ArticleBioinformatics (Oxford, England)2012
Predicting the lethal phenotype of the knockout mouse by integrating comprehensive genomic data.
Article in Bioinformatics (Oxford, England), 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- EssTFNet: integration of adaptive time-frequency and DNA language models for interpretable human essential gene prediction.Briefings in bioinformatics · 2026Article
- A machine learning classifier to identify and prioritise genes associated with murine cardiac development.PLoS genetics · 2026Article
- Lecithin-cholesterol acyltransferase is a potential tumor suppressor and predictive marker for hepatocellular carcinoma metastasis.World journal of gastrointestinal oncology · 2024Article
- Essential genes identification model based on sequence feature map and graph convolutional neural network.BMC genomics · 2024Article
- Maize Feature Store: A centralized resource to manage and analyze curated maize multi-omics features for machine learning applications.Database : the journal of biological databases and curation · 2023Article
- Predicting congenital renal tract malformation genes using machine learning.Scientific reports · 2023Article
- Identification of discriminant features from stationary pattern of nucleotide bases and their application to essential gene classification.Frontiers in genetics · 2023Article
- Characterization of plasma metabolites and proteins in patients with herpetic neuralgia and development of machine learning predictive models based on metabolomic profiling.Frontiers in molecular neuroscience · 2022Article
- Immunogenomic Landscape in Breast Cancer Reveals Immunotherapeutically Relevant Gene Signatures.Frontiers in immunology · 2022Article
- Comprehensive Analysis and Identification of an Immune-Related Gene Signature with Prognostic Value for Prostate Cancer.International journal of general medicine · 2021Article
- A Computational Framework Based on Ensemble Deep Neural Networks for Essential Genes Identification.International journal of molecular sciences · 2020Article
- A holistic view of mouse enhancer architectures reveals analogous pleiotropic effects and correlation with human disease.BMC genomics · 2020Article
- Network Embedding the Protein-Protein Interaction Network for Human Essential Genes Identification.Genes · 2020Article
- Prognostic Value of Immune-Related Genes in the Tumor Microenvironment of Bladder Cancer.Frontiers in oncology · 2020Article
- An Evaluation of Machine Learning Approaches for the Prediction of Essential Genes in Eukaryotes Using Protein Sequence-Derived Features.Computational and structural biotechnology journal · 2019Article
- Identifying mouse developmental essential genes using machine learning.Disease models & mechanisms · 2018Article
- Accurate prediction of human essential genes using only nucleotide composition and association information.Bioinformatics (Oxford, England) · 2017Article
- Properties of genes essential for mouse development.PloS one · 2017Article
- Characteristics of Plant Essential Genes Allow for within- and between-Species Prediction of Lethal Mutant Phenotypes.The Plant cell · 2015Article
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Authors and funding
5 authors.
Funding
Abstract
motivationThe phenotypes of knockout mice provide crucial information for understanding the biological functions of mammalian genes. Among various knockout phenotypes, lethality is of great interest because those involved genes play essential roles. With the availability of large-scale genomic data, we aimed to assess how well the integration of various genomic features can predict the lethal phenotype of single-gene knockout mice.
resultsWe first assembled a comprehensive list of 491 candidate genomic features derived from diverse data sources. Using mouse genes with a known phenotype as the training set, we integrated the informative genomic features to predict the knockout lethality through three machine learning methods. Based on cross-validation, our models could achieve a good performance (accuracy = 73% and recall = 63%). Our results serve as a valuable practical resource in the mouse genetics research community, and also accelerate the translation of the knowledge of mouse genes into better strategies for studying human disease.
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