About Machine Learning in Genomics
Machine Learning in Genomics
About this Collection
This collection is now closed to submissions.
Since the completion of the Human Genome Project, ever-evolving biotechnologies have enabled us to deepen our understanding of our genetic code and that of other species. These datasets provide insights at unprecedented scale and depth, but utilizing their potential requires tackling challenges due to sparsity, high-dimensionality, cross-modality and the need for interpretability. Recently computational methods from the field of machine learning have provided tools for surmounting these challenges.
This article collection aims to promote the latest research applying machine learning approaches to advance genomic research. We welcome submissions of research involving the development and/or application of machine learning methods to genomic data sets including, but not limited to, genome sequencing, gene expression, single-cell genomics, functional genomics, or nuclear organization. Machine learning methods may include supervised, unsupervised or semi-supervised approaches based on neural networks, probabilistic graphical models, ensemble methods, convex optimization, or related approaches.
Keywords: genomics; epigenomics; chromatin organization; single-cell genomics; functional genomics; model organism genomics; gene expression; machine learning; deep learning; artificial intelligence; probabilistic graphical modelling; cross-modality learning; interpretable machine learning; unsupervised learning; semi-supervised learning
This collection is part of the Bioinformatics and Artificial Intelligence and Machine Learning Gateways.
Any questions about this collection? Please get in contact directly with research@f1000.com.
Since the completion of the Human Genome Project, ever-evolving biotechnologies have enabled us to deepen our understanding of our genetic code and that of other species. These datasets provide insights at unprecedented scale and depth, but utilizing their potential requires tackling challenges due to sparsity, high-dimensionality, cross-modality and the need for interpretability. Recently computational methods from the field of machine learning have provided tools for surmounting these challenges.
This article collection aims to promote the latest research applying machine learning approaches to advance genomic research. We welcome submissions of research involving the development and/or application of machine learning methods to genomic data sets including, but not limited to, genome sequencing, gene expression, single-cell genomics, functional genomics, or nuclear organization. Machine learning methods may include supervised, unsupervised or semi-supervised approaches based on neural networks, probabilistic graphical models, ensemble methods, convex optimization, or related approaches.
Keywords: genomics; epigenomics; chromatin organization; single-cell genomics; functional genomics; model organism genomics; gene expression; machine learning; deep learning; artificial intelligence; probabilistic graphical modelling; cross-modality learning; interpretable machine learning; unsupervised learning; semi-supervised learning
This collection is part of the Bioinformatics and Artificial Intelligence and Machine Learning Gateways.
Any questions about this collection? Please get in contact directly with research@f1000.com.
Collection Advisors
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Maxwell Libbrecht
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Jie Liu
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