9,127 research outputs found
Will this work for Susan? Challenges for delivering usable and useful generic linked data browsers
While we witness an explosion of exploration tools for simple datasets on Web 2.0 designed for use by ordinary citizens, the goal of a usable interface for supporting navigation and sense-making over arbitrary linked data has remained elusive. The purpose of this paper is to analyse why - what makes exploring linked data so hard? Through a user-centered use case scenario, we work through requirements for sense making with data to extract functional requirements and to compare these against our tools to see what challenges emerge to deliver a useful, usable knowledge building experience with linked data. We present presentation layer and heterogeneous data integration challenges and offer practical considerations for moving forward to effective linked data sensemaking tools
Progress and Opportunities of Foundation Models in Bioinformatics
Bioinformatics has witnessed a paradigm shift with the increasing integration
of artificial intelligence (AI), particularly through the adoption of
foundation models (FMs). These AI techniques have rapidly advanced, addressing
historical challenges in bioinformatics such as the scarcity of annotated data
and the presence of data noise. FMs are particularly adept at handling
large-scale, unlabeled data, a common scenario in biological contexts due to
the time-consuming and costly nature of experimentally determining labeled
data. This characteristic has allowed FMs to excel and achieve notable results
in various downstream validation tasks, demonstrating their ability to
represent diverse biological entities effectively. Undoubtedly, FMs have
ushered in a new era in computational biology, especially in the realm of deep
learning. The primary goal of this survey is to conduct a systematic
investigation and summary of FMs in bioinformatics, tracing their evolution,
current research status, and the methodologies employed. Central to our focus
is the application of FMs to specific biological problems, aiming to guide the
research community in choosing appropriate FMs for their research needs. We
delve into the specifics of the problem at hand including sequence analysis,
structure prediction, function annotation, and multimodal integration,
comparing the structures and advancements against traditional methods.
Furthermore, the review analyses challenges and limitations faced by FMs in
biology, such as data noise, model explainability, and potential biases.
Finally, we outline potential development paths and strategies for FMs in
future biological research, setting the stage for continued innovation and
application in this rapidly evolving field. This comprehensive review serves
not only as an academic resource but also as a roadmap for future explorations
and applications of FMs in biology.Comment: 27 pages, 3 figures, 2 table
FRIOD: a deeply integrated feature-rich interactive system for effective and efficient outlier detection
In this paper, we propose an novel interactive outlier detection system called feature-rich interactive outlier detection (FRIOD), which features a deep integration of human interaction to improve detection performance and greatly streamline the detection process. A user-friendly interactive mechanism is developed to allow easy and intuitive user interaction in all the major stages of the underlying outlier detection algorithm which includes dense cell selection, location-aware distance thresholding, and final top outlier validation. By doing so, we can mitigate the major difficulty of the competitive outlier detection methods in specifying the key parameter values, such as the density and distance thresholds. An innovative optimization approach is also proposed to optimize the grid-based space partitioning, which is a critical step of FRIOD. Such optimization fully considers the high-quality outliers it detects with the aid of human interaction. The experimental evaluation demonstrates that FRIOD can improve the quality of the detected outliers and make the detection process more intuitive, effective, and efficient
A Survey of Multimodal Information Fusion for Smart Healthcare: Mapping the Journey from Data to Wisdom
Multimodal medical data fusion has emerged as a transformative approach in
smart healthcare, enabling a comprehensive understanding of patient health and
personalized treatment plans. In this paper, a journey from data to information
to knowledge to wisdom (DIKW) is explored through multimodal fusion for smart
healthcare. We present a comprehensive review of multimodal medical data fusion
focused on the integration of various data modalities. The review explores
different approaches such as feature selection, rule-based systems, machine
learning, deep learning, and natural language processing, for fusing and
analyzing multimodal data. This paper also highlights the challenges associated
with multimodal fusion in healthcare. By synthesizing the reviewed frameworks
and theories, it proposes a generic framework for multimodal medical data
fusion that aligns with the DIKW model. Moreover, it discusses future
directions related to the four pillars of healthcare: Predictive, Preventive,
Personalized, and Participatory approaches. The components of the comprehensive
survey presented in this paper form the foundation for more successful
implementation of multimodal fusion in smart healthcare. Our findings can guide
researchers and practitioners in leveraging the power of multimodal fusion with
the state-of-the-art approaches to revolutionize healthcare and improve patient
outcomes.Comment: This work has been submitted to the ELSEVIER for possible
publication. Copyright may be transferred without notice, after which this
version may no longer be accessibl
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Open Research Knowledge Graph
As we mark the fifth anniversary of the alpha release of the Open Research
Knowledge Graph (ORKG), it is both timely and exhilarating to celebrate the significant
strides made in this pioneering project. We designed this book as a tribute
to the evolution and achievements of the ORKG and as a practical guide encapsulating
its essence in a form that resonates with both the general reader and the
specialist.
