AIMC Topic: Transcriptome
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Integration of machine learning and genome-scale metabolic modeling identifies multi-omics biomarkers for radiation resistance.
Nature communications
May 11, 2021
Resistance to ionizing radiation, a first-line therapy for many cancers, is a major clinical challenge. Personalized prediction of tumor radiosensitivity is not currently implemented clinically due to insufficient accuracy of existing machine learnin...
Schema: metric learning enables interpretable synthesis of heterogeneous single-cell modalities.
Genome biology
May 3, 2021
A complete understanding of biological processes requires synthesizing information across heterogeneous modalities, such as age, disease status, or gene expression. Technological advances in single-cell profiling have enabled researchers to assay mul...
Machine learning-assisted high-content analysis of pluripotent stem cell-derived embryos in vitro.
Stem cell reports
Apr 22, 2021
Stem cell-based embryo models by cultured pluripotent and extra-embryonic lineage stem cells are novel platforms to model early postimplantation development. We showed that induced pluripotent stem cells (iPSCs) could form ITS (iPSCs and trophectoder...
Iterative single-cell multi-omic integration using online learning.
Nature biotechnology
Apr 19, 2021
Integrating large single-cell gene expression, chromatin accessibility and DNA methylation datasets requires general and scalable computational approaches. Here we describe online integrative non-negative matrix factorization (iNMF), an algorithm for...
iRNA-m5U: A sequence based predictor for identifying 5-methyluridine modification sites in Saccharomyces cerevisiae.
Methods (San Diego, Calif.)
Apr 18, 2021
The 5-methyluridine (mU)modification plays important roles in a series of biological processes. Accurate identification of mU sites will be helpful to decode its biological functions. Although experimental techniques have been proposed to detect mU, ...
The Predictive Value of Monocytes in Immune Microenvironment and Prognosis of Glioma Patients Based on Machine Learning.
Frontiers in immunology
Apr 16, 2021
Gliomas are primary malignant brain tumors. Monocytes have been proved to actively participate in tumor growth. Weighted gene co-expression network analysis was used to identify meaningful monocyte-related genes for clustering. Neural network and SVM...
Leukocyte Count
Lymphocytes, Tumor-Infiltrating
Databases, Genetic
Molecular Sequence Annotation
Transcriptome
Humans
Reproducibility of Results
Immunotherapy
Machine Learning
ROC Curve
Monocytes
Prognosis
Tumor Microenvironment
Genomics
Treatment Outcome
Biomarkers, Tumor
Computational Biology
Glioma
Gene Expression Profiling
Machine learning, transcriptome, and genotyping chip analyses provide insights into SNP markers identifying flower color in Platycodon grandiflorus.
Scientific reports
Apr 13, 2021
Bellflower is an edible ornamental gardening plant in Asia. For predicting the flower color in bellflower plants, a transcriptome-wide approach based on machine learning, transcriptome, and genotyping chip analyses was used to identify SNP markers. S...
Predicting antimicrobial mechanism-of-action from transcriptomes: A generalizable explainable artificial intelligence approach.
PLoS computational biology
Mar 29, 2021
To better combat the expansion of antibiotic resistance in pathogens, new compounds, particularly those with novel mechanisms-of-action [MOA], represent a major research priority in biomedical science. However, rediscovery of known antibiotics demons...
Artificial neural networks for multi-omics classifications of hepato-pancreato-biliary cancers: towards the clinical application of genetic data.
European journal of cancer (Oxford, England : 1990)
Mar 26, 2021
PURPOSE: Several multi-omics classifications have been proposed for hepato-pancreato-biliary (HPB) cancers, but these classifications have not proven their role in the clinical practice and been validated in external cohorts.
Machine Learning Reduced Gene/Non-Coding RNA Features That Classify Schizophrenia Patients Accurately and Highlight Insightful Gene Clusters.
International journal of molecular sciences
Mar 25, 2021
RNA-seq has been a powerful method to detect the differentially expressed genes/long non-coding RNAs (lncRNAs) in schizophrenia (SCZ) patients; however, due to overfitting problems differentially expressed targets (DETs) cannot be used properly as bi...
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