The development and broad application of high-throughput experimental technologies have enabled biology to enter the era of ‘Big Data’ (large datasets). How to effectively mine biological knowledge from the overwhelming amount of data is a challenging and important problem in plant science.
Machine learning is a multidisciplinary field incorporating computer science, statistics, artificial intelligence, information theory, and so on. It offers promising computational and analytical solutions for the intelligent analysis of large and complex datasets, and is gradually gaining popularity in biology. Machine learning methods have been used in many areas of large-scale data analysis for genetics, genomics, transcriptomics, proteomics, and systems biology. To date, most machine-learning-based applications have been in animal studies, whereas few have been in plant science studies. In addition, most machine learning-based models and software packages were originally developed for the prediction problems in animal studies. To ensure performance, plant-specific models, software packages, and web servers are required, because the distribution of the used features (e.g., sequence and structural features) may vary from species to species. Moreover, the rapid increase of ~OMICS datasets gives rise to an increasing demand for the integrative analysis to solve plant-specific problems using traditional and advanced machine learning algorithms (e.g., deep learning).
The aim of this Research Topic is to collect both reviews and original research articles related to machine learning, in order to develop novel machine learning-based bioinformatics approaches, software packages, and web servers for accelerating the data-to-knowledge translation process in plant sciences.
This Research Topic includes, but is not limited to, the following:
• Review, evaluation and/or application of machine learning-based models, software packages and web servers for specific prediction problems in plants.
• Development of novel models, software packages and web servers for specific prediction problems in plants using traditional and novel machine learning technologies (i.e., deep learning).
• Development of biological databases with machine learning-based predictions and experimental data.
The development and broad application of high-throughput experimental technologies have enabled biology to enter the era of ‘Big Data’ (large datasets). How to effectively mine biological knowledge from the overwhelming amount of data is a challenging and important problem in plant science.
Machine learning is a multidisciplinary field incorporating computer science, statistics, artificial intelligence, information theory, and so on. It offers promising computational and analytical solutions for the intelligent analysis of large and complex datasets, and is gradually gaining popularity in biology. Machine learning methods have been used in many areas of large-scale data analysis for genetics, genomics, transcriptomics, proteomics, and systems biology. To date, most machine-learning-based applications have been in animal studies, whereas few have been in plant science studies. In addition, most machine learning-based models and software packages were originally developed for the prediction problems in animal studies. To ensure performance, plant-specific models, software packages, and web servers are required, because the distribution of the used features (e.g., sequence and structural features) may vary from species to species. Moreover, the rapid increase of ~OMICS datasets gives rise to an increasing demand for the integrative analysis to solve plant-specific problems using traditional and advanced machine learning algorithms (e.g., deep learning).
The aim of this Research Topic is to collect both reviews and original research articles related to machine learning, in order to develop novel machine learning-based bioinformatics approaches, software packages, and web servers for accelerating the data-to-knowledge translation process in plant sciences.
This Research Topic includes, but is not limited to, the following:
• Review, evaluation and/or application of machine learning-based models, software packages and web servers for specific prediction problems in plants.
• Development of novel models, software packages and web servers for specific prediction problems in plants using traditional and novel machine learning technologies (i.e., deep learning).
• Development of biological databases with machine learning-based predictions and experimental data.
