AIMC Topic: Gene Expression Profiling
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Comparison of machine-learning methodologies for accurate diagnosis of sepsis using microarray gene expression data.
PloS one
May 17, 2021
We investigate the feasibility of molecular-level sample classification of sepsis using microarray gene expression data merged by in silico meta-analysis. Publicly available data series were extracted from NCBI Gene Expression Omnibus and EMBL-EBI Ar...
Identification of potential genomic biomarkers for Parkinson's disease using data pooling of gene expression microarrays.
Biomarkers in medicine
May 14, 2021
In this study, we aimed to identify potential diagnostic biomarkers Parkinson's disease (PD) by exploring microarray gene expression data of PD patients. Differentially expressed genes associated with PD were screened from the GSE99039 dataset usin...
Overcoming false-positive gene-category enrichment in the analysis of spatially resolved transcriptomic brain atlas data.
Nature communications
May 11, 2021
Transcriptomic atlases have improved our understanding of the correlations between gene-expression patterns and spatially varying properties of brain structure and function. Gene-category enrichment analysis (GCEA) is a common method to identify func...
Integrating multi-omics data through deep learning for accurate cancer prognosis prediction.
Computers in biology and medicine
May 9, 2021
BACKGROUND: Genomic information is nowadays widely used for precise cancer treatments. Since the individual type of omics data only represents a single view that suffers from data noise and bias, multiple types of omics data are required for accurate...
Gene selection using hybrid dragonfly black hole algorithm: A case study on RNA-seq COVID-19 data.
Analytical biochemistry
May 8, 2021
This paper introduces a new hybrid approach (DBH) for solving gene selection problem that incorporates the strengths of two existing metaheuristics: binary dragonfly algorithm (BDF) and binary black hole algorithm (BBHA). This hybridization aims to i...
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...
Distinguishing Rectal Cancer from Colon Cancer Based on the Support Vector Machine Method and RNA-sequencing Data.
Current medical science
Apr 20, 2021
Colorectal cancer (CRC) is the third most commonly diagnosed cancer worldwide. Several studies have indicated that rectal cancer is significantly different from colon cancer in terms of treatment, prognosis, and metastasis. Recently, the differential...
An approach using ddRADseq and machine learning for understanding speciation in Antarctic Antarctophilinidae gastropods.
Scientific reports
Apr 19, 2021
Sampling impediments and paucity of suitable material for molecular analyses have precluded the study of speciation and radiation of deep-sea species in Antarctica. We analyzed barcodes together with genome-wide single nucleotide polymorphisms obtain...
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
Transcriptome
ROC Curve
Prognosis
Genomics
Glioma
Humans
Tumor Microenvironment
Immunotherapy
Computational Biology
Lymphocytes, Tumor-Infiltrating
Machine Learning
Biomarkers, Tumor
Reproducibility of Results
Databases, Genetic
Monocytes
Gene Expression Profiling
Molecular Sequence Annotation
Treatment Outcome
Investigating heterogeneities of live mesenchymal stromal cells using AI-based label-free imaging.
Scientific reports
Mar 24, 2021
Mesenchymal stromal cells (MSCs) are multipotent cells that have great potential for regenerative medicine, tissue repair, and immunotherapy. Unfortunately, the outcomes of MSC-based research and therapies can be highly inconsistent and difficult to ...
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