AIMC Topic: Transcriptome

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Construction of a 5-feature gene model by support vector machine for classifying osteoporosis samples.

Bioengineered
Osteoporosis is a progressive bone disease in the elderly and lacks an effective classification method of patients. This study constructed a gene signature for an accurate prediction and classification of osteoporosis patients. Three gene expression ...

A nine-hub-gene signature of metabolic syndrome identified using machine learning algorithms and integrated bioinformatics.

Bioengineered
Early risk assessments and interventions for metabolic syndrome (MetS) are limited because of a lack of effective biomarkers. In the present study, several candidate genes were selected as a blood-based transcriptomic signature for MetS. We collected...

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

Briefings in bioinformatics
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...

DeepDRK: a deep learning framework for drug repurposing through kernel-based multi-omics integration.

Briefings in bioinformatics
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 ...

Evaluation of machine learning approaches for cell-type identification from single-cell transcriptomics data.

Briefings in bioinformatics
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 ...

A comprehensive overview and critical evaluation of gene regulatory network inference technologies.

Briefings in bioinformatics
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...

Machine learning-based integrative analysis of methylome and transcriptome identifies novel prognostic DNA methylation signature in uveal melanoma.

Briefings in bioinformatics
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...

Unveiling the immune infiltrate modulation in cancer and response to immunotherapy by MIXTURE-an enhanced deconvolution method.

Briefings in bioinformatics
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...

Deep-joint-learning analysis model of single cell transcriptome and open chromatin accessibility data.

Briefings in bioinformatics
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 ...