AIMC Topic: Transcriptome
Clear Filters Showing 461 to 470 of 899 articles
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...
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...
Research Design
Prospective Studies
Prognosis
Humans
Machine Learning
Transcriptome
Diagnosis, Computer-Assisted
Electrocardiography
Action Potentials
Retrospective Studies
Reproducibility of Results
Gene Expression Profiling
Workflow
Heart Rate
Predictive Value of Tests
Brugada Syndrome
Italy
Signal Processing, Computer-Assisted
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...
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...
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...
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...
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...
Non-invasive diagnostic tool for Parkinson's disease by sebum RNA profile with machine learning.
Scientific reports
Sep 20, 2021
Parkinson's disease (PD) is a progressive neurodegenerative disease presenting with motor and non-motor symptoms, including skin disorders (seborrheic dermatitis, bullous pemphigoid, and rosacea), skin pathological changes (decreased nerve endings an...
Machine Learning Modeling from Omics Data as Prospective Tool for Improvement of Inflammatory Bowel Disease Diagnosis and Clinical Classifications.
Genes
Sep 18, 2021
Research of inflammatory bowel disease (IBD) has identified numerous molecular players involved in the disease development. Even so, the understanding of IBD is incomplete, while disease treatment is still far from the precision medicine. Reliable di...
RDDSVM: accurate prediction of A-to-I RNA editing sites from sequence using support vector machines.
Functional & integrative genomics
Sep 16, 2021
Adenosine to inosine (A-to-I) editing in RNA is involved in various biological processes like gene expression, alternative splicing, and mRNA degradation associated with carcinogenesis and various human diseases. Therefore, accurate identification of...
Stay Ahead of Medical AI
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.
Subscribe NowPopular Topics
- Machine Learning 32072
- Neural Networks, Computer 30250
- Algorithms 27438
- Deep Learning 26937
- Artificial Intelligence 24871
- Robotics 13394
- Image Processing, Computer-Assisted 9748
- Retrospective Studies 9362
- Magnetic Resonance Imaging 6353
- Reproducibility of Results 5671
- Tomography, X-Ray Computed 4871
- Support Vector Machine 4774
- Computational Biology 4340
- Brain 4133
- Computer Simulation 3936
Recent Journals
- Scientific reports 6884
- Sensors (Basel, Switzerland) 4794
- PloS one 4220
- Neural networks : the official journal of the International Neural Network Society 3026
- Computational intelligence and neuroscience 1931
- Computers in biology and medicine 1920
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 1686
- Studies in health technology and informatics 1378
- IEEE journal of biomedical and health informatics 1181
- Journal of chemical information and modeling 1071
- Nature communications 1054
- Computer methods and programs in biomedicine 883
- Bioinformatics (Oxford, England) 877
- Journal of medical Internet research 869
- IEEE transactions on neural networks and learning systems 856