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Gene Expression

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Gene Classification Based on Multi-Class SVMs with Systematic Sampling and Hierarchical Clustering (SSHC) Algorithm.

Advances in experimental medicine and biology
The support vector machines (SVMs) is one of the machine learning algorithms with high classification accuracy. However, the support vector machine algorithm has a very high training complexity. Thus, it is not very efficient with large datasets. In ...

Adversarial deconfounding autoencoder for learning robust gene expression embeddings.

Bioinformatics (Oxford, England)
MOTIVATION: Increasing number of gene expression profiles has enabled the use of complex models, such as deep unsupervised neural networks, to extract a latent space from these profiles. However, expression profiles, especially when collected in larg...

Gene Ontology Curation of Neuroinflammation Biology Improves the Interpretation of Alzheimer's Disease Gene Expression Data.

Journal of Alzheimer's disease : JAD
BACKGROUND: Gene Ontology (GO) is a major bioinformatic resource used for analysis of large biomedical datasets, for example from genome-wide association studies, applied universally across biological fields, including Alzheimer's disease (AD) resear...

Statistical and Machine Learning Methods for eQTL Analysis.

Methods in molecular biology (Clifton, N.J.)
An immense amount of observable diversity exists for all traits and across global populations. In the post-genomic era, equipped with efficient sequencing capabilities and better genotyping methods, we are now able to more fully appreciate how regula...

Computational functional genomics-based reduction of disease-related gene sets to their key components.

Bioinformatics (Oxford, England)
MOTIVATION: The genetic architecture of diseases becomes increasingly known. This raises difficulties in picking suitable targets for further research among an increasing number of candidates. Although expression based methods of gene set reduction a...

Genetic Neural Networks: an artificial neural network architecture for capturing gene expression relationships.

Bioinformatics (Oxford, England)
MOTIVATION: Gene expression prediction is one of the grand challenges in computational biology. The availability of transcriptomics data combined with recent advances in artificial neural networks provide an unprecedented opportunity to create predic...

DeepDiff: DEEP-learning for predicting DIFFerential gene expression from histone modifications.

Bioinformatics (Oxford, England)
MOTIVATION: Computational methods that predict differential gene expression from histone modification signals are highly desirable for understanding how histone modifications control the functional heterogeneity of cells through influencing different...

[From symptom to syndrome using modern software support].

Der Internist
Diagnosing rare diseases can be challenging for clinicians. This article gives an overview on novel approaches, which enable automated phenotype-driven analyses of differential diagnoses for rare diseases as well as genomic variation data of affected...

D-Aspartate amends reproductive performance of aged roosters by changing gene expression and testicular histology.

Reproduction, fertility, and development
Male broiler breeders (n=32) of 55 weeks of age were administered four different doses of capsulated d-aspartate (DA; 0, 100, 200 or 300mgkg-1day-1, p.o. (DA0, DA100, DA200 and DA300 respectively)) for 12 successive weeks to assess reproductive perfo...

Graph-based semi-supervised learning with genomic data integration using condition-responsive genes applied to phenotype classification.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Data integration methods that combine data from different molecular levels such as genome, epigenome, transcriptome, etc., have received a great deal of interest in the past few years. It has been demonstrated that the synergistic effects ...