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Prognosticate the Drugs for Multiple Myeloma Patients by Using Gene Expression Technique with Polyclonal And Monoclonal Samples
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A major protest in cancer treatment is predicting the clinical response to anti-cancer drugs for each individual patient. For complex diseases such as Myeloma, characterized by high inter-patient variance, the implementation of precision medicine approaches is dependent upon understanding the pathological processes at the molecular level. Myeloma is one of the horrible diseases in the world claiming plurality of lives. Accurately predicting drug responses to Myeloma is a most important problem preventing oncologists’ efforts to ensemble the most powerful drugs to treat Myeloma, which is a ischolar_main goal in precision medicine. It entails the design of therapies that are matched for each individual patient. In this article, it considers a review of approaches that have been proposed to tackle the drug sensitivity prediction problem especially with respect to the personalized Cancer therapy. There are a total of 44 drug sensitivity prediction algorithms. In that the gene expression microarrays consistently provided the best predictive power of the individual profiling data sets; however, performance was increased by including multiple, independent data sets. The proposed algorithm surpassed Bayesian Multitask Multiple Kernel Learning (BMMKL) classification which currently represent the state-of-the-art in drug-response prediction and finally passed the gene expression data to Cytoscape for visualization.
Keywords
Drug Sensitivity Prediction, Gene Expression Microarrays, Prediction Algorithms.
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- Cancer Research UK, Available at: http://cancerresearchuk.org
- Cell Division and Cancer, Available at: http://www.abpischools.org.uk – cancer cells
- H.T. Madhloom, S.A. Kareem, H. Ariffin, A.A. Zaidan, H.O. Alanazi and B.B. Zaidan, “An Automated White Blood Cell Localization and Segmentation using Image Arithmetic and Automatic Threshold”, Journal of Applied Sciences, Vol. 10, No. 11, pp. 959-966, 2010.
- F. Scotti, “Robust Segmentation and Measurements of White Cells in Blood Microscopic Images”, Proceedings of IEEE Instrumentation and Measurement Technology Conference, pp. 43-48, 2006.
- M. Ghosh, D. Das, C. Chakraborty and A.K. Ray, “Automated Leukocyte Recognition using Fuzzy Divergence”, Micron, Vol. 41, No. 7, pp. 840-846, 2010.
- V. Vapnik and A. Lerner, “Pattern Recognition using Generalized Portrait Method”, Automation Remote Control, Vol. 24, pp. 774-780, 1963.
- V. Vapnik and A. Chervonenkis, “A Note on One Class of Perceptrons”, Automation Remote Control, Vol. 25, pp. 735-739, 1964.
- V.N. Vapnik, “Statistical Learning Theory”, Wiley, 1998.
- A.J. Smola and B. Scholkopf, “A Tutorial on Support Vector Regression”, Statistics and Computing, Vol. 14, pp. 199-222, 2004.
- V. Cherkassky and Y. Ma, “Comparison of Model Selection for Regression”, Neural Computation, Vol. 15, pp. 1691-1714, 2003.
- M. Gonen and A.A. Margolin, “Drug Susceptibility Prediction against a Panel of Drugs using Kernelized Bayesian Multitask Learning”, Bioinformatics, Vol. 30, pp. 566-563, 2014.
- C.S. Grasso et al., “Functionally-defined Therapeutic Targets in Diffuse Intrinsic Pontine Glioma”, Nature Medicine, Vol. 21, No. 6, pp. 555-559, 2015.
- R.H. Shoemaker, “The NCI60 Human Tumour Cell Line Anticancer Drug Screen”, Nature Review Cancer, Vol. 6, No. 10, pp. 813-823, 2006.
- R. Pal et al., “Boolean Relationships among Genes Responsive to Ionizing Radiation in the NCI 60 ACDS”, Bioinformatics, Vol. 21, No. 8, pp. 1542-1549, 2005.
- J. Barretina et al., “The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity”, Nature, Vol. 483, pp. 603-607, 2012.
- W.E.A. Yang, “Genomics of Drug Sensitivity in Cancer (GDSC): A Resource for Therapeutic Biomarker Discovery in Cancer Cells”, Nucleic Acids Research, Vol. 41, pp. 955-961, 2013.
