scholarly journals ST-Steiner: a spatio-temporal gene discovery algorithm

2019 ◽  
Vol 35 (18) ◽  
pp. 3433-3440 ◽  
Author(s):  
Utku Norman ◽  
A Ercument Cicek
Keyword(s):  
Gene Discovery ◽  
Gene Interaction ◽  
Autism Spectrum ◽  
Supplementary Information ◽  
Temporal Dimension ◽  
Cascading Effect ◽  
Gene Interaction Networks ◽  
Steiner Forest ◽  
Prize Collecting ◽  
Spatio Temporal

AbstractMotivationWhole exome sequencing (WES) studies for autism spectrum disorder (ASD) could identify only around six dozen risk genes to date because the genetic architecture of the disorder is highly complex. To speed the gene discovery process up, a few network-based ASD gene discovery algorithms were proposed. Although these methods use static gene interaction networks, functional clustering of genes is bound to evolve during neurodevelopment and disruptions are likely to have a cascading effect on the future associations. Thus, approaches that disregard the dynamic nature of neurodevelopment are limited.ResultsHere, we present a spatio-temporal gene discovery algorithm, which leverages information from evolving gene co-expression networks of neurodevelopment. The algorithm solves a prize-collecting Steiner forest-based problem on co-expression networks, adapted to model neurodevelopment and transfer information from precursor neurodevelopmental windows. The decisions made by the algorithm can be traced back, adding interpretability to the results. We apply the algorithm on ASD WES data of 3871 samples and identify risk clusters using BrainSpan co-expression networks of early- and mid-fetal periods. On an independent dataset, we show that incorporation of the temporal dimension increases the predictive power: predicted clusters are hit more and show higher enrichment in ASD-related functions compared with the state-of-the-art.Availability and implementationThe code is available at http://ciceklab.cs.bilkent.edu.tr/st-steiner.Supplementary informationSupplementary data are available at Bioinformatics online.

scholarly journals Spatio-Temporal Gene Discovery For Autism Spectrum Disorder

2018 ◽  
Author(s):  
Utku Norman ◽  
A. Ercument Cicek
Keyword(s):  
Autism Spectrum Disorder ◽  
Gene Discovery ◽  
Gene Interaction ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Temporal Dimension ◽  
Cascading Effect ◽  
Steiner Forest ◽  
Spatio Temporal ◽  
Coexpression Networks

AbstractWhole exome sequencing (WES) studies for Autism Spectrum Disorder (ASD) could identify only around six dozen risk genes to date because the genetic architecture of the disorder is highly complex. To speed the gene discovery process up, a few network-based ASD gene discovery algorithms were proposed. Although these methods use static gene interaction networks, functional clustering of genes is bound to evolve during neurodevelopment and disruptions are likely to have a cascading effect on the future associations. Thus, approaches that disregard the dynamic nature of neurodevelopment are limited in power. Here, we present a spatio-temporal gene discovery algorithm for ASD, which leverages information from evolving gene coexpression networks of neurodevelopment. The algorithm solves a variant of prize-collecting Steiner forest-based problem on coexpression networks to model neurodevelopment and transfer information from precursor neurodevelopmental windows. The decisions made by the algorithm can be traced back, adding interpretability to the results. We apply the algorithm on WES data of 3,871 samples and identify risk clusters using BrainSpan coexpression networks of earlyand mid-fetal periods. On an independent dataset, we show that incorporation of the temporal dimension increases the prediction power: Predicted clusters are hit more and show higher enrichment in ASD-related functions compared to the state-of-the-art. Code is available at http://ciceklab.cs.bilkent.edu.tr/ST-Steiner/.

