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Genotet: An Interactive web-based Visual Exploration Framework to Support Validation of Gene Regulatory Networks

IEEE TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2014年第12期20卷 1903-1912页

作者： Yu, Bowen Doraiswamy, Harish Chen, Xi Miraldi, Emily Arrieta-Ortiz, Mario Luis Hafemeister, Christoph Madar, Aviv Bonneau, Richard Silva, Claudio T. NYU Polytech Sch Engn New York NY 10003 USA NYU Ctr Genom & Syst Biol New York NY 10003 USA Cornell Univ Ithaca NY 14853 USA

Elucidation of transcriptional regulatory networks (TRNs) is a fundamental goal in biology, and one of the most important components of TRNs are transcription factors (TFs), proteins that specifically bind to gene promoter and enhancer regions to alter target gene expression patterns. Advances in genomic technologies as well as advances in computational biology have led to multiple large regulatory network models (directed networks) each with a large corpus of supporting data and gene-annotation. There are multiple possible biological motivations for exploring large regulatory network models, including: validating TF-target gene relationships, figuring out co-regulation patterns, and exploring the coordination of cell processes in response to changes in cell state or environment. Here we focus on queries aimed at validating regulatory network models, and on coordinating visualization of primary data and directed weighted gene regulatory networks. The large size of both the network models and the primary data can make such coordinated queries cumbersome with existing tools and, in particular, inhibits the sharing of results between collaborators. In this work, we develop and demonstrate a web-based framework for coordinating visualization and exploration of expression data (RNA-seq, microarray), network models and gene-binding data (ChIP-seq). Using specialized data structures and multiple coordinated views, we design an efficient querying model to support interactive analysis of the data. Finally, we show the effectiveness of our framework through case studies for the mouse immune system (a dataset focused on a subset of key cellular functions) and a model bacteria (a small genome with high data-completeness).

关键词： web-based visualization gene regulatory network

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DIA2: web-based Cyberinfrastructure for Visual Analysis of Funding Portfolios

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IEEE TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2014年第12期20卷 1823-1832页

作者： Madhavan, Krishna Elmqvist, Niklas Vorvoreanu, Mihaela Chen, Xin Wong, Yuetling Xian, Hanjun Dong, Zhihua Johri, Aditya Purdue Univ W Lafayette IN 47907 USA Microsoft Corp Redmond WA 98052 USA George Mason Univ Fairfax VA 22030 USA

We present a design study of the Deep Insights Anywhere, Anytime (DIA2) platform, a web-based visual analytics system that allows program managers and academic staff at the U.S. National Science Foundation to search, view, and analyze their research funding portfolio. The goal of this system is to facilitate users' understanding of both past and currently active research awards in order to make more informed decisions of their future funding. This user group is characterized by high domain expertise yet not necessarily high literacy in visualization and visual analytics they are essentially casual experts and thus require careful visual and information design, including adhering to user experience standards, providing a self-instructive interface, and progressively refining visualizations to minimize complexity. We discuss the challenges of designing a system for casual experts and highlight how we addressed this issue by modeling the organizational structure and workflows of the NSF within our system. We discuss each stage of the design process, starting with formative interviews, prototypes, and finally live deployments and evaluation with stakeholders.

关键词： visual analytics portfolio mining web-based visualization casual visualization design study

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FlowDyn: A daily streamflow prediction pipeline for dynamical deep neural network applications

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ENVIRONMENTAL MODELLING & SOFTWARE 2023年 170卷

作者： Tabas, S. Sadeghi Humaira, N. Samadi, S. Hubig, N. C. Clemson Univ Glenn Dept Civil Engn Clemson SC 29634 USA Clemson Univ Sch Comp Clemson SC USA Clemson Univ Dept Agr Sci Clemson SC USA

This paper presents a dynamical neural network framework to understand how catchment systems respond to daily rainfall-runoff processes over time. We developed an interactive Python-based deep neural network (DNN) package called FlowDyn (presented through a JS-based web platform) to simulate and forecast daily streamflow data for >180 gauging stations across the globe. Several DNN models, including long short-term memory (LSTM), Gated Recurrent Unit (GRU), and a hybrid network of convolutional neural network and LSTM (ConvLSTM), as well as an auto encoder (AE) model were developed and integrated into the FlowDyn pipeline to analyze and forecast sequential daily streamflow values that are embedded within a web-based application for demonstration and visualization. Inputs were gathered from different web services, including the catchment attributes and meteorology for large-sample studies (CAMELS), the national climatic data center (NCDC), and the global runoff data center (GRDC). DNN configurations were trained and tested with an average accuracy rating of 0.83 across 183 river basins globally. FlowDyn simulation and performance demonstrated that different DNN models were able to learn both regionally consistent and location-specific hydrological behaviors. Through the findings of this paper, we advocate the merit of applying FlowDyn package in the field of daily rainfall-runoff prediction at both local and global scales.