- A. Basu et al., “An Interactive Resource to Identify Cancer Genetic and Lineage Dependencies Targeted by Small Molecules”, Cell, Vol. 154, No. 5, pp. 1151-1161, 2013.
- J.B. Kim, “Three-Dimensional in Vitro Tissue Culture Models of Breast Cancer-A Review”, Breast Cancer Research and Treatment, Vol. 85, No. 3, pp. 281-291, 2004.
- J.W. Gordon et.al., “Genetic Transformation of Mouse Embryos by Microinjection of Purified DNA”, Proceedings of National Academy of Science of the United States of America, Vol. 77, No. 12, pp. 7380-7384, 1980.
- J.C. Walrath, “Genetically Engineered Mouse Models in Cancer Research”, Advances in Cancer Research, Vol. 106, pp. 113-164, 2010.
- A. Richmond and Y. Su, “Mouse Xenograft Models vs. GEM Models for Human Cancer Therapeutics”, Disease Models and Mechanism, Vol. 1, No. 2-3, pp. 78-82, 2008.
- R.S. Kerbel, “Human Tumor Xenografts as Predictive Preclinical Models for Anticancer Drug Activity in Humans: better than commonly Perceived-but they can be Improved”, Cancer Biology and Theraphy, Vol. 2, pp. 134-139, 2003.
- J.I. Johnson et al., “Relationships between Drug Activity in NCI Preclinical in Vitro and in Vivo Models and Early Clinical Trials”, British Journal of Cancer, Vol. 84, pp. 1424-1431, 2001.
- C.C. Scholz, “Correlation of Drug Response in Patients and in the Clonogenic Assay with Solid Human Tumour Xenografts”, European Journal of Cancer, Vol. 26, No. 8, pp. 901-905, 1990.
- C. Khanna et al., “The Dog as a Cancer Model”, Nature Biotechnology, Vol. 24, No. 9, pp. 1065-1066, 2006.
- B. Haibe-Kains, “Inconsistency in Large Pharmacogenomic Studies”, Nature, Vol. 504, pp. 389-393, 2013.
- J.C. Costello, et al., “A Community Effort to Assess and Improve Drug Sensitivity Prediction Algorithms”, Nature Biotechnology, Vol. 32, No. 12, pp. 1202-1212, 2014.
- Q. Wan and R. Pal, “An Ensemble based Top Performing Approach for NCI-DREAM Drug Sensitivity Prediction Challenge”, PLoS ONE, Vol. 9, No. 6, pp. 1-7, 2014.
- A.N. Aimi Salihah, M.Y. Mashor and Azian Azamimi Abdullah, “Improving Blast Segmentation of Cute Myelogenous Leukaemia (AML) Images using Bright Stretching Technique”, Proceedings of International Post Graduate Conference on Engineering, pp. 1-5, 2010.
- S. Olalekan Akinola and O. Jephthar Oyabugbe, “Accuracies and Training Times of Data Mining Classification Algorithms: An Empirical Comparative Study”, Journal of Software Engineering and Applications, Vol. 8, pp. 470-477, 2015
- Tanvi Sharma, Anand Sharma and Vibhakar Mansotra, “Performance Analysis of Data Mining Classification Techniques on Public Health Care Data”, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 4, No. 6, pp. 1-5, 2016.
- D. Szklarczyk et al., “STRING v10: Protein-Protein Interaction Networks, Integrated over the Tree of Life”, Nucleic Acids Research, Vol. 43, pp. 447-452, 2015.
- P. Shannon et al., “A Software Environment for Integrated Models of Biomolecular Interaction Networks”, Genome Research, Vol. 13, pp. 2498-2504, 2003.
- N. Sinha and A.G. Ramakrishnan, “Automation of Differential Blood Count”, Proceedings of IEEE International Conference on Convergent Technologies for the Asia-Pacific region, pp. 371-375, 2003.
- National Cancer Institute, “NCI Dictionary of Cancer Terms”, Available at:
- https://www.cancer.gov/publications/dictionaries/cancer-terms?cdrid= 561717
- String, Available at: http://string-db.org/
- CyTargetLinker, Available at: http://projects.bigcat.unimaas.nl/cytargetlinker/
- Drugbank, Available at: http://www.drugbank.ca/
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