AIMC Topic: Gene Expression Profiling

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scDLC: a deep learning framework to classify large sample single-cell RNA-seq data.

BMC genomics Jul 12, 2022
BACKGROUND: Using single-cell RNA sequencing (scRNA-seq) data to diagnose disease is an effective technique in medical research. Several statistical methods have been developed for the classification of RNA sequencing (RNA-seq) data, including, for e...
Gene Expression Profiling Sequence Analysis, RNA Discriminant Analysis RNA Single-Cell Analysis RNA-Seq Deep Learning
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Deep learning-based microarray cancer classification and ensemble gene selection approach.

IET systems biology Jul 4, 2022
Malignancies and diseases of various genetic origins can be diagnosed and classified with microarray data. There are many obstacles to overcome due to the large size of the gene and the small number of samples in the microarray. A combination strateg...
Humans Algorithms Deep Learning Prostatic Neoplasms Oligonucleotide Array Sequence Analysis Male Gene Expression Profiling
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Cell cycle gene regulation dynamics revealed by RNA velocity and deep-learning.

Nature communications May 23, 2022
Despite the fact that the cell cycle is a fundamental process of life, a detailed quantitative understanding of gene regulation dynamics throughout the cell cycle is far from complete. Single-cell RNA-sequencing (scRNA-seq) technology gives access to...
RNA Genes, cdc Deep Learning Single-Cell Analysis Sequence Analysis, RNA Gene Expression Profiling Transcriptome
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Identifying common transcriptome signatures of cancer by interpreting deep learning models.

Genome biology May 17, 2022
BACKGROUND: Cancer is a set of diseases characterized by unchecked cell proliferation and invasion of surrounding tissues. The many genes that have been genetically associated with cancer or shown to directly contribute to oncogenesis vary widely bet...
Genomics Gene Expression Regulation, Neoplastic Neoplasms Transcriptome Gene Expression Profiling Humans Deep Learning
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One Cell At a Time (OCAT): a unified framework to integrate and analyze single-cell RNA-seq data.

Genome biology Apr 20, 2022
Integrative analysis of large-scale single-cell RNA sequencing (scRNA-seq) datasets can aggregate complementary biological information from different datasets. However, most existing methods fail to efficiently integrate multiple large-scale scRNA-se...
Exome Sequencing Sequence Analysis, RNA Cluster Analysis Machine Learning Algorithms Single-Cell Analysis RNA-Seq Gene Expression Profiling
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Identification of Novel Noninvasive Diagnostics Biomarkers in the Parkinson's Diseases and Improving the Disease Classification Using Support Vector Machine.

BioMed research international Mar 15, 2022
BACKGROUND: Parkinson's disease (PD) is a neurological disorder that is marked by the deficit of neurons in the midbrain that changes motor and cognitive function. In the substantia nigra, the selective demise of dopamine-producing neurons was the ma...
Computational Biology Biomarkers Humans Gene Expression Profiling Parkinson Disease Support Vector Machine Protein Interaction Maps Software
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Identification of Type 2 Diabetes Based on a Ten-Gene Biomarker Prediction Model Constructed Using a Support Vector Machine Algorithm.

BioMed research international Mar 4, 2022
BACKGROUND: Type 2 diabetes is a major health concern worldwide. The present study is aimed at discovering effective biomarkers for an efficient diagnosis of type 2 diabetes.
Protein Interaction Maps Gene Regulatory Networks Computational Biology Gene Expression Profiling Humans Support Vector Machine Diabetes Mellitus, Type 2 Algorithms Genetic Markers
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Augmentation of Transcriptomic Data for Improved Classification of Patients with Respiratory Diseases of Viral Origin.

International journal of molecular sciences Feb 24, 2022
To better understand the molecular basis of respiratory diseases of viral origin, high-throughput gene-expression data are frequently taken by means of DNA microarray or RNA-seq technology. Such data can also be useful to classify infected individual...
Transcriptome RNA-Seq Gene Ontology Reproducibility of Results Humans SARS-CoV-2 COVID-19 Algorithms Gene Expression Profiling Neural Networks, Computer Machine Learning
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Identification of a novel four-gene diagnostic signature for patients with sepsis by integrating weighted gene co-expression network analysis and support vector machine algorithm.

Hereditas Feb 21, 2022
Sepsis is a life-threatening condition in which the immune response is directed towards the host tissues, causing organ failure. Since sepsis does not present with specific symptoms, its diagnosis is often delayed. The lack of diagnostic accuracy res...
Humans Algorithms Gene Regulatory Networks Gene Expression Profiling Support Vector Machine Sepsis Gene Ontology
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Construction of genetic classification model for coronary atherosclerosis heart disease using three machine learning methods.

BMC cardiovascular disorders Feb 12, 2022
BACKGROUND: Although the diagnostic method for coronary atherosclerosis heart disease (CAD) is constantly innovated, CAD in the early stage is still missed diagnosis for the absence of any symptoms. The gene expression levels varied during disease de...
Databases, Genetic Predictive Value of Tests Coronary Artery Disease Models, Genetic Support Vector Machine Machine Learning Transcriptome Phenotype Genetic Predisposition to Disease Humans Gene Expression Profiling
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