Medical image analysis of phosphorylated protein interaction extraction algorithm based on text mining technology

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.

[1]  Chris Mellish,et al.  Combining information extraction with genetic algorithms for text mining , 2004, IEEE Intelligent Systems.

[2]  David L. Wheeler,et al.  GenBank: update , 2004, Nucleic Acids Res..

[3]  Quan Pan,et al.  PPLook: an automated data mining tool for protein-protein interaction , 2010, BMC Bioinformatics.

[4]  Xiaobo Zhou,et al.  A semi-supervised learning approach to predict synthetic genetic interactions by combining functional and topological properties of functional gene network , 2010, BMC Bioinformatics.

[5]  Alfonso Valencia,et al.  How to link ontologies and protein–protein interactions to literature: text-mining approaches and the BioCreative experience , 2012, Database J. Biol. Databases Curation.

[6]  Stephen C. Ekker,et al.  Mojo Hand, a TALEN design tool for genome editing applications , 2013, BMC Bioinformatics.

[7]  Ian M. Donaldson,et al.  A survey of protein interaction data and multigenic inherited disorders , 2013, BMC Bioinformatics.

[8]  T. Rindflesch,et al.  A closed literature-based discovery technique finds a mechanistic link between hypogonadism and diminished sleep quality in aging men. , 2012, Sleep.

[9]  Marcelo Zanchetta do Nascimento,et al.  Texture extraction: An evaluation of ridgelet, wavelet and co-occurrence based methods applied to mammograms , 2012, Expert Syst. Appl..

[10]  Alfonso Valencia,et al.  MyMiner: a web application for computer-assisted biocuration and text annotation , 2012, Bioinform..

[11]  Kalpana Raja,et al.  PPInterFinder—a mining tool for extracting causal relations on human proteins from literature , 2013, Database J. Biol. Databases Curation.

[12]  Shang-Hua Teng,et al.  A Local Clustering Algorithm for Massive Graphs and Its Application to Nearly Linear Time Graph Partitioning , 2008, SIAM J. Comput..

[13]  M. Y. Ivankov,et al.  Databases as instruments for analysis of large-scale data sets of interactions between molecular biological objects , 2013, Biology Bulletin.

[14]  J. Natarajan,et al.  A hybrid named entity tagger for tagging human proteins/genes , 2014, Int. J. Data Min. Bioinform..

[15]  Kalpana Raja,et al.  ProNormz - An integrated approach for human proteins and protein kinases normalization , 2014, J. Biomed. Informatics.

[16]  J. Geoffrey Chase,et al.  A Patient-Specific Airway Branching Model for Mechanically Ventilated Patients , 2014, Comput. Math. Methods Medicine.

[17]  Xiu-zhi Guo,et al.  Improved glomerular filtration rate estimation using new equations combined with standardized cystatin C and creatinine in Chinese adult chronic kidney disease patients. , 2014, Clinical biochemistry.

[18]  Rafael C. Jimenez,et al.  The MIntAct project—IntAct as a common curation platform for 11 molecular interaction databases , 2013, Nucleic Acids Res..

[19]  Inference of protein-protein interaction networks from multiple heterogeneous data , 2015, EURASIP J. Bioinform. Syst. Biol..

[20]  Kalpana Raja,et al.  HPIminer: A text mining system for building and visualizing human protein interaction networks and pathways , 2015, J. Biomed. Informatics.

[21]  Chun-Nan Hsu,et al.  Weakly supervised learning of biomedical information extraction from curated data , 2016, BMC Bioinformatics.

[22]  Hongfang Liu,et al.  Text mining facilitates database curation - extraction of mutation-disease associations from Bio-medical literature , 2015, BMC Bioinformatics.

[23]  Lei Huang,et al.  Inference of protein-protein interaction networks from multiple heterogeneous data , 2016, EURASIP Journal on Bioinformatics and Systems Biology.

[24]  Markus Kreuzthaler,et al.  MapReduce in the Cloud: A Use Case Study for Efficient Co-Occurrence Processing of MEDLINE Annotations with MeSH , 2016, MIE.

[25]  Aleix M. Martínez,et al.  Labeled Graph Kernel for Behavior Analysis , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  Moulay A. Akhloufi,et al.  Automatic fire pixel detection using image processing: a comparative analysis of rule-based and machine learning-based methods , 2016, Signal Image Video Process..

[27]  Q. Zou,et al.  Recent Progress in Machine Learning-Based Methods for Protein Fold Recognition , 2016, International journal of molecular sciences.

[28]  Zhiyong Lu,et al.  Community challenges in biomedical text mining over 10 years: success, failure and the future , 2016, Briefings Bioinform..

[29]  F. Moraes,et al.  A decade of human genome project conclusion: Scientific diffusion about our genome knowledge , 2016, Biochemistry and molecular biology education : a bimonthly publication of the International Union of Biochemistry and Molecular Biology.

[30]  J. Hardy,et al.  The amyloid hypothesis of Alzheimer's disease at 25 years , 2016, EMBO molecular medicine.

[31]  Jian Cai,et al.  A novel medical image enhancement algorithm based on improvement correction strategy in wavelet transform domain , 2017, Cluster Computing.

[32]  Jie Wang,et al.  A Seed Expansion Graph Clustering Method for Protein Complexes Detection in Protein Interaction Networks , 2017, Molecules.

[33]  Rajarshi Guha,et al.  Pharos: Collating protein information to shed light on the druggable genome , 2016, Nucleic Acids Res..

[34]  Amina Asif,et al.  Training large margin host-pathogen protein-protein interaction predictors , 2017, J. Bioinform. Comput. Biol..

[35]  Tao Xu,et al.  SegAN: Adversarial Network with Multi-scale L1 Loss for Medical Image Segmentation , 2017, Neuroinformatics.

[36]  Asif Ekbal,et al.  Feature selection for entity extraction from multiple biomedical corpora: A PSO-based approach , 2018, Soft Comput..

[37]  Yanhui Hu,et al.  Molecular Interaction Search Tool (MIST): an integrated resource for mining gene and protein interaction data , 2017, Nucleic Acids Res..

[38]  M. Turner.  Safety of blood, blood derivatives, and plasma-derived products. , 2018, Handbook of clinical neurology.

[39]  Takanori Hayashi,et al.  MEGADOCK-Web: an integrated database of high-throughput structure-based protein-protein interaction predictions , 2018, BMC Bioinformatics.