Network visualizations are understudied in graphical perception. As a result, most network visualization designs still largely rely on designer intuition and algorithm optimizations rather than being guided by knowledge of human perception. The lack of perceptual understanding of network visualizations also limits the generalizability of past empirical evaluations, given their focus on performance over causal interpretation. To bridge this gap between perception and network visualization, we introduce a framework highlighting five key perceptual mechanisms used in node-link diagrams and adjacency matrices: attention, visual search, perceptual organization, ensemble coding, and object recognition. Our framework describes the role these perceptual mechanisms play in common network analytical tasks. We use the framework to revisit four past empirical investigations and outline future design experiments that can help produce more perceptually effective network visualizations. We anticipate this connection will afford translational understanding to guide more effective network visualization design and offer hypotheses for perception-aware network visualizations.

3D photogrammetry is a cost-effective, non-invasive imaging modality that does not require the use of ionizing radiation or sedation. Therefore, it is specifically valuable in pediatrics and is used to support the diagnosis and longitudinal study of craniofacial developmental pathologies such as craniosynostosis — the premature fusion of one or more cranial sutures resulting in local cranial growth restrictions and cranial malformations . Analysis of 3D photogrammetry requires the identification of craniofacial landmarks to segment the head surface and compute metrics to quantify anomalies. Unfortunately, commercial 3D photogrammetry software requires intensive manual landmark placements, which is time-consuming and prone to errors. We designed and implemented SHAPE, a System for Head-shape Analysis and Pediatric Evaluation. It integrates our previously developed automated landmarking method in a visual computing pipeline to evaluate a patient’s 3D photogram while allowing for manual confirmation and correction. It also automatically computes advanced metrics to quantify craniofacial anomalies and automatically creates a report that can be uploaded to the patient’s electronic health record . We conducted a user study with a professional clinical photographer to compare SHAPE to the existing clinical workflow. We found that SHAPE allows for the evaluation of a craniofacial 3D photogram more than three times faster than the current clinical workflow ( 3 . 85 ± 0 . 99 vs. 13 . 07 ± 5 . 29 minutes, p < 0 . 001 ). Our qualitative study findings indicate that the SHAPE workflow is well aligned with the existing clinical workflow and that SHAPE has useful features and is easy to learn.

The goal of the 21th International Workshop on Data Mining in Bioinformatics (BIOKDD 2022) is to encourage KDD researchers to solve the numerous problems and challenges in Bioinformatics using Data Mining technologies. Based on the organizers' expertise and communities, BIOKDD 2022 features 2 closely related themes "Biomedical Ontologies" and "Biological Data Visualization". These themes encourage the knowledge graph community and the data visualization community to work together to solve the many challenging problems in Bioinformatics with new perspectives. The key goal is to accelerate the convergence between Data Mining and Bioinformatics communities to expedite discoveries in basic biology, medicine and healthcare.

Abstract Background One goal of multi-omic studies is to identify interpretable predictive models for outcomes of interest, with analytes drawn from multiple omes. Such findings could support refined biological insight and hypothesis generation. However, standard analytical approaches are not designed to be “ome aware.” Thus, some researchers analyze data from one ome at a time, and then combine predictions across omes. Others resort to correlation studies, cataloging pairwise relationships, but lacking an obvious approach for cohesive and interpretable summaries of these catalogs. Methods We present a novel workflow for building predictive regression models from network neighborhoods in multi-omic networks. First, we generate pairwise regression models across all pairs of analytes from all omes, encoding the resulting “top table” of relationships in a network. Then, we build predictive logistic regression models using the analytes in network neighborhoods of interest. We call this method CANTARE (Consolidated Analysis of Network Topology And Regression Elements). Results We applied CANTARE to previously published data from healthy controls and patients with inflammatory bowel disease (IBD) consisting of three omes: gut microbiome, metabolomics, and microbial-derived enzymes. We identified 8 unique predictive models with AUC > 0.90. The number of predictors in these models ranged from 3 to 13. We compare the results of CANTARE to random forests and elastic-net penalized regressions, analyzing AUC, predictions, and predictors. CANTARE AUC values were competitive with those generated by random forests and penalized regressions. The top 3 CANTARE models had a greater dynamic range of predicted probabilities than did random forests and penalized regressions (p-value = 1.35 × 10–5). CANTARE models were significantly more likely to prioritize predictors from multiple omes than were the alternatives (p-value = 0.005). We also showed that predictive models from a network based on pairwise models with an interaction term for IBD have higher AUC than predictive models built from a correlation network (p-value = 0.016). R scripts and a CANTARE User’s Guide are available at https://sourceforge.net/projects/cytomelodics/files/CANTARE/ . Conclusion CANTARE offers a flexible approach for building parsimonious, interpretable multi-omic models. These models yield quantitative and directional effect sizes for predictors and support the generation of hypotheses for follow-up investigation.