Integration of gene interaction information into a reweighted Lasso-Cox model for accurate survival prediction

2020 ◽  
Author(s):  
Wei Wang ◽  
Wei Liu
Keyword(s):  
Cox Model ◽  
Gene Interaction ◽  
Supplementary Information ◽  
Survival Prediction ◽  
Gene Interaction Network ◽  
Clinical Cancer Research ◽  
Prognostic Performance ◽  
Interaction Information ◽  
Cancer Types ◽  
Risk Of Cancer

Abstract Motivation Accurately predicting the risk of cancer patients is a central challenge for clinical cancer research. For high-dimensional gene expression data, Cox proportional hazard model with the least absolute shrinkage and selection operator for variable selection (Lasso-Cox) is one of the most popular feature selection and risk prediction algorithms. However, the Lasso-Cox model treats all genes equally, ignoring the biological characteristics of the genes themselves. This often encounters the problem of poor prognostic performance on independent datasets. Results Here, we propose a Reweighted Lasso-Cox (RLasso-Cox) model to ameliorate this problem by integrating gene interaction information. It is based on the hypothesis that topologically important genes in the gene interaction network tend to have stable expression changes. We used random walk to evaluate the topological weight of genes, and then highlighted topologically important genes to improve the generalization ability of the RLasso-Cox model. Experiments on datasets of three cancer types showed that the RLasso-Cox model improves the prognostic accuracy and robustness compared with the Lasso-Cox model and several existing network-based methods. More importantly, the RLasso-Cox model has the advantage of identifying small gene sets with high prognostic performance on independent datasets, which may play an important role in identifying robust survival biomarkers for various cancer types. Availability and implementation http://bioconductor.org/packages/devel/bioc/html/RLassoCox.html Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Glimpse: A Gaze-Based Measure of Temporal Salience

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3099
Author(s):  
V. Javier Traver ◽  
Judith Zorío ◽  
Luis A. Leiva
Keyword(s):  
Visual Attention ◽  
Computational Models ◽  
Temporal Evolution ◽  
Temporal Consistency ◽  
Visual Salience ◽  
Temporal Dimension ◽  
Spatial Perspective ◽  
Spatio Temporal ◽  
Scoring Algorithms ◽  
Over Time

Temporal salience considers how visual attention varies over time. Although visual salience has been widely studied from a spatial perspective, its temporal dimension has been mostly ignored, despite arguably being of utmost importance to understand the temporal evolution of attention on dynamic contents. To address this gap, we proposed Glimpse, a novel measure to compute temporal salience based on the observer-spatio-temporal consistency of raw gaze data. The measure is conceptually simple, training free, and provides a semantically meaningful quantification of visual attention over time. As an extension, we explored scoring algorithms to estimate temporal salience from spatial salience maps predicted with existing computational models. However, these approaches generally fall short when compared with our proposed gaze-based measure. Glimpse could serve as the basis for several downstream tasks such as segmentation or summarization of videos. Glimpse’s software and data are publicly available.

scholarly journals An analytical method for the identification of cell type-specific disease gene modules

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Jinting Guan ◽  
Yiping Lin ◽  
Yang Wang ◽  
Junchao Gao ◽  
Guoli Ji
Keyword(s):  
Disease Gene ◽  
Gene Interaction ◽  
Cell Types ◽  
Autism Spectrum ◽  
Specific Gene ◽  
Cell Type ◽  
Specific Disease ◽  
Cell Type Specific ◽  
Gene Modules ◽  
Disease Associated Genes

Abstract Background Genome-wide association studies have identified genetic variants associated with the risk of brain-related diseases, such as neurological and psychiatric disorders, while the causal variants and the specific vulnerable cell types are often needed to be studied. Many disease-associated genes are expressed in multiple cell types of human brains, while the pathologic variants affect primarily specific cell types. We hypothesize a model in which what determines the manifestation of a disease in a cell type is the presence of disease module comprised of disease-associated genes, instead of individual genes. Therefore, it is essential to identify the presence/absence of disease gene modules in cells. Methods To characterize the cell type-specificity of brain-related diseases, we construct human brain cell type-specific gene interaction networks integrating human brain nucleus gene expression data with a referenced tissue-specific gene interaction network. Then from the cell type-specific gene interaction networks, we identify significant cell type-specific disease gene modules by performing statistical tests. Results Between neurons and glia cells, the constructed cell type-specific gene networks and their gene functions are distinct. Then we identify cell type-specific disease gene modules associated with autism spectrum disorder and find that different gene modules are formed and distinct gene functions may be dysregulated in different cells. We also study the similarity and dissimilarity in cell type-specific disease gene modules among autism spectrum disorder, schizophrenia and bipolar disorder. The functions of neurons-specific disease gene modules are associated with synapse for all three diseases, while those in glia cells are different. To facilitate the use of our method, we develop an R package, CtsDGM, for the identification of cell type-specific disease gene modules. Conclusions The results support our hypothesis that a disease manifests itself in a cell type through forming a statistically significant disease gene module. The identification of cell type-specific disease gene modules can promote the development of more targeted biomarkers and treatments for the disease. Our method can be applied for depicting the cell type heterogeneity of a given disease, and also for studying the similarity and dissimilarity between different disorders, providing new insights into the molecular mechanisms underlying the pathogenesis and progression of diseases.