关键词： Deep neural networks Autoencoders FlowDyn package Streamflow prediction web-based visualization

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Medical Data and Mathematically Modeled Implicit Surface Real-Rime visualization in web Browsers

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INTERNATIONAL JOURNAL OF IMAGE AND GRAPHICS 2022年第4期22卷 2250027-2250027页

作者： Zhang, Qi Illinois State Univ Sch Informat Technol 100 North Univ St Normal IL 61761 USA

Raycasting can display volumetric medical data in fine details and reveal crucial inner imaging information, while implicit surface is able to effectively model complex objects with high flexibility, combining these two rendering modalities together will provide comprehensive information of the scene and has wide applications in surgical simulation, image-guided intervention, and medical training. However, medical data rendering is based on texture depth at every sampling point, while mathematically modeled implicit surfaces do not have geometric information in texture space. It is a challenging task to visualize both physical scalar data and virtual implicit surfaces simultaneously. To address this issue, in this paper, we present a new dual-casting ray-based double modality data rendering algorithm and web-based software platform to visualize volumetric medical data and implicit surface in the same browser. The algorithm runs on graphics processing unit and casts two virtual rays from camera to each pixel on the display panel, where one ray travels through the mathematically defined scene for implicit surface rendering and the other one passes the 3D texture space for volumetric data visualization. The proposed algorithm can detect voxel depth information and algebraic surface models along each casting ray and dynamically enhance the visualized dual-modality data with the improved lighting model and transparency adjustment function. Moreover, auxiliary innovative techniques are also presented to enhance the shading and rendering features of interest. Our software platform can seamlessly visualize volumetric medical data and implicit surfaces in the same web browser over Internet.

关键词： web-based visualization volumetric data rendering dual-casting ray implicit surface modeling feature enhancement

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KGETCDA: an efficient representation learning framework based on knowledge graph encoder from transformer for predicting circRNA-disease associations

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BRIEFINGS IN BIOINFORMATICS 2023年第5期24卷 bbad292-bbad292页

作者： Wu, Jinyang Ning, Zhiwei Ding, Yidong Wang, Ying Peng, Qinke Fu, Laiyi Xi An Jiao Tong Univ Sch Automat Sci & Engn Xian Peoples R China

Recent studies have demonstrated the significant role that circRNA plays in the progression of human diseases. Identifying circRNA-disease associations (CDA) in an efficient manner can offer crucial insights into disease diagnosis. While traditional biological experiments can be time-consuming and labor-intensive, computational methods have emerged as a viable alternative in recent years. However, these methods are often limited by data sparsity and their inability to explore high-order information. In this paper, we introduce a novel method named Knowledge Graph Encoder from Transformer for predicting CDA (KGETCDA). Specifically, KGETCDA first integrates more than 10 databases to construct a large heterogeneous non-coding RNA dataset, which contains multiple relationships between circRNA, miRNA, lncRNA and disease. Then, a biological knowledge graph is created based on this dataset and Transformer-based knowledge representation learning and attentive propagation layers are applied to obtain high-quality embeddings with accurately captured high-order interaction information. Finally, multilayer perceptron is utilized to predict the matching scores of CDA based on their embeddings. Our empirical results demonstrate that KGETCDA significantly outperforms other state-of-the-art models. To enhance user experience, we have developed an interactive web-based platform named HNRBase that allows users to visualize, download data and make predictions using KGETCDA with ease. The code and datasets are publicly available at .