The ORKG has opened a new era in the way scholarly knowledge is curated, managed,
and disseminated. By transforming vast arrays of unstructured narrative text
into structured, machine-processable knowledge, the ORKG has emerged as an
essential service with sophisticated functionalities. Over the past five years, our
team has developed the ORKG into a vibrant platform that enhances the accessibility
and visibility of scientific research. This book serves as a non-technical guide
and a comprehensive reference for new and existing users that outlines the
ORKG’s approach, technologies, and its role in revolutionizing scholarly communication.
By elucidating how the ORKG facilitates the collection, enhancement, and
sharing of knowledge, we invite readers to appreciate the value and potential of
this groundbreaking digital tool presented in a tangible form.
Looking ahead, we are thrilled to announce the upcoming unveiling of promising
new features and tools at the fifth-year celebration of the ORKG’s alpha release.
These innovations are set to redefine the boundaries of machine assistance enabled
by research knowledge graphs. Among these enhancements, you can expect
more intuitive interfaces that simplify the user experience, and enhanced machine learning
models that improve the automation and accuracy of data curation.
We also included a glossary tailored to clarifying key terms and concepts associated
with the ORKG to ensure that all readers, regardless of their technical background,
can fully engage with and understand the content presented. This book
transcends the boundaries of a typical technical report. We crafted this as an inspiration
for future applications, a testament to the ongoing evolution in scholarly
communication that invites further collaboration and innovation. Let this book serve
as both your guide and invitation to explore the ORKG as it continues to grow and
shape the landscape of scientific inquiry and communication
DPVis: Visual Analytics with Hidden Markov Models for Disease Progression Pathways
Clinical researchers use disease progression models to understand patient
status and characterize progression patterns from longitudinal health records.
One approach for disease progression modeling is to describe patient status
using a small number of states that represent distinctive distributions over a
set of observed measures. Hidden Markov models (HMMs) and its variants are a
class of models that both discover these states and make inferences of health
states for patients. Despite the advantages of using the algorithms for
discovering interesting patterns, it still remains challenging for medical
experts to interpret model outputs, understand complex modeling parameters, and
clinically make sense of the patterns. To tackle these problems, we conducted a
design study with clinical scientists, statisticians, and visualization
experts, with the goal to investigate disease progression pathways of chronic
diseases, namely type 1 diabetes (T1D), Huntington's disease, Parkinson's
disease, and chronic obstructive pulmonary disease (COPD). As a result, we
introduce DPVis which seamlessly integrates model parameters and outcomes of
HMMs into interpretable and interactive visualizations. In this study, we
demonstrate that DPVis is successful in evaluating disease progression models,
visually summarizing disease states, interactively exploring disease
progression patterns, and building, analyzing, and comparing clinically
relevant patient subgroups.Comment: to appear at IEEE Transactions on Visualization and Computer Graphic
Dynamic Influence Networks for Rule-based Models
We introduce the Dynamic Influence Network (DIN), a novel visual analytics
technique for representing and analyzing rule-based models of protein-protein
interaction networks. Rule-based modeling has proved instrumental in developing
biological models that are concise, comprehensible, easily extensible, and that
mitigate the combinatorial complexity of multi-state and multi-component
biological molecules. Our technique visualizes the dynamics of these rules as
they evolve over time. Using the data produced by KaSim, an open source
stochastic simulator of rule-based models written in the Kappa language, DINs
provide a node-link diagram that represents the influence that each rule has on
the other rules. That is, rather than representing individual biological
components or types, we instead represent the rules about them (as nodes) and
the current influence of these rules (as links). Using our interactive DIN-Viz
software tool, researchers are able to query this dynamic network to find
meaningful patterns about biological processes, and to identify salient aspects
of complex rule-based models. To evaluate the effectiveness of our approach, we
investigate a simulation of a circadian clock model that illustrates the
oscillatory behavior of the KaiC protein phosphorylation cycle.Comment: Accepted to TVCG, in pres
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