AIMC Topic: Transcriptome
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Unveiling new disease, pathway, and gene associations via multi-scale neural network.
PloS one
Apr 6, 2020
Diseases involve complex modifications to the cellular machinery. The gene expression profile of the affected cells contains characteristic patterns linked to a disease. Hence, new biological knowledge about a disease can be extracted from these prof...
Deep learning-based cancer survival prognosis from RNA-seq data: approaches and evaluations.
BMC medical genomics
Apr 3, 2020
BACKGROUND: Recent advances in kernel-based Deep Learning models have introduced a new era in medical research. Originally designed for pattern recognition and image processing, Deep Learning models are now applied to survival prognosis of cancer pat...
Estimating gene expression from DNA methylation and copy number variation: A deep learning regression model for multi-omics integration.
Genomics
Mar 29, 2020
Gene expression analysis plays a significant role for providing molecular insights in cancer. Various genetic and epigenetic factors (being dealt under multi-omics) affect gene expression giving rise to cancer phenotypes. A recent growth in understan...
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 ...
Humans
Machine Learning
Algorithms
Female
Reproducibility of Results
Breast Neoplasms
Datasets as Topic
RNA, Neoplasm
Early Detection of Cancer
Transcriptome
Carcinoma, Ductal, Breast
Neoplasm Staging
Microarray Analysis
Protein Interaction Maps
Models, Biological
Gene Ontology
RNA-Seq
Supervised Machine Learning
Databases, Genetic
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...
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