关键词： CircRNA-disease associations Heterogeneous non-coding RNA database Knowledge graph Transformer web-based visualization

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Three-dimensional reconstruction of the Yaolin Cave

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Cartography and Geographic Information Science 2000年第1期27卷 31-39页

作者： Gong, J. Lin, H. Yin, X. Department of Geography Joint Lab. GeoInfo. Sci. CAS/CUHK Chinese University of Hong Kong Shatin NT Hong Kong

This paper reports on three-dimensional reconstruction of the Yaolin Cave in the Karst Region of Zhejiang, China. An object-oriented method was used to represent two cave classes-the class Boundary and the class Inside. The class Boundary included objects of the cave ceiling and the cave floor which are part of the topographical trend of the cave. This trend was determined by integrating topography with landform using TIN modeling. The inside of the cave with stalagmites, stalactites, dripstone-columns, and other objects was built using surveying data, photographs, and such computer graphics techniques as texture mapping and transparency. A stand-alone, 3-D visualization system-Cave3D-was developed on the Windows 98/NT platform to produce 3-D graphics and implement graphics-based computation, spatial query, and analyses. A virtual Yaolin cave was created using VRML97 and Java and posted on the web.

关键词： 3-D visualization Java Object-oriented modeling Virtual cave Visual objects VRML97 web-based visualization Yaolin Cave

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WEATHER DATA visualization AND ANALYTICAL PLATFORM

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SCALABLE COMPUTING-PRACTICE AND EXPERIENCE 2018年第2期19卷 79-86页

作者： Astsatryan, Hrachya Grigoryan, Hayk Gyulgyulyan, Eliza Hakobyan, Anush Kocharyan, Aram Narsisian, Wahi Sahakyan, Vladimir Shoukourian, Yuri Mkoyan, Artur Abrahamyan, Rita Petrosyan, Zarmandukht Aligon, Julien Natl Acad Sci Republ Armenia Inst Informat & Automat Problems 1 P Sevak Str Yerevan 0014 Armenia Armenian State Hydrometeorol & Monitoring Serv 09-8 A Mikoyan Str 4th Block Davitashen Yerevan 0054 Armenia Univ Toulouse 41 Alles Jules Guesde CS 61321 Toulouse France

This article aims to present a web-based interactive visualization and analytical platform for weather data in Armenia by integrating the three existing infrastructures for observational data, numerical weather prediction, and satellite image processing. The weather data used in the platform consists of near-surface atmospheric elements including air temperature, pressure, relative humidity, wind and precipitation. The visualization and analytical platform has been implemented for 2-m surface temperature. The platform gives Armenian State Hydrometeorological and Monitoring Service analytical capabilities to analyze the in-situ observations, model and satellite image data per station and region for a given period.

关键词： Weather data OLAP web-based visualization observational data numerical weather prediction satellite image processing

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A scalability study of web-native information visualization 08

A scalability study of web-native information visualization

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Proceedings of Graphics Interface 2008

作者： Donald W. Johnson T. J. Jankun-Kelly Mississippi State University

ISBN: (纸本)9781568814230

Several web-native information visualization methods (SVG, HTML5's Canvas, native HTML) are studied to contrast their performances at different data scales. Using Java implementations of parallel coordinates and squarified treemaps for comparison, we explore the design space of these web-based technologies in order to determine what design trade-offs are required.

关键词： asynchronous javascript (AJAX) web-based visualization Java HTML canvas information visualization scalable vector graphics (SVG) parallel coordinates treemap

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web-based Remote Renderingwith IBRAC (Image-based Rendering Acceleration and Compression)

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Computer Graphics Forum 2001年第3期19卷

作者： I. Yoon U. Neumann Computer Science Department Integrated Media Systems CenterUniversity of Southern California

Recent advances in Internet and computer graphics stimulate intensive use and development of 3D graphics on the World Wide web. To increase efficiency of systems using 3D graphics on the web, the presented method utilizes previously rendered and transmitted images to accelerate the rendering and compression of new synthetic scene images. The algorithm employs ray casting and epipolar constraints to exploit spatial and temporal coherence between the current and previously rendered images. The reprojection of color and visibility data accelerates the computation of new images. The rendering method intrinsically computes a residual image, based on a user specified error tolerance that balances image quality against computation time and bandwidth. Encoding and decoding uses the same algorithm, so the transmitted residual image consists only of significant data without addresses or offsets. We measure rendering speed-ups of four to seven without visible degradation. Compression ratios per frame are a factor of two to ten better than MPEG2 in our test cases. There is no transmission of 3D scene data to delay the first image. The efficiency of the server and client generally increases with scene complexity or data size since the rendering time is predominantly a function of image size. This approach is attractive for remote rendering applications such as web-based scientific visualization where a client system may be a relatively low-performance machine and limited network bandwidth makes transmission of large 3D data impractical.

关键词： Image-based rendering compression MPEG remote-rendering web-based visualization

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