A hybrid approach for efficient multi‐classification of white blood cells based on transfer learning techniques and traditional machine learning methods

2021 ◽  
Author(s):  
Emine Cengil ◽  
Ahmet Çınar ◽  
Muhammed Yıldırım
Keyword(s):  
Machine Learning ◽  
Transfer Learning ◽  
Blood Cells ◽  
White Blood Cells ◽  
Hybrid Approach ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Learning Techniques ◽  
Multi Classification

Learning Methodologies for Detection and Classification of Mutagens

2011 ◽  
pp. 274-288
Author(s):  
Huma Lodhi
Keyword(s):  
Machine Learning ◽  
Ames Test ◽  
Computational Techniques ◽  
Biological Method ◽  
Challenging Problem ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Learning Techniques ◽  
Dynamic Growth

Predicting mutagenicity is a complex and challenging problem in chemoinformatics. Ames test is a biological method to assess mutagenicity of molecules. The dynamic growth in the repositories of molecules establishes a need to develop and apply effective and efficient computational techniques to solving chemoinformatics problems such as identification and classification of mutagens. Machine learning methods provide effective solutions to chemoinformatics problems. This chapter presents an overview of the learning techniques that have been developed and applied to the problem of identification and classification of mutagens.

Machine Learning methods for micro-FTIR imaging classification of human skin tumors

2021 ◽  
Author(s):  
Matheus del Valle ◽  
Kleber Stancari ◽  
Pedro Arthur Augusto de Castro ◽  
Moises Oliveira dos Santos ◽  
Denise Maria Zezell
Keyword(s):  
Machine Learning ◽  
Human Skin ◽  
Skin Tumors ◽  
Learning Methods ◽  
Ftir Imaging ◽  
Machine Learning Methods ◽  
Imaging Classification

scholarly journals Classification of HIV-1 Protease Inhibitors by Machine Learning Methods

ACS Omega ◽  
2018 ◽  
Vol 3 (11) ◽  
pp. 15837-15849 ◽  
Author(s):  
Yang Li ◽  
Yujia Tian ◽  
Zijian Qin ◽  
Aixia Yan
Keyword(s):  
Machine Learning ◽  
Protease Inhibitors ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Hiv 1

scholarly journals Seeing It All: Evaluating Supervised Machine Learning Methods for the Classification of Diverse Otariid Behaviours

PLoS ONE ◽  
2016 ◽  
Vol 11 (12) ◽  
pp. e0166898 ◽  
Author(s):  
Monique A. Ladds ◽  
Adam P. Thompson ◽  
David J. Slip ◽  
David P. Hocking ◽  
Robert G. Harcourt
Keyword(s):  
Machine Learning ◽  
Supervised Machine Learning ◽  
Learning Methods ◽  
Machine Learning Methods

scholarly journals The rise and fall of machine learning methods in biomedical research

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 2012 ◽  
Author(s):  
Hashem Koohy
Keyword(s):  
Machine Learning ◽  
Biomedical Research ◽  
Life Sciences ◽  
Biological Data ◽  
Research Note ◽  
Machine Learning Techniques ◽  
Learning Methods ◽  
The Past ◽  
Machine Learning Methods ◽  
Learning Techniques

In the era of explosion in biological data, machine learning techniques are becoming more popular in life sciences, including biology and medicine. This research note examines the rise and fall of the most commonly used machine learning techniques in life sciences over the past three decades.

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