Animated interval scatter-plot views for the exploratory analysis of large scale microarray time-course data.

Microarray technologies are a relatively new development that allow biologists to monitor the activity of thousands of genes (normally around 8,000) in parallel across multiple stages of a biological process. While this new perspective on biological functioning is recognised as having the potential to have a significant impact on the diagnosis, treatment, and prevention of diseases, it is only through effective analysis of the data produced that biologists can begin to unlock this potential. A significant obstacle to achieving effective analysis of microarray time-course is the combined scale and complexity of the data. This inevitably makes it difficult to reveal certain significant patterns in the data. In particular, it is less dominant patterns and, specifically, patterns that occur over smaller intervals of an experiment's overall time-frame that are more difficult to find. While existing techniques are capable of finding either unexpected patterns of activity over the majority of an experiment's time-frame or expected patterns of activity over smaller intervals of the time-frame, there are no techniques, or combination of techniques, that are suitable for finding unsuspected patterns of activity over smaller intervals. In order to overcome this limitation we have developed the Time-series Explorer, which specifically supports biologists in their attempts to reveal these types of pattern by allowing them to control an animated interval scatter-plot view of their data. This paper discusses aspects of the technique that make such an animated overview viable and describes the results of a user evaluation assessing the practical utility of the technique within the wider context of microarray time-series analysis as a whole

MaTSE: the gene expression time-series explorer.

Background High throughput gene expression time-course experiments provide a perspective on biological functioning recognized as having huge value for the diagnosis, treatment, and prevention of diseases. There are however significant challenges to properly exploiting this data due to its massive scale and complexity. In particular, existing techniques are found to be ill suited to finding patterns of changing activity over a limited interval of an experiments time frame. The Time-Series Explorer (TSE) was developed to overcome this limitation by allowing users to explore their data by controlling an animated scatter-plot view. MaTSE improves and extends TSE by allowing users to visualize data with missing values, cross reference multiple conditions, highlight gene groupings, and collaborate by sharing their findings. Results MaTSE was developed using an iterative software development cycle that involved a high level of user feedback and evaluation. The resulting software combines a variety of visualization and interaction techniques which work together to allow biologists to explore their data and reveal temporal patterns of gene activity. These include a scatter-plot that can be animated to view different temporal intervals of the data, a multiple coordinated view framework to support the cross reference of multiple experimental conditions, a novel method for highlighting overlapping groups in the scatter-plot, and a pattern browser component that can be used with scatter-plot box queries to support cooperative visualization. A final evaluation demonstrated the tools effectiveness in allowing users to find unexpected temporal patterns and the benefits of functionality such as the overlay of gene groupings and the ability to store patterns. Conclusions We have developed a new exploratory analysis tool, MaTSE, that allows users to find unexpected patterns of temporal activity in gene expression time-series data. Overall, the study acted well to demonstrate the benefits of an iterative software development life cycle and allowed us to investigate some visualization problems that are likely to be common in the field of bioinformatics. The subjects involved in the final evaluation were positive about the potential of MaTSE to help them find unexpected patterns in their data and characterized MaTSE as an exploratory tool valuable for hypothesis generation and the creation of new biological knowledge

Visual Support for the Modeling and Simulation of Cell Biological Processes

This dissertation aims at bringing information visualization closer to the demands of analytical problem solving for the specific domain of modeling and simulating cell biological systems. To this end, main segments of visual support in the domain are identified. For one of these segments, the visual analysis of simulation data, new concepts are developed. First, this includes the visualization of simulation data in the context of data generation. Second, new multiple view techniques for large and complex simulation data are introduced.Diese Arbeit verfolgt das Ziel, Informationsvisualisierung näher an die Anforderungen des Analyseprozesses heranzuführen, mit Blick auf die konkrete Anwendung der Modellierung und Simulation zellbiologischer Systeme. Dazu werden wesentliche Teilbereiche der visuellen Unterstützung identifiziert. Für den Teilbereich der visuellen Analyse von Simulationsdaten werden neue Konzepte entwickelt. Dies beinhaltet zum einen die Visualisierung von Simulationsdaten im Kontext der Datengenerierung. Zum anderen werden neue Multiple-View-Techniken für große und komplexe Simulationsdaten vorgestellt

