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Gene Expression

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Multimodal Imaging under Artificial Intelligence Algorithm for the Diagnosis of Liver Cancer and Its Relationship with Expressions of EZH2 and p57.

Computational intelligence and neuroscience
OBJECTIVE: It aimed to explore the diagnostic efficacy of multimodal ultrasound images based on mask region with convolutional neural network (M-RCNN) segmentation algorithm for small liver cancer and analyze the expression of zeste gene enhancer hom...

Optimal Deep Learning Enabled Prostate Cancer Detection Using Microarray Gene Expression.

Journal of healthcare engineering
Prostate cancer is the main cause of death over the globe. Earlier detection and classification of cancer is highly important to improve patient health. Previous studies utilized statistical and machine learning (ML) techniques for prostate cancer de...

Performance Analysis of Deep Learning Models for Binary Classification of Cancer Gene Expression Data.

Journal of healthcare engineering
The classification of patients as cancer and normal patients by applying the computational methods on their gene expression profiles is an extremely important task. Recently, deep learning models, mainly multilayer perceptron and convolutional neural...

GSEnet: feature extraction of gene expression data and its application to Leukemia classification.

Mathematical biosciences and engineering : MBE
Gene expression data is highly dimensional. As disease-related genes account for only a tiny fraction, a deep learning model, namely GSEnet, is proposed to extract instructive features from gene expression data. This model consists of three modules, ...

GraphGONet: a self-explaining neural network encapsulating the Gene Ontology graph for phenotype prediction on gene expression.

Bioinformatics (Oxford, England)
MOTIVATION: Medical care is becoming more and more specific to patients' needs due to the increased availability of omics data. The application to these data of sophisticated machine learning models, in particular deep learning (DL), can improve the ...

Gene expression data classification using topology and machine learning models.

BMC bioinformatics
BACKGROUND: Interpretation of high-throughput gene expression data continues to require mathematical tools in data analysis that recognizes the shape of the data in high dimensions. Topological data analysis (TDA) has recently been successful in extr...

Machine learning phenomics (MLP) combining deep learning with time-lapse-microscopy for monitoring colorectal adenocarcinoma cells gene expression and drug-response.

Scientific reports
High-throughput phenotyping is becoming increasingly available thanks to analytical and bioinformatics approaches that enable the use of very high-dimensional data and to the availability of dynamic models that link phenomena across levels: from gene...

Prediction of lung cancer using gene expression and deep learning with KL divergence gene selection.

BMC bioinformatics
BACKGROUND: Lung cancer is one of the cancers with the highest mortality rate in China. With the rapid development of high-throughput sequencing technology and the research and application of deep learning methods in recent years, deep neural network...

Artificial intelligence and machine learning approaches using gene expression and variant data for personalized medicine.

Briefings in bioinformatics
Precision medicine uses genetic, environmental and lifestyle factors to more accurately diagnose and treat disease in specific groups of patients, and it is considered one of the most promising medical efforts of our time. The use of genetics is argu...