AIMC Topic: Transcriptome

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The viral expression and immune status in human cancers and insights into novel biomarkers of immunotherapy.

BMC cancer Nov 5, 2021
BACKGROUND: Viral infections are prevalent in human cancers and they have great diagnostic and theranostic values in clinical practice. Recently, their potential of shaping the tumor immune microenvironment (TIME) has been related to the immunotherap...
Humans Machine Learning Kaplan-Meier Estimate Papillomaviridae Oncogenic Viruses Genetic Variation High-Throughput Nucleotide Sequencing Genome, Viral Tumor Virus Infections Squamous Cell Carcinoma of Head and Neck Head and Neck Neoplasms Support Vector Machine DNA, Viral Epstein-Barr Virus Infections Herpesvirus 4, Human Mutation Transcriptome Leukocytes RNA-Seq Immunotherapy Papillomavirus Infections Stomach Neoplasms Tumor Microenvironment Biomarkers, Tumor Neoplasms
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Construction and external validation of a 5-gene random forest model to diagnose non-obstructive azoospermia based on the single-cell RNA sequencing of testicular tissue.

Aging Nov 4, 2021
Non-obstructive azoospermia (NOA) is among the most severe factors for male infertility, but our understandings of the latent biological mechanisms remain insufficient. The single-cell RNA sequencing (scRNA-seq) data of 432 testicular cells isolated ...
Humans Diagnosis, Computer-Assisted Single-Cell Analysis Protein Interaction Maps RNA-Seq Azoospermia Machine Learning Adult Male Transcriptome
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Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram.

Nature methods Oct 28, 2021
Charting an organs' biological atlas requires us to spatially resolve the entire single-cell transcriptome, and to relate such cellular features to the anatomical scale. Single-cell and single-nucleus RNA-seq (sc/snRNA-seq) can profile cells comprehe...
Software Brain Gene Expression Regulation Female Male Regulatory Sequences, Nucleic Acid Animals Chromatin Gene Expression Profiling Transcriptome Deep Learning Single-Cell Analysis RNA-Seq Mice Mice, Inbred C57BL
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Manipulating cellular microRNAs and analyzing high-dimensional gene expression data using machine learning workflows.

STAR protocols Oct 23, 2021
MicroRNAs (miRNAs) are elements of the gene regulatory network and manipulating their abundance is essential toward elucidating their role in patho-physiological conditions. We present a detailed workflow that identifies important miRNAs using a mach...
Workflow Cell Culture Techniques Gene Knockdown Techniques Islets of Langerhans Gene Expression Profiling Transcriptome Gene Regulatory Networks MicroRNAs Humans Machine Learning Algorithms Cells, Cultured
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The BrAID study protocol: integration of machine learning and transcriptomics for brugada syndrome recognition.

BMC cardiovascular disorders Oct 13, 2021
BACKGROUND: Type 1 Brugada syndrome (BrS) is a hereditary arrhythmogenic disease showing peculiar electrocardiographic (ECG) patterns, characterized by ST-segment elevation in the right precordial leads, and risk of Sudden Cardiac Death (SCD). Furthe...
Brugada Syndrome Italy Signal Processing, Computer-Assisted Workflow Heart Rate Diagnosis, Computer-Assisted Electrocardiography Action Potentials Predictive Value of Tests Machine Learning Research Design Prospective Studies Humans Retrospective Studies Prognosis Reproducibility of Results Gene Expression Profiling Transcriptome
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Artificial image objects for classification of breast cancer biomarkers with transcriptome sequencing data and convolutional neural network algorithms.

Breast cancer research : BCR Oct 10, 2021
BACKGROUND: Transcriptome sequencing has been broadly available in clinical studies. However, it remains a challenge to utilize these data effectively for clinical applications due to the high dimension of the data and the highly correlated expressio...
Ki-67 Antigen Transcriptome Databases, Genetic Survival Analysis Image Interpretation, Computer-Assisted Humans Neural Networks, Computer Algorithms Female Reproducibility of Results Breast Neoplasms Biomarkers, Tumor
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Prediction of Essential Genes in Comparison States Using Machine Learning.

IEEE/ACM transactions on computational biology and bioinformatics Oct 7, 2021
Identifying essential genes in comparison states (EGS) is vital to understanding cell differentiation, performing drug discovery, and identifying disease causes. Here, we present a machine learning method termed Prediction of Essential Genes in Compa...
Gene Regulatory Networks Computational Biology Genes, Essential Genetic Markers Machine Learning Transcriptome
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MISSIM: An Incremental Learning-Based Model With Applications to the Prediction of miRNA-Disease Association.

IEEE/ACM transactions on computational biology and bioinformatics Oct 7, 2021
In the past few years, the prediction models have shown remarkable performance in most biological correlation prediction tasks. These tasks traditionally use a fixed dataset, and the model, once trained, is deployed as is. These models often encounte...
Humans Machine Learning Algorithms Neoplasms Transcriptome Genetic Predisposition to Disease Computational Biology MicroRNAs
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DeepCellState: An autoencoder-based framework for predicting cell type specific transcriptional states induced by drug treatment.

PLoS computational biology Oct 5, 2021
Drug treatment induces cell type specific transcriptional programs, and as the number of combinations of drugs and cell types grows, the cost for exhaustive screens measuring the transcriptional drug response becomes intractable. We developed DeepCel...
Humans Antineoplastic Agents Transcriptome Gene Knockdown Techniques PC-3 Cells Deep Learning Computational Biology Unsupervised Machine Learning MCF-7 Cells
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Evolutionarily informed machine learning enhances the power of predictive gene-to-phenotype relationships.

Nature communications Sep 24, 2021
Inferring phenotypic outcomes from genomic features is both a promise and challenge for systems biology. Using gene expression data to predict phenotypic outcomes, and functionally validating the genes with predictive powers are two challenges we add...
Nitrogen Genetic Variation Species Specificity Genome, Plant Genotype Machine Learning Gene Expression Regulation, Plant Models, Genetic Phenotype Arabidopsis Evolution, Molecular Transcriptome Genomics Zea mays
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