BACKGROUND: Relationships between specific microbes and proper immune system development, composition, and function have been reported in a number of studies. However, researchers have discovered only a fraction of the likely relationships. "Omic" methodologies such as 16S ribosomal RNA (rRNA) sequencing and time-of-flight mass cytometry (CyTOF) immunophenotyping generate data that support generation of hypotheses, with the potential to identify additional relationships at a level of granularity ripe for further experimentation. Pairwise linear regressions between microbial and host immune features provide one approach for quantifying relationships between "omes", and the differences in these relationships across study cohorts or arms. This approach yields a top table of candidate results. However, the top table alone lacks the detail that domain experts such as microbiologists and immunologists need to vet candidate results for follow-up experiments. RESULTS: To support this vetting, we developed VOLARE (Visualization Of LineAr Regression Elements), a web application that integrates a searchable top table, small in-line graphs illustrating the fitted models, a network summarizing the top table, and on-demand detailed regression plots showing full sample-level detail. We applied VOLARE to three case studies-microbiome:cytokine data from fecal samples in human immunodeficiency virus (HIV), microbiome:cytokine data in inflammatory bowel disease and spondyloarthritis, and microbiome:immune cell data from gut biopsies in HIV. We present both patient-specific phenomena and relationships that differ by disease state. We also analyzed interaction data from system logs to characterize usage scenarios. This log analysis revealed that users frequently generated detailed regression plots, suggesting that this detail aids the vetting of results. CONCLUSIONS: Systematically integrating microbe:immune cell readouts through pairwise linear regressions and presenting the top table in an interactive environment supports the vetting of results for scientific relevance. VOLARE allows domain experts to control the analysis of their results, screening dozens of candidate relationships with ease. This interactive environment transcends the limitations of a static top table.

BACKGROUND: The interpretation of the results from genome-scale experiments is a challenging and important problem in contemporary biomedical research. Biological networks that integrate experimental results with existing knowledge from biomedical databases and published literature can provide a rich resource and powerful basis for hypothesizing about mechanistic explanations for observed gene-phenotype relationships. However, the size and density of such networks often impede their efficient exploration and understanding. RESULTS: We introduce a visual analytics approach that integrates interactive filtering of dense networks based on degree-of-interest functions with attribute-based layouts of the resulting subnetworks. The comparison of multiple subnetworks representing different analysis facets is facilitated through an interactive super-network that integrates brushing-and-linking techniques for highlighting components across networks. An implementation is freely available as a Cytoscape app. CONCLUSIONS: We demonstrate the utility of our approach through two case studies using a dataset that combines clinical data with high-throughput data for studying the effect of β-blocker treatment on heart failure patients. Furthermore, we discuss our team-based iterative design and development process as well as the limitations and generalizability of our approach.

