patient-Specific Airway Branching Model for Mechanically Ventilated Patients Computational & Mathematical Methods in Medicine 2014 2014 2 645732
[6]
Guo X, Qin Y, Zheng K, et al. Improved glomerular filtration rate estimation using new equations combined with standardized cystatin C and creatinine in Chinese adult chronic kidney disease patients Clin Biochem 2014 47 13–14 1220-1226
[7]
Hayashi T, Matsuzaki Y, Yanagisawa K, et al. MEGADOCK-Web: an integrated database of high-throughput structure-based protein-protein interaction predictions BMC Bioinformatics 2018 19 S4 62
[8]
Hu Y, Vinayagam A, Nand A, et al. Molecular Interaction Search Tool (MIST): an integrated resource for mining gene and protein interaction data Nucleic Acids Res 2018 46 Database issue D567-D574
[9]
Huang L, Liao L, and Wu CH Inference of protein-protein interaction networks from multiple heterogeneous data EURASIP J Bioinforma Syst Biol 2016 2016 1 8
[10]
Huang CC and Lu Z Community challenges in biomedical text mining over 10 years: success, failure and the future Brief Bioinform 2016 17 1 132-144
[11]
Kaur J, Gupta V (2013) Performance Analysis of Effective Japanese and Chinese Question Answering System. International Journal of Engineering Trends & Technology 4(4)
[12]
Krallinger M, Leitner F, Vazquez M, et al. How to link ontologies and protein-protein interactions to literature: text-mining approaches and the BioCreative experience Database 2012 2012 bas017-bas017
[13]
Kreuzthaler M, Antonio Miñarro-Giménez J, and Schulz S MapReduce in the Cloud: A Use Case Study for Efficient Co-Occurrence Processing of MEDLINE Annotations with MeSH Stud Health Technol Inform 2016 228 582-586
[14]
Lei H, Li L, and Wu CH Inference of protein-protein interaction networks from multiple heterogeneous data EURASIP J Bioinforma Syst Biol 2016 2016 1 8
[15]
Miller CM, Rindflesch TC, Fiszman M, et al. A Closed Literature-Based Discovery Technique Finds a Mechanistic Link Between Hypogonadism and Diminished Sleep Quality in Aging Men SLEEP 2012 35 2 279-285
[16]
Mora A, Michalickova K, and Donaldson IM A survey of protein interaction data and multigenic inherited disorders BMC Bioinformatics 2013 14 1 1-19
[17]
Moraes F and Góes A A decade of human genome project conclusion: Scientific diffusion about our genome knowledge Biochemistry & Molecular Biology Education a Bimonthly Publication of the International Union of Biochemistry & Molecular Biology 2016 44 3 215-223
[18]
Nguyen DT, Mathias S, Bologa C, et al. Pharos: Collating protein information to shed light on the druggable genome Nucleic Acids Res 2016 45 D1 D995
[19]
Raja K, Subramani S, and Natarajan J PPInterFinder—a mining tool for extracting causal relations on human proteins from literature Database 2013 2013 bas052
[20]
Raja K, Subramani S, and Natarajan J A hybrid named entity tagger for tagging human proteins/genes International Journal of Data Mining and Bioinformatics 2014 10 3 315-328
[21]
Ramos RP, Nascimento MZD, and Pereira DC Texture extraction: An evaluation of ridgelet, wavelet and co-occurrence based methods applied to mammograms Expert Syst Appl 2012 39 12 11036-11047
[22]
Ravikumar KE, Wagholikar KB, Li D, et al. Text mining facilitates database curation - extraction of mutation-disease associations from Bio-medical literature BMC Bioinformatics 2015 17 1 1
[23]
Salgado D, Krallinger M, Depaule M, et al. MyMiner: a web application for computer-assisted biocuration and text annotation Bioinformatics 2012 28 17 2285-2287
[24]
Selkoe DJ and Hardy J The amyloid hypothesis of Alzheimer's disease at 25 years Embo Molecular Medicine 2016 8 6 595-608
[25]
