AIMC Topic: Gene Expression

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Improved prediction of gene expression through integrating cell signalling models with machine learning.

BMC bioinformatics Aug 6, 2022
BACKGROUND: A key problem in bioinformatics is that of predicting gene expression levels. There are two broad approaches: use of mechanistic models that aim to directly simulate the underlying biology, and use of machine learning (ML) to empirically ...
Neoplasms Computational Biology Gene Expression Humans Machine Learning
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Use of SVM-based ensemble feature selection method for gene expression data analysis.

Statistical applications in genetics and molecular biology Jul 14, 2022
Gene selection is one of the key steps for gene expression data analysis. An SVM-based ensemble feature selection method is proposed in this paper. Firstly, the method builds many subsets by using Monte Carlo sampling. Secondly, ranking all the featu...
Support Vector Machine Gene Expression Algorithms Humans Male Data Analysis
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Assessment of deep learning and transfer learning for cancer prediction based on gene expression data.

BMC bioinformatics Jul 3, 2022
BACKGROUND: Machine learning is now a standard tool for cancer prediction based on gene expression data. However, deep learning is still new for this task, and there is no clear consensus about its performance and utility. Few experimental works have...
Deep Learning Gene Expression Machine Learning Neoplasms Humans Neural Networks, Computer
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Influence of Garlic (Allium sativum) Clove-Based Selenium Nanoparticles on Status of Nutritional, Biochemical, Enzymological, and Gene Expressions in the Freshwater Prawn Macrobrachium rosenbergii (De Man, 1879).

Biological trace element research Jun 3, 2022
Selenium (Se) is one of the essential micronutrients for performing vital body functions. This study aims at examining the influence of dietary supplementation of garlic clove-based green-synthesized selenium nanoparticles (GBGS-SeNPs, 48-87 nm) on c...
Transaminases Antioxidants Gene Expression Trace Elements Animals Palaemonidae Nanoparticles Syzygium Selenium Garlic
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Gene expression data classification using topology and machine learning models.

BMC bioinformatics May 20, 2022
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...
Gene Expression Machine Learning Data Analysis
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Machine learning phenomics (MLP) combining deep learning with time-lapse-microscopy for monitoring colorectal adenocarcinoma cells gene expression and drug-response.

Scientific reports May 20, 2022
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...
Phenomics Microscopy Time-Lapse Imaging Colorectal Neoplasms Deep Learning Machine Learning Adenocarcinoma Phenotype Gene Expression Humans
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Feature Subset Selection with Optimal Adaptive Neuro-Fuzzy Systems for Bioinformatics Gene Expression Classification.

Computational intelligence and neuroscience May 14, 2022
Recently, bioinformatics and computational biology-enabled applications such as gene expression analysis, cellular restoration, medical image processing, protein structure examination, and medical data classification utilize fuzzy systems in offering...
Gene Expression Artificial Intelligence Computational Biology Algorithms Animals Fuzzy Logic Male
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Prediction of lung cancer using gene expression and deep learning with KL divergence gene selection.

BMC bioinformatics May 12, 2022
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...
Humans China Lung Neoplasms Gene Expression Neural Networks, Computer Deep Learning
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Integrating Long-Range Regulatory Interactions to Predict Gene Expression Using Graph Convolutional Networks.

Journal of computational biology : a journal of computational molecular cell biology Mar 21, 2022
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Epigenesis, Genetic Genome Gene Expression Humans Neural Networks, Computer Genomics
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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 Mar 14, 2022
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...
Liver Neoplasms Enhancer of Zeste Homolog 2 Protein Artificial Intelligence Humans Multimodal Imaging Gene Expression Algorithms Cyclin-Dependent Kinase Inhibitor p57
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