A common class of biomedical analysis is to explore expression data from high throughput experiments for the purpose of uncovering functional relationships that can lead to a hypothesis about mechanisms of a disease. We call this analysis expression driven, -omics hypothesizing. In it, scientists use interactive data visualizations and read deeply in the research literature. Little is known, however, about the actual flow of reasoning and behaviors (sense making) that scientists enact in this analysis, end-to-end. Understanding this flow is important because if bioinformatics tools are to be truly useful they must support it. Sense making models of visual analytics in other domains have been developed and used to inform the design of useful and usable tools. We believe they would be helpful in bioinformatics. To characterize the sense making involved in expression-driven, -omics hypothesizing, we conducted an in-depth observational study of one scientist as she engaged in this analysis over six months. From findings, we abstracted a preliminary sense making model. Here we describe its stages and suggest guidelines for developing visualization tools that we derived from this case. A single case cannot be generalized. But we offer our findings, sense making model and case-based tool guidelines as a first step toward increasing interest and further research in the bioinformatics field on scientists' analytical workflows and their implications for tool design.

Investigators across many disciplines and organizations must sift through large collections of text documents to understand and piece together information. Whether they are fighting crime, curing diseases, deciding what car to buy, or researching a new field, inevitably investigators will encounter text documents. Taking a visual analytics approach, we integrate multiple text analysis algorithms with a suite of interactive visualizations to provide a flexible and powerful environment that allows analysts to explore collections of documents while sensemaking. Our particular focus is on the process of integrating automated analyses with interactive visualizations in a smooth and fluid manner. We illustrate this integration through two example scenarios: an academic researcher examining InfoVis and VAST conference papers and a consumer exploring car reviews while pondering a purchase decision. Finally, we provide lessons learned toward the design and implementation of visual analytics systems for document exploration and understanding.

Despite the growing number of systems providing visual analytic support for investigative analysis, few empirical studies of the potential benefits of such systems have been conducted, particularly controlled, comparative evaluations. Determining how such systems foster insight and sensemaking is important for their continued growth and study, however. Furthermore, studies that identify how people use such systems and why they benefit (or not) can help inform the design of new systems in this area. We conducted an evaluation of the visual analytics system Jigsaw employed in a small investigative sensemaking exercise, and compared its use to three other more traditional methods of analysis. Sixteen participants performed a simulated intelligence analysis task under one of the four conditions. Experimental results suggest that Jigsaw assisted participants to analyze the data and identify an embedded threat. We describe different analysis strategies used by study participants and how computational support (or the lack thereof) influenced the strategies. We then illustrate several characteristics of the sensemaking process identified in the study and provide design implications for investigative analysis tools based thereon. We conclude with recommendations on metrics and techniques for evaluating visual analytics systems for investigative analysis.

Although many static-analysis techniques have been developed for automatically detecting bugs, such as null dereferences, fewer automated approaches have been presented for analyzing whether and how such bugs are fixed. Attempted bug fixes may be incomplete in that a related manifestation of the bug remains unfixed. In this paper, we characterize the “completeness” of attempted bug fixes that involve the flow of invalid values from one program point to another, such as null dereferences, in Java programs. Our characterization is based on the definition of a bug neighborhood, which is a scope of flows of invalid values. We present an automated analysis that, given two versions P and P' of a program, identifies the bugs in P that have been fixed in P', and classifies each fix as complete or incomplete. We implemented our technique for null-dereference bugs and conducted empirical studies using open-source projects. Our results indicate that, for the projects we studied, many bug fixes are not complete, and thus, may cause failures in subsequent executions of the program.

Web applications can suffer from poor reliability, and AJAX technology makes Web sites even more error-prone. Failures of a Web application, particularly user-visible failures, impact users' satisfaction and may drive users away from using the Web site. Conventional testing techniques are inadequate for improving AJAX applications' reliability, and application providers commonly rely on fast failure detection, which is challenging. In this paper, we present a novel technique for automatically detecting user-visible failures in AJAX applications. Our technique trains a Bayesian model to analyze users' interaction behaviors to infer whether such user responses are related to user-visible failures. We implemented our technique in a tool called SIRANA. We performed a case study using a commercial AJAX application with seeded bugs, and collected users' interaction data during 14 one-hour sessions. We evaluated our technique using SIRANA applied to the collected data. The results demonstrate the effectiveness of our technique: It not only detected all seeded bugs, but also detected four real, previously-unknown bugs.

