AIMC Topic: Gene Expression

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Unsupervised generative and graph representation learning for modelling cell differentiation.

Scientific reports Jun 17, 2020
Using machine learning techniques to build representations from biomedical data can help us understand the latent biological mechanism of action and lead to important discoveries. Recent developments in single-cell RNA-sequencing protocols have allow...
Humans Machine Learning Cell Differentiation Gene Expression Animals Models, Biological Datasets as Topic RNA-Seq Models, Statistical
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Data reduction and data visualization for automatic diagnosis using gene expression and clinical data.

Artificial intelligence in medicine May 28, 2020
Accurate diagnoses of specific diseases require, in general, the review of the whole medical history of a patient. Currently, even though many advances have been made for disease monitoring, domain experts are still requested to perform direct analys...
Humans Neural Networks, Computer Gene Expression Data Visualization
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Uncovering the prognostic gene signatures for the improvement of risk stratification in cancers by using deep learning algorithm coupled with wavelet transform.

BMC bioinformatics May 19, 2020
BACKGROUND: The aim of gene expression-based clinical modelling in tumorigenesis is not only to accurately predict the clinical endpoints, but also to reveal the genome characteristics for downstream analysis for the purpose of understanding the mech...
Algorithms Prognosis Risk Assessment Humans Neoplasms Gene Expression Deep Learning Proportional Hazards Models Support Vector Machine Wavelet Analysis
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Putative cell type discovery from single-cell gene expression data.

Nature methods May 18, 2020
We present the Single-Cell Clustering Assessment Framework, a method for the automated identification of putative cell types from single-cell RNA sequencing (scRNA-seq) data. By iteratively applying a machine learning approach to a given set of cells...
Datasets as Topic RNA-Seq Cluster Analysis Gene Expression Humans Machine Learning Software Reproducibility of Results Animals Single-Cell Analysis
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Artificial Intelligence Analysis of Gene Expression Data Predicted the Prognosis of Patients with Diffuse Large B-Cell Lymphoma.

The Tokai journal of experimental and clinical medicine Apr 20, 2020
OBJECTIVE: We aimed to identify new biomarkers in Diffuse Large B-cell Lymphoma (DLBCL) using the deep learning technique.
Databases, Genetic Gene Expression Data Analysis Humans Male Forecasting Female Artificial Intelligence Middle Aged Lymphoma, Large B-Cell, Diffuse Prognosis
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Avocado: a multi-scale deep tensor factorization method learns a latent representation of the human epigenome.

Genome biology Mar 30, 2020
The human epigenome has been experimentally characterized by thousands of measurements for every basepair in the human genome. We propose a deep neural network tensor factorization method, Avocado, that compresses this epigenomic data into a dense, i...
Enhancer Elements, Genetic Gene Expression DNA Replication Timing Epigenome Promoter Regions, Genetic Humans Deep Learning Genomics
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Graph embedding and unsupervised learning predict genomic sub-compartments from HiC chromatin interaction data.

Nature communications Mar 3, 2020
Chromatin interaction studies can reveal how the genome is organized into spatially confined sub-compartments in the nucleus. However, accurately identifying sub-compartments from chromatin interaction data remains a challenge in computational biolog...
Chromatin Epigenome K562 Cells Computer Graphics Reproducibility of Results Genomics Cluster Analysis Unsupervised Machine Learning Gene Expression Markov Chains Neural Networks, Computer Data Analysis Algorithms Humans
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RefDNN: a reference drug based neural network for more accurate prediction of anticancer drug resistance.

Scientific reports Feb 5, 2020
Cancer is one of the most difficult diseases to treat owing to the drug resistance of tumour cells. Recent studies have revealed that drug responses are closely associated with genomic alterations in cancer cells. Numerous state-of-the-art machine le...
Precision Medicine Neoplasms Antineoplastic Agents Cell Line, Tumor Genomics Computational Biology Neural Networks, Computer Machine Learning Computer Simulation Humans Drug Resistance, Neoplasm Drug Repositioning Gene Expression Genetic Markers
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Machine learning with autophagy-related proteins for discriminating renal cell carcinoma subtypes.

Scientific reports Jan 20, 2020
Machine learning techniques have been previously applied for classification of tumors based largely on morphological features of tumor cells recognized in H&E images. Here, we tested the possibility of using numeric data acquired from software-based ...
Autophagy-Related Proteins Autophagy-Related Protein 5 Autophagy-Related Protein-1 Homolog Microtubule-Associated Proteins Carcinoma, Renal Cell Intracellular Signaling Peptides and Proteins Humans Machine Learning RNA, Messenger Gene Expression Autophagy Kidney Neoplasms
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Logistic regression paradigm for training a single-hidden layer feedforward neural network. Application to gene expression datasets for cancer research.

Journal of biomedical informatics Dec 31, 2019
OBJECTIVE: The speed of the diagnosis process is vital in pursuing the trial of curing cancer. During the last decade, precision medicine evolved by detecting different types of cancer through microarrays (MA) of deoxyribonucleic acid (DNA) processed...
Neoplasms Logistic Models Gene Expression Neural Networks, Computer Machine Learning Algorithms
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