Browsing by Author "Koonchanok, Ratanond"

Now showing 1 - 3 of 3
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    Data Prophecy: Exploring the Effects of Belief Elicitation in Visual Analytics
    (2021) Koonchanok, Ratanond; Baser, Parul; Sikharam, Abhinav; Raveendranath, Nirmal Kumar; Reda, Khairi; Human-Centered Computing, School of Informatics and Computing
    Interactive visualizations are widely used in exploratory data analysis, but existing systems provide limited support for confirmatory analysis. We introduce PredictMe, a tool for belief-driven visual analysis, enabling users to draw and test their beliefs against data, as an alternative to data-driven exploration. PredictMe combines belief elicitation with traditional visualization interactions to support mixed analysis styles. In a comparative study, we investigated how these affordances impact participants' cognition. Results show that PredictMe prompts participants to incorporate their working knowledge more frequently in queries. Participants were more likely to attend to discrepancies between their mental models and the data. However, those same participants were also less likely to engage in interactions associated with exploration, and ultimately inspected fewer visualizations and made fewer discoveries. The results suggest that belief elicitation may moderate exploratory behaviors, instead nudging users to be more deliberate in their analysis. We discuss the implications for visualization design.
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    Sequoia: an interactive visual analytics platform for interpretation and feature extraction from nanopore sequencing datasets
    (BMC, 2021-07-07) Koonchanok, Ratanond; Daulatabad, Swapna Vidhur; Mir, Quoseena; Reda, Khairi; Janga, Sarath Chandra; Human-Centered Computing, School of Informatics and Computing
    Background: Direct-sequencing technologies, such as Oxford Nanopore's, are delivering long RNA reads with great efficacy and convenience. These technologies afford an ability to detect post-transcriptional modifications at a single-molecule resolution, promising new insights into the functional roles of RNA. However, realizing this potential requires new tools to analyze and explore this type of data. Result: Here, we present Sequoia, a visual analytics tool that allows users to interactively explore nanopore sequences. Sequoia combines a Python-based backend with a multi-view visualization interface, enabling users to import raw nanopore sequencing data in a Fast5 format, cluster sequences based on electric-current similarities, and drill-down onto signals to identify properties of interest. We demonstrate the application of Sequoia by generating and analyzing ~ 500k reads from direct RNA sequencing data of human HeLa cell line. We focus on comparing signal features from m6A and m5C RNA modifications as the first step towards building automated classifiers. We show how, through iterative visual exploration and tuning of dimensionality reduction parameters, we can separate modified RNA sequences from their unmodified counterparts. We also document new, qualitative signal signatures that characterize these modifications from otherwise normal RNA bases, which we were able to discover from the visualization. Conclusions: Sequoia's interactive features complement existing computational approaches in nanopore-based RNA workflows. The insights gleaned through visual analysis should help users in developing rationales, hypotheses, and insights into the dynamic nature of RNA. Sequoia is available at https://github.com/dnonatar/Sequoia .
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    Techniques for Improving the Robustness of Visual Analytics
    (2024-08) Koonchanok, Ratanond; Reda, Khairi; Chakraborty, Sunandan; Cafaro, Francesco; McCabe, Sean
    Interactive visualization systems, such as Tableau, are integral parts of the data analysis workflow. While such tools were built to help analysts perform exploratory data analysis with minimal effort, analysts have also been using them to make statistical inferences (e.g., predicting future trends) based on patterns revealed by the dataset. However, in addition to revealing true patterns, visualizations can also surface noise and other random fluctuations in data, which could lead to spurious discoveries. The latter poses a threat to the trustworthiness of analyses, especially given the increased reliance on visualizations across various domains. My central thesis is that it is possible to reduce the incidence of false discovery by introducing lightweight user interface elements in visualization tools. In particular, I propose eliciting and incorporating analyst beliefs into visualizations as an approach for guarding against spurious patterns and reducing the risk of analysts “overfitting” the data. To study how analysts would respond to such intervention, I first designed an interactive tool that combined visual belief elicitation with traditional visualization functionalities. In a qualitative study with data analysts, the tool appeared to allow users to operationalize their working knowledge into analyses, nudging them to adopt normative analysis practices (e.g., specifying hypotheses before peeking at data). I then conducted a crowdsourced experiment to investigate if this design could indeed help reduce the incidence of false discovery. Compared to a control condition, participants who used our intervention made significantly more accurate inferences and reported fewer false discoveries. Lastly, I investigated the capability of human intuition by comparing inferences from participants against those generated by statistical machines to understand the advantages and limitations of each. Overall, my thesis paves the way toward the development of a robust visual analytics system that facilitates collaborative decision-making processes, leveraging the complementary abilities of humans and machines.