Several recent studies indicate that many industrial applications exhibit poor quality in the design of exception-handling. To improve the quality of error-handling, we need to understand the problems and obstacles that developers face when designing and implementing exception-handling. In this paper, we present our research on understanding the viewpoint of developers-novices and experts-toward exception-handling. First, we conducted a study with novice developers in industry. The study results reveal that novices tend to ignore exceptions because of the complex nature of exception-handling. Then, we conducted a second study with experts in industry to understand their perspective on exception-handling. The study results show that, for experts, exception-handling is a crucial part in the development process. Experts also confirm the novices' approach of ignoring exception-handling and provide insights as to why novices do so. After analyzing the study data, we identified factors that influence experts' strategy selection process for handling exceptions and then built a model that represents a strategy selection process experts use to handle exceptions. Our model is based on interacting modules and fault scope. We conclude with some recommendations to help novices improve their understanding of exception-handling.

This paper presents a new approach for locating and repairing faults that cause runtime exceptions in Java programs. The approach handles runtime exceptions that involve a flow of an incorrect value that finally leads to the exception. This important class of exceptions includes exceptions related to dereferences of null pointers, arithmetic faults (e.g., ArithmeticException), and type faults (e.g., ArrayStoreException). Given a statement at which such an exception occurred, the technique combines dynamic analysis (using stack-trace information) with static backward data-flow analysis (beginning at the point where the runtime exception occurred) to identify the source statement at which an incorrect assignment was made; this information is required to locate the fault. The approach also identifies the source statements that may cause this same exception on other executions, along with the reference statements that may raise an exception in other executions because of this incorrect assignment; this information is required to repair the fault. The paper also presents an application of our technique to null pointer exceptions. Finally, the paper describes an implementation of the null-pointer-exception analysis and a set of studies that demonstrate the advantages of our approach for locating and repairing faults in the program.

Developers must actively maintain status information about the programming tasks they are engaged in. Unfortunately, much of this knowledge does not exist in tangible form. Commonly, developers are forced to recover the details of this knowledge after encountering an unanticipated interruption. In this paper, we present a technique enabling developers to quickly reengage in a task after such an interruption. A dashboard visualization, called TaskBoard, of intermediate task knowledge is constructed from recent activities and program executions and then displayed to assist reengagement. Developers can annotate its contents with task descriptions and prospective cues.

Software archives are one of the best sources available to researchers for understanding the software development process. However, much detective work is still necessary in order to unravel the software development story. During this process, researchers must isolate changes and follow their trails over time. In support of this analysis, several research tools have provided different representations for connecting the many changes extracted from software archives. Most of these tools are based on textual analysis of source code and use line-based differencing between software versions. This approach limits the ability to process changes structurally resulting in less concise and comparable items. Adoption of structure-based approaches have been hampered by complex implementations and overly verbose change descriptions. We present a technique for expressing changes that is fine-grained but preserves some structural aspects. The structural information itself may not have changed, but instead provides a context for interpreting the change. This in turn, enables more relevant and concise descriptions in terms of software types and programming activities. We apply our technique to common challenges that researchers face, and then we discuss and compare our results with other techniques.

Software developers often work on multiple simultaneous projects. Even when only a single project is underway, everyday distractions interrupt the development effort. Consequently, developers spend significant effort pursuing recovery of their context. By context, we focus on the classes and methods within the code that are relevant to a specific bug being fixed or enhancement made. Context is reified by a program in terms of a set of presentations (windows a containing source code, command executions, and data files); however, it is not enough to save the latest context. Even when working on a single task, programmers flip between contexts as they extend their understanding, and when they decide on a change, they may have to visit several contexts in order to address all possible ripple effects. Consequently, we would like to record a history of contexts and be able to retrieve them as demanded by the current task. We introduce a novel technique to obtain a context, consisting of a set of methods relevant for the current task, from a programmer's interactions with an IDE. Using this context, we demonstrate how to improve the ability of a programmer to recover the mental state associated with tasks and to facilitate the exploration of software through recommendation systems