AIMC Topic: Transcriptome

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Prediction of tumor purity from gene expression data using machine learning.

Briefings in bioinformatics Nov 5, 2021
MOTIVATION: Bulk tumor samples used for high-throughput molecular profiling are often an admixture of cancer cells and non-cancerous cells, which include immune and stromal cells. The mixed composition can confound the analysis and affect the biologi...
Humans Gene Expression Profiling Transcriptome Artifacts DNA, Neoplasm Machine Learning DNA Contamination Reproducibility of Results Biomarkers, Tumor Neoplasms
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Machine learning revealed stemness features and a novel stemness-based classification with appealing implications in discriminating the prognosis, immunotherapy and temozolomide responses of 906 glioblastoma patients.

Briefings in bioinformatics Sep 2, 2021
Glioblastoma (GBM) is the most malignant and lethal intracranial tumor, with extremely limited treatment options. Immunotherapy has been widely studied in GBM, but none can significantly prolong the overall survival (OS) of patients without selection...
Glioblastoma Patient Selection Programmed Cell Death 1 Receptor Survival Analysis Temozolomide Antineoplastic Agents, Alkylating Atlases as Topic Immunotherapy Gene Expression Regulation, Neoplastic Treatment Outcome Neoplastic Stem Cells Male Middle Aged Gene Expression Profiling Transcriptome Humans Machine Learning Prognosis Adult Tumor Microenvironment Brain Neoplasms Female Aged
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DeepDRK: a deep learning framework for drug repurposing through kernel-based multi-omics integration.

Briefings in bioinformatics Sep 2, 2021
Recent pharmacogenomic studies that generate sequencing data coupled with pharmacological characteristics for patient-derived cancer cell lines led to large amounts of multi-omics data for precision cancer medicine. Among various obstacles hindering ...
Antineoplastic Agents Transcriptome Deep Learning Pharmacogenetics Software Precision Medicine Prognosis Neoplasms Cell Line, Tumor Computational Biology Drug Repositioning Datasets as Topic Humans
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Evaluation of machine learning approaches for cell-type identification from single-cell transcriptomics data.

Briefings in bioinformatics Sep 2, 2021
Single-cell transcriptomics technologies have vast potential in advancing our understanding of cellular heterogeneity in complex tissues. While methods to interpret single-cell transcriptomics data are developing rapidly, challenges in most analysis ...
Humans Cells, Cultured Phenotype Brain Benchmarking Mice Datasets as Topic Logistic Models Pancreas Support Vector Machine Lymphocytes, Tumor-Infiltrating Animals Leukocytes, Mononuclear Transcriptome Single-Cell Analysis
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A comprehensive overview and critical evaluation of gene regulatory network inference technologies.

Briefings in bioinformatics Sep 2, 2021
Gene regulatory network (GRN) is the important mechanism of maintaining life process, controlling biochemical reaction and regulating compound level, which plays an important role in various organisms and systems. Reconstructing GRN can help us to un...
Databases, Genetic Bayes Theorem Gene Expression Regulation Gene Regulatory Networks Computational Biology Models, Genetic Escherichia coli Biomarkers, Tumor Machine Learning Neoplasms Transcriptome RNA-Seq
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Machine learning-based integrative analysis of methylome and transcriptome identifies novel prognostic DNA methylation signature in uveal melanoma.

Briefings in bioinformatics Jul 20, 2021
Uveal melanoma (UVM) is the most common primary intraocular human malignancy with a high mortality rate. Aberrant DNA methylation has rapidly emerged as a diagnostic and prognostic signature in many cancers. However, such DNA methylation signature av...
Male Humans Middle Aged Disease-Free Survival Machine Learning Gene Expression Regulation, Neoplastic Aged DNA, Neoplasm Female Aged, 80 and over Gene Expression Profiling DNA Methylation Melanoma Uveal Neoplasms Adult Transcriptome Neoplasm Proteins Uveal Melanoma Survival Rate Predictive Value of Tests
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Unveiling the immune infiltrate modulation in cancer and response to immunotherapy by MIXTURE-an enhanced deconvolution method.

Briefings in bioinformatics Jul 20, 2021
The accurate quantification of tumor-infiltrating immune cells turns crucial to uncover their role in tumor immune escape, to determine patient prognosis and to predict response to immune checkpoint blockade. Current state-of-the-art methods that qua...
Gene Expression Regulation, Neoplastic Support Vector Machine Databases, Nucleic Acid Humans Models, Immunological Neoplasms Transcriptome Immunotherapy
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Deep-joint-learning analysis model of single cell transcriptome and open chromatin accessibility data.

Briefings in bioinformatics Jul 20, 2021
Simultaneous profiling transcriptomic and chromatin accessibility information in the same individual cells offers an unprecedented resolution to understand cell states. However, computationally effective methods for the integration of these inherent ...
Humans Databases, Factual Transcriptome Deep Learning Single-Cell Analysis Models, Biological K562 Cells Chromatin
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Machine learning application identifies novel gene signatures from transcriptomic data of spontaneous canine hemangiosarcoma.

Briefings in bioinformatics Jul 20, 2021
Angiosarcomas are soft-tissue sarcomas that form malignant vascular tissues. Angiosarcomas are very rare, and due to their aggressive behavior and high metastatic propensity, they have poor clinical outcomes. Hemangiosarcomas commonly occur in domest...
Hemangiosarcoma Gene Expression Regulation, Neoplastic Dogs Dog Diseases Machine Learning Databases, Nucleic Acid Animals Gene Expression Profiling Transcriptome
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Discovery of molecular features underlying the morphological landscape by integrating spatial transcriptomic data with deep features of tissue images.

Nucleic acids research Jun 4, 2021
Profiling molecular features associated with the morphological landscape of tissue is crucial for investigating the structural and spatial patterns that underlie the biological function of tissues. In this study, we present a new method, spatial gene...
Deep Learning Breast Neoplasms Humans Image Processing, Computer-Assisted Animals Biomarkers Male Mice Brain Transcriptome Prostatic Neoplasms Female
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