G-nome surfer

Shaer, Orit; Kol, Guy; Strait, Megan; Fan, Chloe; Grevet, Catherine; Elfenbein, Sarah

doi:10.1145/1753326.1753539

Cited by 65 publications

(8 citation statements)

References 18 publications

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“…Therefore in terms of communication there was a significant uptick at the EML system. This finding aligns with research by Shaer et al [79,119] and Manshaei et al [80,112] who noted increased conversation at large touch-screen interfaces.…”

Section: Collaborationsupporting

confidence: 91%

“…• It should be relevant to the domain of medical research, allowing us to recruit users with a background in either biology or medicine thereby grounding our study results in prior related work in medical AI collaboration [54] and HCI for medical AI [79,80].…”

Section: Datasetsmentioning

confidence: 94%

“…A good example of this is the G-nome Surfer system by Shaer et al [79]. In the system students collaboratively interact with genomics data at a tabletop multi-touch interface to learn about the different representations and processes involved in genomics data.…”

Section: Data Explorationmentioning

confidence: 99%

“…First, a large display was selected to increase sensemaking and data discovery, which is well supported in literature [79,80]. Second, embodied multi-touch interactions were leveraged to increase collaboration and sensemaking, as supported by literature [78,79].…”

Section: Hardwarementioning

confidence: 99%

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Trust at Hand: A Multi-touch Tabletop Interface for Collaborative Training of Machine Learning Models in the Medical Domain

Bakogeorge

2024

Preprint

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Despite strides in medical AI research, adoption of medical AI models still lags behind, with low trust among medical practitioners. This can also be observed in medical AI research teams, where low levels of collaboration between team members during the process of model creation resulting in low trust in the model. In this thesis I present a prototype large form factor multi-touch interface for labeling and training models that addresses undertrust in medical AI workflows by: (i) Increasing normative trust through frequent interaction between domain experts and the AI model. (ii) Increasing affective trust between domain experts and data scientists through encouraging frequent collaborative interactions. (iii) Collects rich spatial data during labeling through multi-touch tabletop interface, which can be later leveraged by data scientists during model training. User study data shows that the system increased normative and affective trust when compared to traditional AI workflows.

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Section: Collaborationsupporting

confidence: 91%

Section: Datasetsmentioning

confidence: 94%

Section: Data Explorationmentioning

confidence: 99%

Section: Hardwarementioning

confidence: 99%

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Trust at Hand: A Multi-touch Tabletop Interface for Collaborative Training of Machine Learning Models in the Medical Domain

Bakogeorge

2024

Preprint

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“…Involv [24] and DeepTree [11] are tabletop interfaces for exploring the Encyclopedia of Life that are aimed at informal science learning settings. G-nome Surfer is a tabletop interface for collaborative exploration of genomic information [44,45,46]. In this work, we go beyond information exploration and delve into the design and specification of new biological systems.…”

Section: Reality-based Interfaces For Scientific Discovery and Educationmentioning

confidence: 99%

MoClo planner: Interactive visualization for Modular Cloning bio-design

Shaer

Valdes

Liu

et al. 2013

2013 IEEE Symposium on Biological Data Visualization (BioVis)

Self Cite

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MoClo Planner is an interactive visualization system for collaborative bio-design, utilizing a multi-touch interactive surface. The system integrates the information gathering, design, and specification of complex synthetic biological constructs using the Modular Cloning (MoClo) assembly method. Modular Cloning is a hierarchical DNA construction method that allows for the assembly of multi-part constructs from a library of biological parts in a one-pot reaction. This cutting-edge method facilitates and expedites the assembly of complex biological designs. However, it is an intricate multi-step process, which to date, has not been adequately supported by existing bio-design tools. Novel visual tools are needed in order to make MoClo more tractable and accessible to a broad range of users, to facilitate a less error prone bio-design process, and to improve workflow. MoClo Planner is a result of a participatory and user-centered design process, which included close collaboration with domain experts. Using multi-touch interactions and a rich graphical interface, the system accelerates the MoClo learning process, and reduces design time and errors. In this paper, we present user requirements and describe the design, implementation, and evaluation of MoClo Planner.

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Development and Usability Evaluation of a Prototype Conversational Interface for Biological Information Retrieval via Bioinformatics

Paixão-Cortes

Ellwanger

et al. 2019

Human Interface and the Management of Information. Visual Information and Knowledge Management

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G-nome surfer

Cited by 65 publications

References 18 publications

Trust at Hand: A Multi-touch Tabletop Interface for Collaborative Training of Machine Learning Models in the Medical Domain

Trust at Hand: A Multi-touch Tabletop Interface for Collaborative Training of Machine Learning Models in the Medical Domain

MoClo planner: Interactive visualization for Modular Cloning bio-design

Development and Usability Evaluation of a Prototype Conversational Interface for Biological Information Retrieval via Bioinformatics

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