Survey on Usage of Machine Learning Techniques in Different Biological Domains

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2019 by IJCTT Journal
Volume-67 Issue-7
Year of Publication : 2019
Authors :  Divya K S, Dr M A Dorairangaswamy, Jain Stoble B
DOI :  10.14445/22312803/IJCTT-V67I7P108

MLA

MLA Style: Divya K S, Dr M A Dorairangaswamy, Jain Stoble B"Survey on Usage of Machine Learning Techniques in Different Biological Domains" International Journal of Computer Trends and Technology 67.7 (2019): 54-56.

APA Style Divya K S, Dr M A Dorairangaswamy, Jain Stoble B. Survey on Usage of Machine Learning Techniques in Different Biological Domains International Journal of Computer Trends and Technology, 67(7),54-56.

Abstract
Nowadays, one of the most challenging problems in computational biology is to transform the raw data into knowledge. Different Machine learning techniques can be used to carry out this transformation. There are several biological realm where machine learning techniques are applied for knowledge extraction from raw data. We can catego-rize these domains into genomics, proteomics, mi-croarrays, systems biology, evolution and text min-ing. This paper gives a brief overview of different biological domains where machine learning tech-niques can be applied.

Reference
[1] Machine learning in bioinformatics Pedro Lar-ran‹aga,BorjaCalvo, Roberto Santana,ConchaBielza, Josu-Galdiano,In‹akiInza, Jose¤ A.Lozano, Rube¤nArman‹anzas,Guzma¤nSantafe¤, AritzPe¤rezand Victor Robles
[2] https://www.ncbi.nlm.nih.gov/CBBresearch/Przytycka/download/lectures/PCB_Lect08_Bind_Motifs.pdf
[3] Motif Discovery in Protein SequencesBy Salma Aouled El Haj Mohamed, MouradElloumi and Julie D. Thompson-Submitted: April 13th 2016Reviewed: August 30th 2016Published: December 14th 2016 .DOI: 10.5772/65441
[4] Bilal Aslam, MadihaBasit, Muhammad AtifNisar, Moh-sinKhurshid, Muhammad HidayatRasool, Proteomics: Technologies and Their Applications, Journal of Chromato-graphic Science, Volume 55, Issue 2, 1 February 2017, Pag-es 182–196, https://doi.org/10.1093/chromsci/bmw167
[5] Microarray Image Segmentation Using Clustering Methods VolkanUslan and ?hsanÖmürBucak,Department of Computer Engineering,Fatih University, 34500,B.Çekmece,?stanbul, This email address is being protected from spambots. You need JavaScript enabled to view it. This email address is being protected from spambots. You need JavaScript enabled to view it.
[6] Rodriguez-Esteban, Raul. “Biomedical text mining and its applications.” PLoS computational biology vol. 5,12 (): e1000597. doi:10.1371/journal.pcbi.1000597
[7] Text Mining Basics in Bioinformatics Carmen De Maioa , Giuseppe Fenzab , Vincenzo Loiab , MimmoParentebaDi-partimento di Ingegneriadell’InformazioneedElettrica e Ma-tematicaApplicata, University of Salerno, 84084 Fisciano (SA), Italy bDipartimento di ScienzeAziendali - Manage-ment & Innovation Systems, University of Salerno, 84084 Fisciano (SA), Italy
[8] Shatkay H, Höglund A, Brady S, Blum T, Dönnes P, et al. SherLoc: high-accuracy prediction of protein subcellular lo-calization by integrating text and protein sequence da-ta. Bioinformatics. 2007;23:1410–1417.Available: http://www-bs.informatik.uni- tuebin-gen.de/Services/SherLoc2/ [PubMed] [Google Scholar]
[9] Brady S, Shatkay H. EpiLoc: a (working) text-based system for predicting protein subcellular location. Pac SympBi-ocomput. 2008:604–615. Available: http://epiloc.cs.queensu.ca/ [PubMed] [Google Scholar]
[10] Glenisson P, Coessens B, Van Vooren S, Mathys J, Moreau Y, et al. TXTGate: profiling gene groups with text-based in-formation. Genome Bi-ol. 2004;5:R43. Available: http://tomcat.esat.kuleuven.be/txtgate/[PMC free article] [PubMed] [Google Scholar]
[11] ]Liki? VA, McConville MJ, Lithgow T, Bacic A. Systems biology: the next frontier for bioinformatics. Adv Bioinfor-matics.2010;2010:268925. doi:10.1155/2010/268925
[12] Gabaldón T. Evolution of proteins and proteomes: a phylogenetic approach. EvolBioinform Online. 2007;1:51–61. Published 2007 Feb 24.

Keywords
Bioinformatics, Genomics, Proteomics, Microarrays, Systems biology, Evolution, Text min-ing.