Knowledge visualization: From theory to practice

Visualizations have been known as efficient tools that can help users analyze com- plex data. However, understanding the displayed data and finding underlying knowl- edge is still difficult. In this work, a new approach is proposed based on understanding the definition of knowledge. Although there are many definitions used in different ar- eas, this work focuses on representing knowledge as a part of a visualization and showing the benefit of adopting knowledge representation. Specifically, this work be- gins with understanding interaction and reasoning in visual analytics systems, then a new definition of knowledge visualization and its underlying knowledge conversion processes are proposed. The definition of knowledge is differentiated as either explicit or tacit knowledge. Instead of directly representing data, the value of the explicit knowledge associated with the data is determined based on a cost/benefit analysis. In accordance to its importance, the knowledge is displayed to help the user under- stand the complex data through visual analytical reasoning and discovery

의학 연구에서의 과학적 증거의 활용을 위한 시각적 분석 시스템 디자인

학위논문(박사) -- 서울대학교대학원 : 공과대학 컴퓨터공학부, 2022. 8. 서진욱.Evidence-based medicine, "the conscientious, explicit, and judicious use of current best evidence in healthcare and medical research" [98], is one of the most widely accepted medical paradigms of modern times. Searching, reviewing, and synthesizing reliable and high-quality scientific evidence is the key step for the paradigm. However, despite the widespread use of the EBM paradigm, challenges remain in applying Evidence-based medicine protocols to medical research. One of the barriers to applying the best scientific evidence to medical research is the severe literature and clinical data overload that causes the evidence-based tasks to be tremendous time-consuming tasks that require vast human effort. In this dissertation, we aim to employ visual analytics approaches to address the challenges of searching and reviewing massive scientific evidence in medical research. To overcome the burden and facilitate handling scientific evidence in medical research, we conducted three design studies and implemented novel visual analytics systems for laborious evidence-based tasks. First, we designed PLOEM, a novel visual analytics system to aid evidence synthesis, an essential step in Evidence-Based medicine, and generate an Evidence Map in a standardized method. We conducted a case study with an oncologist with years of evidence-based medicine experience. In the second study, we conducted a preliminary survey with 76 medical doctors to derive the design requirements for a biomedical literature search. Based on the results, We designed EEEVis, an interactive visual analytic system for biomedical literature search tasks. The system enhances the PubMed search result with several bibliographic visualizations and PubTator annotations. We performed a user study to evaluate the designs with 24 medical doctors and presented the design guidelines and challenges for a biomedical literature search system design. The third study presents GeneVis, a visual analytics system to identify and analyze gene expression signatures across major cancer types. A task that cancer researchers utilize to discover biomarkers in precision medicine. We conducted four case studies with domain experts in oncology and genomics. The study results show that the system can facilitate the task and provide new insights from the data. Based on the three studies of this dissertation, we conclude that carefully designed visual analytics approaches can provide an enhanced understanding and support medical researchers for laborious evidence-based tasks in medical research.