• 2019-10
  • 2019-11
  • 2020-03
  • 2020-07
  • 2020-08
  • 2021-03
  • br Acknowledgements br We thank our supervisors


    We thank our supervisors for their continuous suggestions and feedback, we thank our college management especially Dr.Lavu Rathaiah Garu for supporting us, who put their faith in us. This work is mainly based on biomedical data extraction from the corpus, so we thank the referees in this Cucurbitacin I wide area. We thank each and everyone who supported us to accomplish this work successfully. This material has not been published in whole or in part elsewhere and not funded by any organization or committee.  Informatics in Medicine Unlocked 16 (2019) 100188
    ML Machine learning
    XML eXtensible Markup Language
    MEDLINE Medical Literature Analysis and Retrieval System Online
    MeSH Medical Subject Headings
    MLP Multi-Layer Perceptron
    LHNFCSF Linguistic Hedges Neuro-Fuzzy Cclassifier with Selected Features
    Fuzzy ART Fuzzy logic and Adaptive Resonance Theory FAST Fast clustering-based feature selection algorithm
    MST Minimum-Spanning Tree
    FSS Feature Subset Selection
    MRMD Max-Relevance-Max-Distance
    DFS Distinguishing Feature Selector
    FE Feature Extraction
    COSMIC Catalogue of Somatic Mutations in Cancer
    BKT Bio-Key Terms
    DNA DeoxyriboNucleic Acid
    Appendix A. Supplementary data
    [1] Michie D, Spiegelhalter DJ, Taylor CC. Machine learning. Neural and Statistical Classification 1994;13. [2] Kotsiantis SB, Zaharakis I, Pintelas P. Supervised machine learning: a review of classification techniques. Emerging artificial intelligence applications in computer engineering 2007;160:3–24. [3] Lee WS, Liu B. August. Learning with positive and unlabeled examples using weighted logistic regression. ICML, vol. 3. 2003. p. 448–55. [4] Rust J. Using randomization to break the curse of dimensionality. Econometrica: Journal of the Econometric Society 1997:487–516. [5] Quackenbush J. Computational genetics: computational analysis of microarray data. Cucurbitacin I Nat Rev Genet 2001;2(6):418. [6] Novichkova S, Egorov S, Daraselia N. MedScan, a natural language processing en-gine for MEDLINE abstracts. Bioinformatics 2003;19(13):1699–706. [7] Spasic I, Ananiadou S, McNaught J, Kumar A. Text mining and ontologies in bio-medicine: making sense of raw text. Briefings Bioinf 2005;6(3):239–51. [8] Guyon I, Elisseeff A. An introduction to variable and feature selection. J Mach Learn Res 2003;3(Mar):1157–82. [9] Lazar C, Taminau J, Meganck S, Steenhoff D, Coletta A, Molter C, de Schaetzen V, Duque R, Bersini H, Nowe A. A survey on filter techniques for feature selection in gene expression microarray analysis. IEEE ACM Trans Comput Biol Bioinform 2012;9(4):1106–19.
    [10] Azar Ahmad Taher, Aboul Ella Hassanien. Dimensionality reduction of medical big data using neural-fuzzy classifier. Soft computing 2015;19(4):1115–27. [11] Lilleberg Joseph, Zhu Yun, Zhang Yanqing. Support vector machines and word2vec for text classification with semantic features. 2015 IEEE 14th international con-ference on cognitive informatics & cognitive computing (ICCI* CC). IEEE. 2015. [12] Sheikhan Mansour, Sharifi Rad Maryam. Gravitational search algorithm–optimized neural misuse detector with selected features by fuzzy grids–based association rules mining. Neural Comput Appl 2013;23(7–8):2451–63. [13] Song Q, Ni J, Wang G. A fast clustering-based feature subset selection algorithm for high-dimensional data. IEEE Trans Knowl Data Eng 2013;25(1):1–14. [14] Bermejo P, Gámez JA, Puerta JM. Speeding up incremental wrapper feature subset selection with Naive Bayes classifier. Knowl Based Syst 2014;55:140–7. [15] Zou Q, Zeng J, Cao L, Ji R. A novel features ranking metric with application to scalable visual and bioinformatics data classification. Neurocomputing 2016;173:346–54. [16] Sharma A, Imoto S, Miyano S. A top-r feature selection algorithm for microarray gene expression data. IEEE ACM Trans Comput Biol Bioinform 2012;9(3):754–64.