Machine Learning Techniques on Gene Function Prediction Volume II

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Editorial

30 June 2022

Editorial: Machine Learning Techniques on Gene Function Prediction Volume II

Ren Qi

,

Arun Kumar Sangaiah

,

Dariusz Mrozek

and

Quan Zou

1,769 views

1 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 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.

In our first instalment, Machine Learning Techniques on Gene Function Prediction Volume I, we found most authors paid attention to gene and ncRNA function prediction. This Research topic will further explore the potential for machine learning applied to gene function prediction. Moreover, we would also like to share some works on single-cell sequencing data analysis and related machine learning methods.

We hope that code describing novel methodology and data from real-world applications can be presented together in this issue. The list of possible topics includes, but 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;
- Machine learning methods on single cell sequencing data.

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 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.

In our first instalment, Machine Learning Techniques on Gene Function Prediction Volume I, we found most authors paid attention to gene and ncRNA function prediction. This Research topic will further explore the potential for machine learning applied to gene function prediction. Moreover, we would also like to share some works on single-cell sequencing data analysis and related machine learning methods.

We hope that code describing novel methodology and data from real-world applications can be presented together in this issue. The list of possible topics includes, but 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;
- Machine learning methods on single cell sequencing data.

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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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