근거중심의학(Evidence-Based Medicine)이란 "임상 치료 및 의학 연구에서 현재 존재하는 최고의 증거를 양심적이고, 명백하며, 분별 있게 이용하는 방법론"이며 [98], 현대 의학에서 가장 널리 받아들여지는 의학 패러다임이다. 신뢰할 수 있는 고수준의 과학적 근거를 검색, 검토, 합성하는 것이야 말로 근거중심의학의 핵심이다. 하지만, 근거중심의학이 이미 광범위하게 사용되고 있음에도 불구하고, 의학 연구에 근거중심의학의 프로토콜을 실천하는 데에는 여전히 많은 어려움이 따른다. 의료 문헌 정보, 임상 정보 및 유전체학 정보까지 연구자가 검토해야 할 근거의 양은 방대하며 광범위하다. 또한 의학과 기술의 발전으로 인해 점차 더 빠른 속도로 늘어나고 있기에, 이를 모두 엄밀히 검토하기 위해서는 막대한 양의 시간과 인력이 있어야 한다. 본 논문은 시각적 분석 방법론을 접목하여 의학 연구에서 방대한 과학적 증거를 검색하고 검토할 시 발생하는 막대한 인적 자원의 과부하 문제를 완화하고자 한다. 이를 위하여 근거중심의학의 절차 중 특히 인력 소모가 막심한 절차들을 선정하고, 이러한 난관을 극복하고 보다 효율적이고 효과적으로 데이터에서 유의미한 정보를 도출할 수 있게끔 보조하는 세 가지 시각적 분석 시스템들을 구현하였으며, 각각의 시스템에 관한 디자인 연구를 수행하였다. 우선 첫 디자인 연구에서는 근거중심의학 연구에 있어 필수적 단계인 근거 합성 방법론의 하나인 근거 매핑(Evidence Mapping) 과정을 지원하기 위한 시각적 분석 시스템 PLOEM을 설계했다. 그리고 이를 검증하기 위해 다년간의 근거 기반 의료 경험이 있는 종양학자와 함께 사례 연구를 수행했다. 두 번째 디자인 연구에서는 의학 문헌 검색 시스템의 요구사항 분석을 위해 총 76명의 의사를 상대로 설문조사를 진행하였고, 이러한 분석을 바탕으로 대화형 시각적 분석 시스템인 EEEVis를 설계했다. 이 시스템은 여러 종의 서지 정보 시각화 인터페이스와 PubTator의 주석 정보를 활용하여 PubMed 검색 엔진의 검색 결과를 증강하는 시스템이며, 이를 평가하기 위해 총 24명의 의사와 함께 사용자 연구를 수행하였다. 이 연구 결과를 바탕으로 의학 문헌 검색 시스템에 대한 설계 지침과 과제를 제시한다. 마지막으로 세 번째 디자인 연구에서는 임의의 유전자군의 유전자 발현 패턴을 주요 암 유형에 따라 시각화하고 분석할 수 있는 시스템인 GeneVis를 설계하였다. 암 유형에 따른 유전자 발현 패턴의 분석과 비교는 암 연구자들이 정밀 의학에서 생체 지표(Biomarker)를 발견하기 위해 빈번히 수행하는 작업이다. 우리는 종양학 전문가 및 유전체학 전문가 총 4인을 대상으로 사례 연구를 진행하였고, 그 결과 GeneVis가 해당 작업을 더 수월하게 수행하는 것과 기존의 데이터에서 새로운 정보를 도출하는 것에 도움이 되었음을 확인하였다. 위의 세 디자인 연구의 결과를 바탕으로, 본 논문은 사용자 분석과 작업 분석을 동반한 시각적 분석 방법론이 의학 연구의 근거 관련 작업의 어려움을 해소하고, 분석 데이터에 대한 보다 나은 이해를 제공하는 것이 가능하다고 결론 내린다.CHAPTER1 Introduction 1 1.1 Background and Motivation 1 1.2 Dissertation Outline 5 CHAPTER2 Related Work 7 2.1 Evidence Mapping: Graphical Representation for a Scientific Evidence Landscape 7 2.2 Scientific Literature Visualizations and Bibliography Visualizations 9 2.3 Visual Anlytics Systems for Genomics Data sets and Research Tasks 10 CHAPTER3 PLOEM: An Interactive Visualization Tool for Effective Evidence Mapping with Biomedical literature 12 3.1 Introduction 12 3.2 Visual Representations and Interactions of PLOEM 14 3.2.1 Overview of the PICO Criteria 14 3.2.2 Trend Visualization with the Timeline view 17 3.2.3 Representing the PICO Co-occurrence with the Relation view 20 3.2.4 Study detail view 22 3.3 Usage Scenarios: Visualizing Various Study Sizes with PLOEM 23 3.4 Conclusion 24 CHAPTER4 EEEvis: Efficacy improvement in searching MEDLINE database using a novel PubMed visual analytic system 26 4.1 Introduction 26 4.1.1 Motivation 26 4.1.2 Preliminary Survey: A Questionnaire on conventional literature search methods 28 4.1.3 Design Requirements for Biomedical Literature Search Systems 36 4.2 System and Interface Implementation of EEEVis 37 4.2.1 System Overview 37 4.2.2 Bibliography Filters 40 4.2.3 Timeline View 41 4.2.4 Co-authorship Network View 43 4.2.5 Article List and Detail View 44 4.3 User Study 46 4.3.1 Participants 46 4.3.2 Procedures 48 4.3.3 Results and Observations 50 4.4 Discussion 54 4.4.1 Design Implications 56 4.4.2 Limitations and Future Work 57 4.5 Conclusions 59 CHAPTER5 GeneVis: A Visual Analytics Systemfor Gene Signature Analysis in Cancers 68 5.1 Motivation 68 5.2 System and Interface Implementation 69 5.2.1 System Overview 69 5.2.2 Gene Expression Detail View 71 5.2.3 Gene Vector Projection View 72 5.2.4 Gene x Cancer Type Heatmap view 74 5.2.5 User Interaction in Multiple Coordinated Views 76 5.3 Case Studies 76 5.3.1 Participants 76 5.3.2 Task and Procedures 76 5.3.3 Case1: Identifying SimilarGeneSignatures with TGFB1in Hallmark Gene Sets 80 5.3.4 Case2: Identifying Cluster Patterns in the HRD data set 81 5.3.5 Results 82 5.4 Summary 85 CHAPTER6 Conclusion and future work 86 6.1 Conclusion 86 6.2 Future Work 87 Abstract (Korean) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102박