scholarly journals APPLICATION OF 3D SPATIO-TEMPORAL DATA MODELING, MANAGEMENT, AND ANALYSIS IN DB4GEO

2016 ◽  
Vol IV-2/W1 ◽  
pp. 163-170
Author(s):  
P. V. Kuper ◽  
M. Breunig ◽  
M. Al-Doori ◽  
A. Thomsen
Keyword(s):  
Water Supply ◽  
United Arab Emirates ◽  
Transport Systems ◽  
Reservoir Modeling ◽  
Future Research ◽  
Temporal Data ◽  
Temporal Dimension ◽  
Geospatial Database ◽  
Spatio Temporal ◽  
Temporal Objects

Many of today´s world wide challenges such as climate change, water supply and transport systems in cities or movements of crowds need spatio-temporal data to be examined in detail. Thus the number of examinations in 3D space dealing with geospatial objects moving in space and time or even changing their shapes in time will rapidly increase in the future. Prominent spatio-temporal applications are subsurface reservoir modeling, water supply after seawater desalination and the development of transport systems in mega cities. All of these applications generate large spatio-temporal data sets. However, the modeling, management and analysis of 3D geo-objects with changing shape and attributes in time still is a challenge for geospatial database architectures. In this article we describe the application of concepts for the modeling, management and analysis of 2.5D and 3D spatial plus 1D temporal objects implemented in DB4GeO, our service-oriented geospatial database architecture. An example application with spatio-temporal data of a landfill, near the city of Osnabrück in Germany demonstrates the usage of the concepts. Finally, an outlook on our future research focusing on new applications with big data analysis in three spatial plus one temporal dimension in the United Arab Emirates, especially the Dubai area, is given.

scholarly journals Predicting placenta transcriptional regulatory interactions based on immunohistochemistry images data and convolutional neural network

2020 ◽  
Author(s):  
Yajun Liu ◽  
Yilin Guo ◽  
Ya Gao ◽  
Guiming Hu ◽  
Ju Ma ◽  
...  
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Convolutional Neural Network ◽  
Human Placenta ◽  
Chorionic Villus ◽  
Gene Interaction ◽  
Gene Interaction Networks ◽  
Transcriptional Regulatory ◽  
High Resolution Images

Aims: The dysfunction of placenta development is correlated to the defects of pregnancy and fetal growth. The detailed molecular mechanism of placenta development is not identified in human due to the lack of material in vivo. Image-based reconstructions of GRN are still very underdeveloped. Methods and Results: In this study, immunohistochemistry images of different TFs in chorionic villus were obtained by a high-resolution scanner. Next, we used a convolutional neural network and machine learning method to infer gene interaction networks of human placenta from these images based on the transfer learning technique. The experimental results show that deep learning models reveals regulatory roles that have not yet been fully recognized. The spatial expression data reveal new regulatory relationships that traditional experiments have failed to recognize, and has allowed the development of gene regulation networks based on the spatial distribution of gene expression. Conclusions: We demonstrate the effectiveness of this approach in building networks using high-resolution images of the human placenta. Our analysis is of certain significance for further exploration of the development of the placenta and the occurrence of pregnancy-related diseases in the future. The datasets and analysis provide a useful source for the researchers in the field of the maternal-fetal interface and the establishment of pregnancy.

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