Siu S, Kuptsov VN, Iun U et al (2013) Databases as instruments for analysis of large-scale data sets on interactions between molecular biological objects. Izv Akad Nauk Ser Biol (3):261
[26]
Sorokina SY, Kuptzov VN, Urban YN, et al. Databases as instruments for analysis of large-scale data sets of interactions between molecular biological objects Biol Bull 2013 40 3 233-242
[27]
Spielman DA and Teng SH A Local Clustering Algorithm for Massive Graphs and its Application to Nearly-Linear Time Graph Partitioning SIAM J Comput 2013 42 1 1-26
[28]
Subramani S, Kalpana R, Monickaraj PM, et al. HPIminer: A text mining system for building and visualizing human protein interaction networks and pathways J Biomed Inform 2015 54 121-131
[29]
Subramani S, Raja K, and Natarajan J An integrated approach for human proteins and protein kinases normalization J Biomed Inform 2014 47 131-138
[30]
The MIntAct project IntAct as a common curation platform for 11 molecular interaction databases Nucleic Acids Res 2014 42 D1 D358-D363
[31]
Toulouse T, Rossi L, Celik T, et al. Automatic fire pixel detection using image processing: a comparative analysis of rule-based and machine learning-based methods Signal Image & Video Processing 2016 10 4 647-654
[32]
Turner ML Safety of blood, blood derivatives, and plasma-derived products Handb Clin Neurol 2018 153 463-472
[33]
Wang J, Zheng W, Qian Y, et al. A Seed Expansion Graph Clustering Method for Protein Complexes Detection in Protein Interaction Networks Molecules 2017 22 12 2179
[34]
Wei L, Zou Q (2016) Recent Progress in Machine Learning-Based Methods for Protein Fold Recognition. Int J Mol Sci 17(12)
[35]
Xia KJ, Wang JQ, and Cai J A novel medical image enhancement algorithm based on improvement correction strategy in wavelet transform domain Clust Comput 2017 2017 20 1-9
[36]
Xue Y, Xu T, Zhang H, et al. SegAN: Adversarial Network with Multi-scale $L_1$ Loss for Medical Image Segmentation Neuroinformatics 2017 2017 6 1-10
[37]
Yadav S, Ekbal A, Saha S Feature selection for entity extraction from multiple biomedical corpora: A PSO-based approach. Soft Comput 2017(4):1–24
[38]
You ZH, Yin Z, Han K, et al. A semi-supervised learning approach to predict synthetic genetic interactions by combining functional and topological properties of functional gene network Bmc Bioinformatics 2010 11 1 343
[39]
Zhang SW, Li YJ, Xia L, et al. PPLook: an automated data mining tool for protein-protein interaction Bmc Bioinformatics 2010 11 1 326-320
[40]
Zhao R and Martinez AM Labeled Graph Kernel for Behavior Analysis IEEE Transactions on Pattern Analysis & Machine Intelligence 2016 38 8 1640-1650
Recommendations
Predicting protein interaction sites: binding hot-spots in protein--protein and protein--ligand interfaces
Motivation: Protein assemblies are currently poorly represented in structural databases and their structural elucidation is a key goal in biology. Here we analyse clefts in protein surfaces, likely to correspond to binding 'hot-spots', and rank them ...
Protein-ligand interaction prediction
Motivation: Predicting interactions between small molecules and proteins is a crucial step to decipher many biological processes, and plays a critical role in drug discovery. When no detailed 3D structure of the protein target is available, ligand-...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
© Springer Science+Business Media, LLC, part of Springer Nature 2019. Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
Publisher
Kluwer Academic Publishers
United States
Publication History
Published: 01 April 2020
Accepted: 31 May 2019
Revision received: 14 May 2019
Received: 29 January 2019
Author Tags
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 19 Dec 2024