Collection

Artificial intelligence in genomics

Submission status: Open
Submission deadline: 12 October 2024

Artificial intelligence (AI) has emerged as a powerful platform to streamline the analysis of complex genomic datasets and help researchers explore the genetic basis of important diseases or phenotypes. In a new Call for Papers, Communications Biology, Nature Communications and Scientific Reports are interested in submissions that highlight the possibilities offered by AI approaches to improve genomics research, regardless of the model system.

Original research Articles can report novel methods that involve AI-based algorithms or focus on specific applications of machine or deep learning to identify or prioritise genes and variants associated with a particular trait. Communications Biology and Nature Communications will also consider Reviews, Perspectives, and Comments that are relevant to these topics, though all submissions will be subject to the same peer review process and editorial standards as regular manuscripts considered at the respective journals.

To submit, see the participating journals

Editors

Kaoru Ito, PhD

Kaoru Ito, PhD

RIKEN Center for Integrative Medical Sciences, Japan

Articles

A metagenomic analysis of the phase 2 Anopheles gambiae 1000 genomes dataset reveals a wide diversity of cobionts associated with field collected mosquitoes

A metagenomic analysis of 1,142 field-collected Anopheles gambiae mosquito specimens by the Microsoft Premonition Bayesian mixture model pipeline revealed a diverse set of vertebrate hosts, as well as the presence of Plasmodium parasites and other microbes.
- Andrzej Pastusiak
- Michael R. Reddy
- Ethan K. Jackson
ArticleOpen Access30 May 2024 Communications Biology
Supervised latent factor modeling isolates cell-type-specific transcriptomic modules that underlie Alzheimer’s disease progression

Designing a supervised latent factor framework for snRNA-seq human brain, the authors find distinct Alzheimer’s-predictive gene modules across celltypes, suggesting subcelltype disease progression trajectories.
- Liam Hodgson
- Yue Li
- Danilo Bzdok
ArticleOpen Access17 May 2024 Communications Biology
Unsupervised deep representation learning enables phenotype discovery for genetic association studies of brain imaging

A study utilizing unsupervised deep learning to generate interpretable brain imaging phenotypes from brain T1 and T2-FLAIR MRI identified 97 genetic loci enhancing understanding of brain structure genetics.
- Khush Patel
- Ziqian Xie
- Degui Zhi
ArticleOpen Access5 Apr 2024 Communications Biology
A platform-independent AI tumor lineage and site (ATLAS) classifier

ATLAS is a pair of AI Tumor Lineage and Site-of-origin machine learning models, that can accurately classify both primary and metastatic tumors using high-throughput RNA expression data and can identify de-differentiated anaplastic tumors.
- Nicholas R. Rydzewski
- Yue Shi
- Shuang G. Zhao
ArticleOpen Access13 Mar 2024 Communications Biology
Gene-SGAN: discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering

Many diseases can display distinct brain imaging phenotypes across individuals, potentially reflecting disease subtypes. However, biological interpretability is limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Here, the authors describe a deep-learning method that links imaging phenotypes with genetic factors, thereby conferring genetic correlations to the disease subtypes.
- Zhijian Yang
- Junhao Wen
- Christos Davatzikos
ArticleOpen Access8 Jan 2024 Nature Communications
PhenoSV: interpretable phenotype-aware model for the prioritization of genes affected by structural variants

Here, authors present PhenoSV, a phenotype-aware machine-learning model for the functional interpretation of various types of structural variants (SVs) and genes within or outside SVs, facilitating the extraction of biological insights from coding and noncoding SVs.
- Zhuoran Xu
- Quan Li
- Kai Wang
ArticleOpen Access28 Nov 2023 Nature Communications
Novel Alzheimer’s disease genes and epistasis identified using machine learning GWAS platform
- Mischa Lundberg
- Letitia M. F. Sng
- Natalie A. Twine
ArticleOpen Access17 Oct 2023 Scientific Reports
Deep learning of human polyadenylation sites at nucleotide resolution reveals molecular determinants of site usage and relevance in disease

The authors develop deep learning models to identify genome-wide polyA sites at nucleotide resolution and calculate site strength. They further examine genomic parameters regulating site usage and reveal genetic variants altering polyA activity.
- Emily Kunce Stroup
- Zhe Ji
ArticleOpen Access15 Nov 2023 Nature Communications
Genome interpretation in a federated learning context allows the multi-center exome-based risk prediction of Crohn’s disease patients
- Daniele Raimondi
- Haleh Chizari
- Yves Moreau
ArticleOpen Access9 Nov 2023 Scientific Reports
Adversarial and variational autoencoders improve metagenomic binning

AVAMB is a deep learning ensemble approach for metagenomics binning that achieves state-of-the-art binning performance and increased taxonomic diversity on both synthetic and real datasets.
- Pau Piera Líndez
- Joachim Johansen
- Simon Rasmussen
ArticleOpen Access21 Oct 2023 Communications Biology
Improving genetic risk prediction across diverse population by disentangling ancestry representations

A deep-learning framework leverages data from diverse populations and disentangles ancestry from the phenotype-relevant information in its representation.
- Prashnna K. Gyawali
- Yann Le Guen
- Zihuai He
ArticleOpen Access22 Sep 2023 Communications Biology
Combination of expert guidelines-based and machine learning-based approaches leads to superior accuracy of automated prediction of clinical effect of copy number variations
- Tomáš Sládeček
- Michaela Gažiová
- Tomáš Szemes
ArticleOpen Access29 Jun 2023 Scientific Reports

Artificial intelligence in genomics

Editors

Articles

A metagenomic analysis of the phase 2 Anopheles gambiae 1000 genomes dataset reveals a wide diversity of cobionts associated with field collected mosquitoes

Supervised latent factor modeling isolates cell-type-specific transcriptomic modules that underlie Alzheimer’s disease progression

Unsupervised deep representation learning enables phenotype discovery for genetic association studies of brain imaging

A platform-independent AI tumor lineage and site (ATLAS) classifier

Gene-SGAN: discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering

PhenoSV: interpretable phenotype-aware model for the prioritization of genes affected by structural variants

Novel Alzheimer’s disease genes and epistasis identified using machine learning GWAS platform

Deep learning of human polyadenylation sites at nucleotide resolution reveals molecular determinants of site usage and relevance in disease

Genome interpretation in a federated learning context allows the multi-center exome-based risk prediction of Crohn’s disease patients

Adversarial and variational autoencoders improve metagenomic binning

Improving genetic risk prediction across diverse population by disentangling ancestry representations

Combination of expert guidelines-based and machine learning-based approaches leads to superior accuracy of automated prediction of clinical effect of copy number variations

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Artificial intelligence in genomics

Editors

Kaoru Ito, PhD

Articles

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