Machine Learning Techniques on Gene Function Prediction

437.2K

views

212

authors

48

articles

Machine Learning Techniques on Gene Function Prediction

437.2K

views

212

authors

48

articles

Editorial

04 October 2019

Editorial: Machine Learning Techniques on Gene Function Prediction

Quan Zou

,

Arun Kumar Sangaiah

and

Dariusz Mrozek

4,867 views

5 citations

Editors

3

University of Electronic Science and Technology of China

Arun Kumar Sangaiah

School of Computer Science and Engineering, VIT University

Silesian University of Technology

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Gene function, including that of coding and non-coding genes, can be difficult to identify in molecular wet laboratories. Therefore, computational methods, often including machine learning, may be a useful tool to guide and predict function. Although machine learning has been considered as a “black box” in the past, it can be more accurate than simple statistical testing methods. In recent years, deep learning and big data machine learning techniques have developed rapidly and achieved an amazing level of performance in many areas including image classification and speech recognition. This Research topic will explore the potential for machine learning applied to gene function prediction.

We hope that code describing novel methodology and data from real world application can be presented together in this issue. The list of possible topics includes, but is not limited to:

- Latest machine learning algorithms on gene function prediction;

- Reviews or surveys with benchmark datasets in gene function prediction;

- Deep learning techniques with applications in gene function prediction;

- Non-coding gene functional computational analysis.

Gene function, including that of coding and non-coding genes, can be difficult to identify in molecular wet laboratories. Therefore, computational methods, often including machine learning, may be a useful tool to guide and predict function. Although machine learning has been considered as a “black box” in the past, it can be more accurate than simple statistical testing methods. In recent years, deep learning and big data machine learning techniques have developed rapidly and achieved an amazing level of performance in many areas including image classification and speech recognition. This Research topic will explore the potential for machine learning applied to gene function prediction.

We hope that code describing novel methodology and data from real world application can be presented together in this issue. The list of possible topics includes, but is not limited to:

- Latest machine learning algorithms on gene function prediction;

- Reviews or surveys with benchmark datasets in gene function prediction;

- Deep learning techniques with applications in gene function prediction;

- Non-coding gene functional computational analysis.

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Editors

University of Electronic Science and Technology of China

Arun Kumar Sangaiah

School of Computer Science and Engineering, VIT University

Silesian University of Technology

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Frontiers in Genetics

Computational Genomics

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Frontiers in Plant Science

Computational Genomics

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Frontiers in Bioengineering and Biotechnology

Computational Genomics

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