Biology subjects

Shang, X.

Publications and source records attributed to Shang, X..

3 recordsLinked to original sources

bioRxiv2018

Identifying Emerging Phenomenon in Plant Long Temporal Phenotyping Experiments

The rapid improvement of phenotyping capability, accuracy, and throughput have greatly increased the volume and diversity of phenomics data. A remaining challenge is an efficient way to identify phenotypic patterns to improve our understanding of the quantitative variation of complex phenotypes, and to attribute gene functions. To address this challenge, we developed a new algorithm to identify emerging phenomena from large-scale temporal plant phenotyping experiments. An emerging phenomenon is defined as a group of genotypes who exhibit a coherent phenotype pattern during a relatively short time. Emerging phenomena are highly transient and diverse, and are dependent in complex ways on both environmental conditions and development. Identifying emerging phenomena may help biologists to examine potential relationships among phenotypes and genotypes in a genetically diverse population and to associate such relationships with the change of environments or development. We present an emerging phenomenon identification tool called Temporal Emerging Phenomenon Finder (TEP-Finder). Using large-scale longitudinal phenomics data as input, TEP-Finder first encodes the complicated phenotypic patterns into a dynamic phenotype network. Then, emerging phenomena in different temporal scales are identified from dynamic phenotype network using a maximal clique based approach. Meanwhile, a directed acyclic network of emerging phenomena is composed to model the relationships among the emerging phenomena. The experiment that compares TEP-Finder with two state-of-art algorithms shows that the emerging phenomena identified by TEP-Finder are more functionally specific, robust, and biologically significant. The source code, manual, and sample data of TEP-Finder are all available at: http://phenomics.uky.edu/TEP-Finder/.

bioinformatics↗

bioRxiv2018

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

BackgroundSingle 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.\n\nResultsIn 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.\n\nConclusionsThe 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.

bioinformatics↗

bioRxiv2018

A learning-based framework for miRNA-disease association prediction using neural networks

MotivationA microRNA (miRNA) is a type of non-coding RNA, which plays important roles in many bio-logical processes. Lots of studies have shown that miRNAs were implicated in human diseases, indicating that miRNAs might be potential biomarkers for various types of diseases. Therefore, it is important to reveal the relationships between miRNAs and diseases/phenotypes.\n\nResultsWe proposed a novel learning-based framework, MDA-CNN, for miRNA-disease association identification. The model first captures richer interaction features between diseases and miRNAs based on a three-layer network with an additional gene layer. An auto-encoder is then employed to identify the essential feature combination for each pair of miRNA and disease automatically. A final label is given by a convolutional neural network taking the reduced feature representation as input. The evaluation results showed that the proposed framework outperformed some state-of-the-art approaches in a large margin on both tasks of miRNA-disease associations prediction and miRNA-phenotype associations prediction.\n\nAvailabilityThe software will be available after the manuscript is accepted.\n\nContactjiajiepeng@nwpu.edu.cn;zywei@fudan.edu.cn;shang@nwpu.edu.cn\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