AIMC Topic: Transcriptome
Clear Filters Showing 581 to 590 of 899 articles
Transfer learning with convolutional neural networks for cancer survival prediction using gene-expression data.
PloS one
Mar 26, 2020
Precision medicine in oncology aims at obtaining data from heterogeneous sources to have a precise estimation of a given patient's state and prognosis. With the purpose of advancing to personalized medicine framework, accurate diagnoses allow prescri...
Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning.
Scientific reports
Mar 5, 2020
Early detection of breast cancer and its correct stage determination are important for prognosis and rendering appropriate personalized clinical treatment to breast cancer patients. However, despite considerable efforts and progress, there is a need ...
Gene Ontology
RNA, Neoplasm
Humans
Machine Learning
Algorithms
Early Detection of Cancer
Transcriptome
Supervised Machine Learning
Neoplasm Staging
Female
Protein Interaction Maps
Models, Biological
Reproducibility of Results
Datasets as Topic
Breast Neoplasms
RNA-Seq
Carcinoma, Ductal, Breast
Databases, Genetic
Microarray Analysis
Network modeling of patients' biomolecular profiles for clinical phenotype/outcome prediction.
Scientific reports
Feb 27, 2020
Methods for phenotype and outcome prediction are largely based on inductive supervised models that use selected biomarkers to make predictions, without explicitly considering the functional relationships between individuals. We introduce a novel netw...
DeepTRIAGE: interpretable and individualised biomarker scores using attention mechanism for the classification of breast cancer sub-types.
BMC medical genomics
Feb 24, 2020
BACKGROUND: Breast cancer is a collection of multiple tissue pathologies, each with a distinct molecular signature that correlates with patient prognosis and response to therapy. Accurately differentiating between breast cancer sub-types is an import...
A genome-wide multiphenotypic association analysis identified candidate genes and gene ontology shared by four common risky behaviors.
Aging
Feb 22, 2020
BACKGROUND: Risky behaviors can lead to huge economic and health losses. However, limited efforts are paid to explore the genetic mechanisms of risky behaviors.
Hyper-Graph Regularized Constrained NMF for Selecting Differentially Expressed Genes and Tumor Classification.
IEEE journal of biomedical and health informatics
Feb 20, 2020
Non-negative Matrix Factorization (NMF) is a dimensionality reduction approach for learning a parts-based and linear representation of non-negative data. It has attracted more attention because of that. In practice, NMF not only neglects the manifold...
Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning-enabled molecular diagnostics.
EMBO molecular medicine
Feb 12, 2020
Limited therapy options due to antibiotic resistance underscore the need for optimization of current diagnostics. In some bacterial species, antimicrobial resistance can be unambiguously predicted based on their genome sequence. In this study, we seq...
Application of Machine Learning in Developing a Novelty Five-Pseudogene Signature to Predict Prognosis of Head and Neck Squamous Cell Carcinoma: A New Aspect of "Junk Genes" in Biomedical Practice.
DNA and cell biology
Feb 11, 2020
Head and neck squamous cell carcinoma (HNSCC) is the sixth malignancy, which is characterized by poor prognosis or high mortality because of the lack of predicting markers. Aberrant cancer pseudogenes have been found predictive for prognosis. We aim ...
Investigating the gene expression profiles of cells in seven embryonic stages with machine learning algorithms.
Genomics
Feb 8, 2020
The development of embryonic cells involves several continuous stages, and some genes are related to embryogenesis. To date, few studies have systematically investigated changes in gene expression profiles during mammalian embryogenesis. In this stud...
scAI: an unsupervised approach for the integrative analysis of parallel single-cell transcriptomic and epigenomic profiles.
Genome biology
Feb 3, 2020
Simultaneous measurements of transcriptomic and epigenomic profiles in the same individual cells provide an unprecedented opportunity to understand cell fates. However, effective approaches for the integrative analysis of such data are lacking. Here,...
Don't Miss the Future of Medicine
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 32091
- Neural Networks, Computer 30255
- Algorithms 27448
- Deep Learning 26951
- Artificial Intelligence 24880
- Robotics 13397
- Image Processing, Computer-Assisted 9750
- Retrospective Studies 9368
- Magnetic Resonance Imaging 6357
- Reproducibility of Results 5674
- Tomography, X-Ray Computed 4872
- Support Vector Machine 4776
- Computational Biology 4347
- Brain 4135
- Computer Simulation 3937
Recent Journals
- Scientific reports 6908
- Sensors (Basel, Switzerland) 4794
- PloS one 4228
- 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 1072
- Nature communications 1055
- Computer methods and programs in biomedicine 883
- Bioinformatics (Oxford, England) 879
- Journal of medical Internet research 871
- IEEE transactions on neural networks and learning systems 857