A visual analytics approach to feature discovery and subspace exploration in protein flexibility matrices

The vast amount of information generated by domain scientists makes the transi- tion from data to knowledge difficult and often impedes important discoveries. For example, the knowledge gained from protein flexibility data sets can speed advances in genetic therapies and drug discovery. However, these models generate so much data that large scale analysis by traditional methods is almost impossible. This hinders biomedical advances. Visual analytics is a new field that can help alleviate this problem. Visual analytics attempts to seamlessly integrate human abilities in pattern recognition, domain knowledge, and synthesis with automatic analysis techniques. I propose a novel, visual analytics pipeline and prototype which eases discovery, com- parison, and exploration in the outputs of complex computational biology datasets. The approach utilizes automatic feature extraction by image segmentation to locate regions of interest in the data, visually presents the features to users in an intuitive way, and provides rich interactions for multi-resolution visual exploration. Functional- ity is also provided for subspace exploration based on automatic similarity calculation and comparative visualizations. The effectiveness of feature discovery and subspace exploration is shown through a user study and user scenarios. Feedback from analysts confirms the suitability of the proposed solution to domain tasks

Epiviz: Integrative Visual Analysis Software for Genomics

Computational and visual data analysis for genomics has traditionally involved a combination of tools and resources, of which the most ubiquitous consist of genome browsers, focused mainly on integrative visualization of large numbers of big datasets, and computational environments, focused on data modeling of a small number of moderately sized datasets. Workflows that involve the integration and exploration of multiple heterogeneous data sources, small and large, public and user specific have been poorly addressed by these tools. Commonly, the data visualized in these tools is the output of analyses performed in powerful computing environments like R/Bioconductor or Python. Two essential aspects of data analysis are usually treated as distinct, in spite of being part of the same exploratory process: algorithmic analysis and interactive visualization. In current technologies these are not integrated within one tool, but rather, one precedes the other. Recent technological advances in web-based data visualization have made it possible for interactive visualization tools to tightly integrate with powerful algorithmic tools, without being restricted to one such tool in particular. We introduce Epiviz (http://epiviz.cbcb.umd.edu), an integrative visualization tool that bridges the gap between the two types of tools, simplifying genomic data analysis workflows. Epiviz is the first genomics interactive visualization tool to provide tight-knit integration with computational and statistical modeling and data analysis. We discuss three ways in which Epiviz advances the field of genomic data analysis: 1) it brings code to interactive visualizations at various different levels; 2) takes the first steps in the direction of collaborative data analysis by incorporating user plugins from source control providers, as well as by allowing analysis states to be shared among the scientific community; 3) combines established analysis features that have never before been available simultaneously in a visualization tool for genomics. Epiviz can be used in multiple branches of genomics data analysis for various types of datasets, of which we detail two: functional genomics data, aligned to a continuous coordinate such as the genome, and metagenomics, organized according to volatile hierarchical coordinate spaces. We also present security implications of the current design, performance benchmarks, a series of limitations and future research steps

Epiviz: a view inside the design of an integrated visual analysis software for genomics

Computational and visual data analysis for genomics has traditionally involved a combination of tools and resources, of which the most ubiquitous consist of genome browsers, focused mainly on integrative visualization of large numbers of big datasets, and computational environments, focused on data modeling of a small number of moderately sized datasets. Workflows that involve the integration and exploration of multiple heterogeneous data sources, small and large, public and user specific have been poorly addressed by these tools. In our previous work, we introduced Epiviz, which bridges the gap between the two types of tools, simplifying these workflows. In this paper we expand on the design decisions behind Epiviz, and introduce a series of new advanced features that further support the type of interactive exploratory workflow we have targeted. We discuss three ways in which Epiviz advances the field of genomic data analysis: 1) it brings code to interactive visualizations at various different levels; 2) takes the first steps in the direction of collaborative data analysis by incorporating user plugins from source control providers, as well as by allowing analysis states to be shared among the scientific community; 3) combines established analysis features that have never before been available simultaneously in a genome browser. In our discussion section, we present security implications of the current design, as well as a series of limitations and future research steps. Since many of the design choices of Epiviz are novel in genomics data analysis, this paper serves both as a document of our own approaches with lessons learned, as well as a start point for future efforts in the same direction for the genomics community.https://doi.org/10.1186/1471-2105-16-S11-S