9,394 research outputs found
Generating explanations for complex biomedical queries
Recent advances in health and life sciences have led to generation of a large amount of biomedical data. To facilitate access to its desired parts, such a big mass of data has been represented in structured forms, like databases and ontologies. On the other hand, representing these databases and ontologies in different formats, constructing them independently from each other, and storing them at different locations have brought about many challenges for answering queries about the knowledge represented in these ontologies and databases. One of the challenges for the users is to be able to represent such a biomedical query in a natural language, and get its answers in an understandable form. Another challenge is to extract relevant knowledge from different knowledge resources, and integrate them appropriately using also definitions, such as, chains of gene-gene interactions, cliques of genes based on gene-gene relations, or similarity/diversity of genes/drugs. Furthermore, once an answer is found for a complex query, the experts may need further explanations about the answer. The first two challenges have been addressed earlier using Answer Set Programming (ASP), with the development of a software system (called BIOQUERY-ASP). This thesis addresses the third challenge: explanation generation in ASP. In this thesis, we extend the earlier work on the first two challenges, to new forms of biomedical queries (e.g., about drug similarity) and to new biomedical knowledge resources. We introduce novel mathematical models and algorithms to generate (shortest or k different) explanations for queries in ASP, and provide a comprehensive theoretical analysis of these methods. We implement these algorithms and integrate them in BIOQUERY-ASP, and provide an experimental evaluation of our methods with some complex biomedical queries over the biomedical knowledge resources PHARMGKB, DRUGBANK, BIOGRID, CTD, SIDER, DISEASEONTOLOGY and ORPHADATA
Why do These Match? Explaining the Behavior of Image Similarity Models
Explaining a deep learning model can help users understand its behavior and
allow researchers to discern its shortcomings. Recent work has primarily
focused on explaining models for tasks like image classification or visual
question answering. In this paper, we introduce Salient Attributes for Network
Explanation (SANE) to explain image similarity models, where a model's output
is a score measuring the similarity of two inputs rather than a classification
score. In this task, an explanation depends on both of the input images, so
standard methods do not apply. Our SANE explanations pairs a saliency map
identifying important image regions with an attribute that best explains the
match. We find that our explanations provide additional information not
typically captured by saliency maps alone, and can also improve performance on
the classic task of attribute recognition. Our approach's ability to generalize
is demonstrated on two datasets from diverse domains, Polyvore Outfits and
Animals with Attributes 2. Code available at:
https://github.com/VisionLearningGroup/SANEComment: Accepted at ECCV 202
Portinari: A Data Exploration Tool to Personalize Cervical Cancer Screening
Socio-technical systems play an important role in public health screening
programs to prevent cancer. Cervical cancer incidence has significantly
decreased in countries that developed systems for organized screening engaging
medical practitioners, laboratories and patients. The system automatically
identifies individuals at risk of developing the disease and invites them for a
screening exam or a follow-up exam conducted by medical professionals. A triage
algorithm in the system aims to reduce unnecessary screening exams for
individuals at low-risk while detecting and treating individuals at high-risk.
Despite the general success of screening, the triage algorithm is a
one-size-fits all approach that is not personalized to a patient. This can
easily be observed in historical data from screening exams. Often patients rely
on personal factors to determine that they are either at high risk or not at
risk at all and take action at their own discretion. Can exploring patient
trajectories help hypothesize personal factors leading to their decisions? We
present Portinari, a data exploration tool to query and visualize future
trajectories of patients who have undergone a specific sequence of screening
exams. The web-based tool contains (a) a visual query interface (b) a backend
graph database of events in patients' lives (c) trajectory visualization using
sankey diagrams. We use Portinari to explore diverse trajectories of patients
following the Norwegian triage algorithm. The trajectories demonstrated
variable degrees of adherence to the triage algorithm and allowed
epidemiologists to hypothesize about the possible causes.Comment: Conference paper published at ICSE 2017 Buenos Aires, at the Software
Engineering in Society Track. 10 pages, 5 figure
Logical Gene Ontology Annotations (GOAL): exploring gene ontology annotations with OWL
MOTIVATION: Ontologies such as the Gene Ontology (GO) and their use in annotations make cross species comparisons of genes possible, along with a wide range of other analytical activities. The bio-ontologies community, in particular the Open Biomedical Ontologies (OBO) community, have provided many other ontologies and an increasingly large volume of annotations of gene products that can be exploited in query and analysis. As many annotations with different ontologies centre upon gene products, there is a possibility to explore gene products through multiple ontological perspectives at the same time. Questions could be asked that link a gene product’s function, process, cellular location, phenotype and disease. Current tools, such as AmiGO, allow exploration of genes based on their GO annotations, but not through multiple ontological perspectives. In addition, the semantics of these ontology’s representations should be able to, through automated reasoning, afford richer query opportunities of the gene product annotations than is currently possible. RESULTS: To do this multi-perspective, richer querying of gene product annotations, we have created the Logical Gene Ontology, or GOAL ontology, in OWL that combines the Gene Ontology, Human Disease Ontology and the Mammalian Phenotype Ontology, together with classes that represent the annotations with these ontologies for mouse gene products. Each mouse gene product is represented as a class, with the appropriate relationships to the GO aspects, phenotype and disease with which it has been annotated. We then use defined classes to query these protein classes through automated reasoning, and to build a complex hierarchy of gene products. We have presented this through a Web interface that allows arbitrary queries to be constructed and the results displayed. CONCLUSION: This standard use of OWL affords a rich interaction with Gene Ontology, Human Disease Ontology and Mammalian Phenotype Ontology annotations for the mouse, to give a fine partitioning of the gene products in the GOAL ontology. OWL in combination with automated reasoning can be effectively used to query across ontologies to ask biologically rich questions. We have demonstrated that automated reasoning can be used to deliver practical on-line querying support for the ontology annotations available for the mouse. AVAILABILITY: The GOAL Web page is to be found at http://owl.cs.manchester.ac.uk/goal
On the Potential of Artificial Intelligence Chatbots for Data Exploration of Federated Bioinformatics Knowledge Graphs
In this paper, we present work in progress on the role of artificial
intelligence (AI) chatbots, such as ChatGPT, in facilitating data access to
federated knowledge graphs. In particular, we provide examples from the field
of bioinformatics, to illustrate the potential use of Conversational AI to
describe datasets, as well as generate and explain (federated) queries across
datasets for the benefit of domain experts
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