Machine learning in genomics

Reviews

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  Machine learning applications in genetics and genomics

  Machine learning methods are becoming increasingly important in the analysis of large-scale genomic, epigenomic, proteomic and metabolic data sets. In this Review, the authors consider the applications of supervised, semi-supervised and unsupervised machine learning methods to genetic and genomic studies. They provide general guidelines for the selection and application of algorithms that are best suited to particular study designs.

  • Maxwell W. Libbrecht
  • William Stafford Noble

  Review Article7 May 2015 Nature Reviews Genetics
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  Navigating the pitfalls of applying machine learning in genomics

  Machine learning is widely applied in various fields of genomics and systems biology. In this Review, the authors describe how responsible application of machine learning requires an understanding of several common pitfalls that users should be aware of (and mitigate) to avoid unreliable results.

  • Sean Whalen
  • Jacob Schreiber
  • Katherine S. Pollard

  Review Article26 Nov 2021 Nature Reviews Genetics
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  Deep learning: new computational modelling techniques for genomics

  This Review describes different deep learning techniques and how they can be applied to extract biologically relevant information from large, complex genomic data sets.

  • Gökcen Eraslan
  • Žiga Avsec
  • Fabian J. Theis

  Review Article10 Apr 2019 Nature Reviews Genetics
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  Obtaining genetics insights from deep learning via explainable artificial intelligence

  In this Review, the authors describe advances in deep learning approaches in genomics, whereby researchers are moving beyond the typical ‘black box’ nature of models to obtain biological insights through explainable artificial intelligence (xAI).

  • Gherman Novakovsky
  • Nick Dexter
  • Sara Mostafavi

  Review Article3 Oct 2022 Nature Reviews Genetics
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  Harnessing deep learning for population genetic inference

  Applying deep learning to large-scale genomic data of species or populations is providing new opportunities to understand the evolutionary forces that drive genetic diversity. This Review introduces common deep learning architectures and provides comprehensive guidelines to implement deep learning models for population genetic inference. The authors also discuss current opportunities and challenges for deep learning in population genetics.

  • Xin Huang
  • Aigerim Rymbekova
  • Martin Kuhlwilm

  Review Article4 Sep 2023 Nature Reviews Genetics
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  The diversification of methods for studying cell–cell interactions and communication

  In this Review, the authors summarize recent progress in cell–cell interaction (CCI) research. They describe the recent evolution in computational tools that underpin CCI studies, discuss improvements in experimental methods enabling more high-throughput analyses of CCIs, and highlight future directions for the field.

  • Erick Armingol
  • Hratch M. Baghdassarian
  • Nathan E. Lewis

  Review Article18 Jan 2024 Nature Reviews Genetics
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  Computational methods for analysing multiscale 3D genome organization

  In this Review, Zhang et al. discuss how recent advances in computational methods are helping to reveal the multiscale features involved in genome folding within the nucleus and how the resulting 3D genome organization relates to genome function.

  • Yang Zhang
  • Lorenzo Boninsegna
  • Jian Ma

  Review Article6 Sep 2023 Nature Reviews Genetics
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  Measuring biological age using omics data

  Molecular measures of biological ageing based on high-throughput omics technologies are enabling the quantitative characterization of ageing. The authors review how epigenomic, transcriptomic, proteomic, metabolomic and other omics data can be harnessed using machine learning to build ‘ageing clocks’.

  • Jarod Rutledge
  • Hamilton Oh
  • Tony Wyss-Coray

  Review Article17 Jun 2022 Nature Reviews Genetics
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  Decoding disease: from genomes to networks to phenotypes

  In this Review, the authors discuss computational methods for interpreting the molecular and clinical effects of genetic variants. They focus on methods leveraging machine learning, including those that characterize the effects on wider molecular networks.

  • Aaron K. Wong
  • Rachel S. G. Sealfon
  • Olga G. Troyanskaya

  Review Article2 Aug 2021 Nature Reviews Genetics