Exploring Genome-Wide Expression Profiles Using Machine Learning Techniques

Microarray technology allows the comprehensive measurement of the expression level of many genes simultaneously on a common substrate. Typical applications of microarrays include the quantification of expression profiles of a system under different experimental conditions, or expression profile comparisons of two systems for one or more conditions. Microarray image analysis is a crucial step in the analysis of microarray data. In this chapter an extensive overview of the segmentation of the microarray image is presented. Methods already presented in the literature are classified into two main categories:methods which are based on image processing techniques and those which are based on Machine learning techniques. A novel classification-based application for the segmentation is also presented to demonstrate efficiency.

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Combining optimization and machine learning techniques for genome-wide prediction of human cell cycle-regulated genes

Bioinformatics ◽

10.1093/bioinformatics/btt671 ◽

2013 ◽

Vol 30 (2) ◽

pp. 228-233 ◽

Cited By ~ 6

Author(s):

Marianna De Santis ◽

Francesco Rinaldi ◽

Emmanuela Falcone ◽

Stefano Lucidi ◽

Giulia Piaggio ◽

...

Keyword(s):

Machine Learning ◽

Cell Cycle ◽

Human Cell ◽

Machine Learning Techniques ◽

Genome Wide ◽

Learning Techniques

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Machine Learning Techniques and Chi-Square Feature Selection for Cancer Classification Using SAGE Gene Expression Profiles

Lecture Notes in Computer Science - Data Mining for Biomedical Applications ◽

10.1007/11691730_11 ◽

2006 ◽

pp. 106-115 ◽

Cited By ~ 58

Author(s):

Xin Jin ◽

Anbang Xu ◽

Rongfang Bie ◽

Ping Guo

Keyword(s):

Gene Expression ◽

Machine Learning ◽

Feature Selection ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Cancer Classification ◽

Machine Learning Techniques ◽

Chi Square ◽

Learning Techniques ◽

Selection For

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Advanced Bioinformatics Approach in Machine Learning for Analyzing Genome Wide Expression Profiles and Proteomic Data Sets

Computer Networks and Information Technologies - Communications in Computer and Information Science ◽

10.1007/978-3-642-19542-6_54 ◽

2011 ◽

pp. 305-307

Author(s):

Archana Dash ◽

Tripti Swarnkar ◽

Mamata Nayak

Keyword(s):

Machine Learning ◽

Expression Profiles ◽

Data Sets ◽

Proteomic Data ◽

Genome Wide ◽

Genome Wide Expression

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Image Processing and Machine Learning Techniques for the Segmentation of cDNA

Handbook of Research on Advanced Techniques in Diagnostic Imaging and Biomedical Applications ◽

10.4018/978-1-60566-314-2.ch019 ◽

2009 ◽

pp. 294-306 ◽

Cited By ~ 2

Author(s):

Nikolaos Giannakeas ◽

Dimitrios I. Fotiadis

Keyword(s):

Machine Learning ◽

Image Processing ◽

Expression Profiles ◽

Machine Learning Techniques ◽

Experimental Conditions ◽

Microarray Image ◽

Learning Techniques ◽

Common Substrate ◽

Processing Techniques ◽

Comprehensive Measurement

Microarray technology allows the comprehensive measurement of the expression level of many genes simultaneously on a common substrate. Typical applications of microarrays include the quantification of expression profiles of a system under different experimental conditions, or expression profile comparisons of two systems for one or more conditions. Microarray image analysis is a crucial step in the analysis of microarray data. In this chapter an extensive overview of the segmentation of the microarray image is presented. Methods already presented in the literature are classified into two main categories:methods which are based on image processing techniques and those which are based on Machine learning techniques. A novel classification-based application for the segmentation is also presented to demonstrate efficiency.

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Using machine learning techniques to reduce data annotation time

PsycEXTRA Dataset ◽

10.1037/e577762012-020 ◽

2006 ◽

Author(s):

Christopher Schreiner ◽

Kari Torkkola ◽

Mike Gardner ◽

Keshu Zhang

Keyword(s):

Machine Learning ◽

Machine Learning Techniques ◽

Data Annotation ◽

Learning Techniques

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Using Machine Learning Algorithms on Prediction of Stock Price

Journal of Modeling and Optimization ◽

10.32732/jmo.2020.12.2.84 ◽

2020 ◽

Vol 12 (2) ◽

pp. 84-99

Author(s):

Li-Pang Chen

Keyword(s):

Machine Learning ◽

Stock Price ◽

Short Term Memory ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Support Vector ◽

Short Term ◽

Learning Techniques ◽

Historical Database ◽

Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

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