scholarly journals Exploring Genome-Wide Expression Profiles Using Machine Learning Techniques

2016 ◽  
pp. 347-364 ◽  
Author(s):  
Moritz Kebschull ◽  
Panos N. Papapanou
Keyword(s):  
Machine Learning ◽  
Expression Profiles ◽  
Machine Learning Techniques ◽  
Genome Wide ◽  
Learning Techniques ◽  
Genome Wide Expression

Image Processing and Machine Learning Techniques for the Segmentation of cDNA Microarray Images

2012 ◽  
pp. 817-829
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.

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

2013 ◽  
Vol 30 (2) ◽  
pp. 228-233 ◽  
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

Image Processing and Machine Learning Techniques for the Segmentation of cDNA

2009 ◽  
pp. 294-306 ◽  
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.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

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.

389-P: Ability for Detecting or Predicting Hypoglycemia with the Aid of Machine Learning Techniques: A Meta-analysis

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 389-P
Author(s):  
SATORU KODAMA ◽  
MAYUKO H. YAMADA ◽  
YUTA YAGUCHI ◽  
MASARU KITAZAWA ◽  
MASANORI KANEKO ◽  
...  
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques
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