A novel method to measure the semantic similarity of HPO terms

Chu

2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

et al. 2017

Self Cite

Background: DNA methylation has been identified to be widely associated to complex diseases. Among biological platforms to profile DNA methylation in human, the Illumina Infinium HumanMethylation450 BeadChip (450K) has been accepted as one of the most efficient technologies. However, challenges exist in analysis of DNA methylation data generated by this technology due to widespread biases. Results: Here we proposed a generalized framework for evaluating data analysis methods for Illumina 450K array. This framework considers the following steps towards a successful analysis: importing data, quality control, within-array normalization, correcting type bias, detecting differentially methylated probes or regions and biological interpretation. Conclusions: We evaluated five methods using three real datasets, and proposed outperform methods for the Illumina 450K array data analysis. Minfi and methylumi are optimal choice when analyzing small dataset. BMIQ and RCP are proper to correcting type bias and the normalized result of them can be used to discover DMPs. R package missMethyl is suitable for GO term enrichment analysis and biological interpretation.

Section: Biological Interpretationmentioning

confidence: 99%

A framework for analyzing DNA methylation data from Illumina Infinium HumanMethylation450 BeadChip

Chu

2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

et al. 2017

Self Cite

“…In early bioinformatics, the recognition of transcription factor binding sites was mainly concentrated in promoter regions. Many computational tools were developed to uncover the biological function of these functional element using various models [2][3][4][5][6][7][8][9][10][11]. In recent years, with the development of high-throughput sequencing technologies, the scope of research has been extended to whole genomes by specific protein and specific DNA sequences of immunoprecipitation throughout entire genomes.…”

Section: Introductionmentioning

confidence: 99%

MD-SVM: a novel SVM-based algorithm for the motif discovery of transcription factor binding sites

Lin

et al. 2019

BMC Bioinformatics

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Background: Transcription factors (TFs) play important roles in the regulation of gene expression. They can activate or block transcription of downstream genes in a manner of binding to specific genomic sequences. Therefore, motif discovery of these binding preference patterns is of central significance in the understanding of molecular regulation mechanism. Many algorithms have been proposed for the identification of transcription factor binding sites. However, it remains a challengeable problem. Results: Here, we proposed a novel motif discovery algorithm based on support vector machine (MD-SVM) to learn a discriminative model for TF binding sites. MD-SVM firstly obtains position weight matrix (PWM) from a set of training datasets. Then it translates the MD problem into a computational framework of multiple instance learning (MIL). It was applied to several real biological datasets. Results show that our algorithm outperforms MI-SVM in terms of both accuracy and specificity. Conclusions: In this paper, we modeled the TF motif discovery problem as a MIL optimization problem. The SVM algorithm was adapted to discriminate positive and negative bags of instances. Compared to other svm-based algorithms, MD-SVM show its superiority over its competitors in term of ROC AUC. Hopefully, it could be of benefit to the research community in the understanding of molecular functions of DNA functional elements and transcription factors.

“…Inspired by the success of neural network in modeling the hierarchical structure and function of a cell [18], we ask whether combining the rich prior biological knowledge in gene ontology (GO) with neural networks could enhance the clustering of cells based on their global transcriptome profiles. Gene Ontology (GO) [19], which has been widely used in many areas [20][21][22][23][24], provides a popular vocabulary system for systematically describing the attributes of genes and other biological entities. As one of the most popular bioinformatics sources, it contains reliable and easy-interpreted prior biological knowledge.…”

Section: Introductionmentioning

confidence: 99%

Combining Gene Ontology with Deep Neural Networks to Enhance the Clustering of Single Cell RNA-Seq Data

Peng

Shang

2018

Preprint

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Background: Single cell RNA sequencing (scRNA-seq) is applied to assay the individual transcriptomes of large numbers of cells. The gene expression at single-cell level provides an opportunity for better understanding of cell function and new discoveries in biomedical areas. To ensure that the single-cell based gene expression data are interpreted appropriately, it is crucial to develop new computational methods.Results: In this article, we try to construct the structure of neural networks based on the prior knowledge of Gene Ontology (GO). By integrating GO with both unsupervised and supervised models, two novel methods are proposed, named GOAE (Gene Ontology AutoEncoder) and GONN (Gene Ontology Neural Network) respectively, for clustering of scRNA-seq data. Conclusions:The evaluation results show that the proposed models outperform some state-of-the-art approaches. Furthermore, incorporating with GO, we provide an opportunity to interpret the underlying biological mechanism behind the neural network-based model.