AIMC Topic: Transcriptome
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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...
Molecular Sequence Annotation
Reproducibility of Results
Leukocyte Count
Transcriptome
Glioma
Tumor Microenvironment
Computational Biology
ROC Curve
Humans
Lymphocytes, Tumor-Infiltrating
Immunotherapy
Genomics
Biomarkers, Tumor
Machine Learning
Databases, Genetic
Monocytes
Gene Expression Profiling
Prognosis
Treatment Outcome
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...
A first perturbome of Pseudomonas aeruginosa: Identification of core genes related to multiple perturbations by a machine learning approach.
Bio Systems
Mar 20, 2021
Tolerance to stress conditions is vital for organismal survival, including bacteria under specific environmental conditions, antibiotics, and other perturbations. Some studies have described common modulation and shared genes during stress response t...
Deep generative neural network for accurate drug response imputation.
Nature communications
Mar 19, 2021
Drug response differs substantially in cancer patients due to inter- and intra-tumor heterogeneity. Particularly, transcriptome context, especially tumor microenvironment, has been shown playing a significant role in shaping the actual treatment outc...
Integrated multi-omics analysis of ovarian cancer using variational autoencoders.
Scientific reports
Mar 18, 2021
Cancer is a complex disease that deregulates cellular functions at various molecular levels (e.g., DNA, RNA, and proteins). Integrated multi-omics analysis of data from these levels is necessary to understand the aberrant cellular functions accountab...
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