Graphical Model and Clustering-Regression based Methods for Causal Interactions: Breast Cancer Case Study

The early detection of Breast Cancer, the deadly disease that mostly affects women is extremely complex because it requires various features of the cell type. Therefore, the efficient approach to diagnosing Breast Cancer at the early stage was to apply artificial intelligence where machines are simulated with intelligence and programmed to think and act like a human. This allows machines to passively learn and find a pattern, which can be used later to detect any new changes that may occur. In general, machine learning is quite useful particularly in the medical field, which depends on complex genomic measurements such as microarray technique and would increase the accuracy and precision of results. With this technology, doctors can easily diagnose patients with cancer quickly and apply the proper treatment in a timely manner. Therefore, the goal of this paper is to address and propose a robust Breast Cancer diagnostic system using complex genomic analysis via microarray technology. The system will combine two machine learning methods, K-means cluster, and linear regression.

[1]  Luo Si,et al.  A Knowledge Driven Regression Model for Gene Expression and Microarray Analysis , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[2]  Syed Mohsin,et al.  Gene expression profiling for the prediction of therapeutic response to docetaxel in patients with breast cancer , 2003, The Lancet.

[3]  Jack Y. Yang,et al.  A comparative study of different machine learning methods on microarray gene expression data , 2008, BMC Genomics.

[4]  Robert V. Brill,et al.  Applied Statistics and Probability for Engineers , 2004, Technometrics.

[5]  Kathleen Marchal,et al.  Cluster analysis of microarray data , 2004 .

[6]  T. Pham,et al.  Analysis of Microarray Gene Expression Data , 2006 .

[7]  L. Qin,et al.  The Clustering of Regression Models Method with Applications in Gene Expression Data , 2006, Biometrics.

[8]  K. Dinakaran,et al.  Model Based Modified K-Means Clustering for Microarray Data , 2009, 2009 International Conference on Information Management and Engineering.

[9]  L. Qin,et al.  Finding gene clusters for a replicated time course study , 2014, BMC Research Notes.

[10]  R. Spang,et al.  Predicting the clinical status of human breast cancer by using gene expression profiles , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[11]  Lucila Ohno-Machado,et al.  A primer on gene expression and microarrays for machine learning researchers , 2004, J. Biomed. Informatics.

[12]  Werner Dubitzky,et al.  Data Mining and Machine Learning Methods for Microarray Analysis , 2002 .

[13]  Ritu Sharma,et al.  K-Means Clustering in Spatial Data Mining using Weka Interface , 2012 .

[14]  Tolga Ensari,et al.  Microarray breast cancer data classification using machine learning methods , 2018, 2018 Electric Electronics, Computer Science, Biomedical Engineerings' Meeting (EBBT).

[15]  Saurabh Pal,et al.  A Novel Approach for Breast Cancer Detection Using Data Mining Techniques , 2017 .

[16]  อนิรุธ สืบสิงห์,et al.  Data Mining Practical Machine Learning Tools and Techniques , 2014 .

[17]  John Quackenbush.  Microarray data normalization and transformation , 2002, Nature Genetics.

[18]  Hajar Mousannif,et al.  Using Machine Learning Algorithms for Breast Cancer Risk Prediction and Diagnosis , 2016, ANT/SEIT.

[19]  Monika Jena,et al.  A Study on WEKA Tool for Data Preprocessing, Classification and Clustering , 2013 .

[20]  P. Rajendiran,et al.  Using K-Means Clustering Technique to Study of Breast Cancer , 2014, 2014 World Congress on Computing and Communication Technologies.

[21]  Susmita Datta,et al.  Comparisons and validation of statistical clustering techniques for microarray gene expression data , 2003, Bioinform..

[22]  Dimitrios I. Fotiadis,et al.  Machine learning applications in cancer prognosis and prediction , 2014, Computational and structural biotechnology journal.

[23]  Md. Kamrul Hasan,et al.  Linear regression-based feature selection for microarray data classification , 2015, Int. J. Data Min. Bioinform..

[24]  A. Schulze,et al.  Navigating gene expression using microarrays — a technology review , 2001, Nature Cell Biology.

[25]  Melissa Zhao,et al.  Machine Learning With K-Means Dimensional Reduction for Predicting Survival Outcomes in Patients With Breast Cancer , 2018, Cancer informatics.

[26]  Sung-Bae Cho,et al.  Machine Learning in DNA Microarray Analysis for Cancer Classification , 2003, APBC.

[27]  Ahmet Sacan,et al.  Data simulation and regulatory network reconstruction from time-series microarray data using stepwise multiple linear regression , 2012, Network Modeling Analysis in Health Informatics and Bioinformatics.

[28]  A. Beigzadeh,et al.  Machine learning models in breast cancer survival prediction. , 2016, Technology and health care : official journal of the European Society for Engineering and Medicine.