Identify Huntington’s disease associated genes based on restricted Boltzmann machine with RNA-seq data

Jiang, Xue; Zhang, Han; Duan, Feng; Quan, Xiongwen

doi:10.1186/s12859-017-1859-6

Cited by 19 publications

(13 citation statements)

References 30 publications

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“…Finally, we compared our results with the existing ML-based method [ 14 ] for identifying HD-contributing genes and checked whether these 66 contributing genes are included in the known HD gene set. Out of the 66 genes, 13 genes are mutually identified in both ML studies.…”

Section: Discussionmentioning

confidence: 99%

“…Even though the emergence of a wide range of biological data of HD, including genomic profiling and electronic health records, a comprehensive understanding of the mechanism of HD from ML is so far unrealized, majorly due to the lack of needed data density [ 13 ]. For example, a previous ML study on RNA profiling of HD reported 4433 candidate genes from 16 samples [ 14 ], which is a typical high dimension, low sample size (HDLSS) situation, and ML may suffer from overfitting and low convergence. In this study, to harness the knowledge of the HD mechanism from the existing data, we tackled the data density issue by rationally reducing the dimension size, and identified the enriched pathways of HD by ML.…”

Section: Introductionmentioning

confidence: 99%

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Identification of contributing genes of Huntington’s disease by machine learning

et al. 2020

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Background Huntington’s disease (HD) is an inherited disorder caused by the polyglutamine (poly-Q) mutations of the HTT gene results in neurodegeneration characterized by chorea, loss of coordination, cognitive decline. However, HD pathogenesis is still elusive. Despite the availability of a wide range of biological data, a comprehensive understanding of HD’s mechanism from machine learning is so far unrealized, majorly due to the lack of needed data density. Methods To harness the knowledge of the HD pathogenesis from the expression profiles of postmortem prefrontal cortex samples of 157 HD and 157 controls, we used gene profiling ranking as the criteria to reduce the dimension to the order of magnitude of the sample size, followed by machine learning using the decision tree, rule induction, random forest, and generalized linear model. Results These four Machine learning models identified 66 potential HD-contributing genes, with the cross-validated accuracy of 90.79 ± 4.57%, 89.49 ± 5.20%, 90.45 ± 4.24%, and 97.46 ± 3.26%, respectively. The identified genes enriched the gene ontology of transcriptional regulation, inflammatory response, neuron projection, and the cytoskeleton. Moreover, three genes in the cognitive, sensory, and perceptual systems were also identified. Conclusions The mutant HTT may interfere with both the expression and transport of these identified genes to promote the HD pathogenesis.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identification of contributing genes of Huntington’s disease by machine learning

et al. 2020

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“…However, the above methods could not effectively distinguish the disease-associated genes from the non-disease-associated genes in the modifier gene set. Possible reasons for this may be that the expression levels of the disease-associated genes did not change significantly during the disease’s progression, or that a large number of gene expression levels had changed during the disease’s progression, thereby making it difficult to identify the disease-associated genes [ 28 ].…”

Section: Resultsmentioning

confidence: 99%

Ensemble Consensus-Guided Unsupervised Feature Selection to Identify Huntington’s Disease-Associated Genes

Guo

Jiang

et al. 2018

Genes

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Due to the complexity of the pathological mechanisms of neurodegenerative diseases, traditional differentially-expressed gene selection methods cannot detect disease-associated genes accurately. Recent studies have shown that consensus-guided unsupervised feature selection (CGUFS) performs well in feature selection for identifying disease-associated genes. Since the random initialization of the feature selection matrix in CGUFS results in instability of the final disease-associated gene set, for the purposes of this study we proposed an ensemble method based on CGUFS—namely, ensemble consensus-guided unsupervised feature selection (ECGUFS) in order to further improve the accuracy of disease-associated genes and the stability of feature gene sets. We also proposed a bagging integration strategy to integrate the results of CGUFS. Lastly, we conducted experiments with Huntington’s disease RNA sequencing (RNA-Seq) data and obtained the final feature gene set, where we detected 287 disease-associated genes. Enrichment analysis on these genes has shown that postsynaptic density and the postsynaptic membrane, synapse, and cell junction are all affected during the disease’s progression. However, ECGUFS greatly improved the accuracy of disease-associated gene prediction and the stability of the disease-associated gene set. We conducted a classification of samples with labels based on the linear support vector machine with 10-fold cross-validation. The average accuracy is 0.9, which suggests the effectiveness of the feature gene set.

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“…Critical proteins exhibiting dramatic structural changes in dynamic protein-protein interactions networks were identified in [80] using a DBN framework; the reconstruction errors and the variabilities across time were analyzed in the biological process. In [81], a DBM framework called stacked RBM was proposed to analyze the RNA-seq data of Huntington disease. In addition, the framework was able to screen the key genes during the Huntington disease development.…”

Section: Review Of Deep Learning Implementation In Health Carementioning

confidence: 99%

Deep Learning Intervention for Health Care Challenges: Some Biomedical Domain Considerations

Igbe

Liu

et al. 2019

JMIR Mhealth Uhealth

135

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The use of deep learning (DL) for the analysis and diagnosis of biomedical and health care problems has received unprecedented attention in the last decade. The technique has recorded a number of achievements for unearthing meaningful features and accomplishing tasks that were hitherto difficult to solve by other methods and human experts. Currently, biological and medical devices, treatment, and applications are capable of generating large volumes of data in the form of images, sounds, text, graphs, and signals creating the concept of big data. The innovation of DL is a developing trend in the wake of big data for data representation and analysis. DL is a type of machine learning algorithm that has deeper (or more) hidden layers of similar function cascaded into the network and has the capability to make meaning from medical big data. Current transformation drivers to achieve personalized health care delivery will be possible with the use of mobile health (mHealth). DL can provide the analysis for the deluge of data generated from mHealth apps. This paper reviews the fundamentals of DL methods and presents a general view of the trends in DL by capturing literature from PubMed and the Institute of Electrical and Electronics Engineers database publications that implement different variants of DL. We highlight the implementation of DL in health care, which we categorize into biological system, electronic health record, medical image, and physiological signals. In addition, we discuss some inherent challenges of DL affecting biomedical and health domain, as well as prospective research directions that focus on improving health management by promoting the application of physiological signals and modern internet technology.

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Identify Huntington’s disease associated genes based on restricted Boltzmann machine with RNA-seq data

Cited by 19 publications

References 30 publications

Identification of contributing genes of Huntington’s disease by machine learning

Identification of contributing genes of Huntington’s disease by machine learning

Ensemble Consensus-Guided Unsupervised Feature Selection to Identify Huntington’s Disease-Associated Genes

Deep Learning Intervention for Health Care Challenges: Some Biomedical Domain Considerations

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