3,190 research outputs found
Clinical Trial Recommendations Using Semantics-Based Inductive Inference and Knowledge Graph Embeddings
Designing a new clinical trial entails many decisions, such as defining a
cohort and setting the study objectives to name a few, and therefore can
benefit from recommendations based on exhaustive mining of past clinical trial
records. Here, we propose a novel recommendation methodology, based on neural
embeddings trained on a first-of-a-kind knowledge graph of clinical trials. We
addressed several important research questions in this context, including
designing a knowledge graph (KG) for clinical trial data, effectiveness of
various KG embedding (KGE) methods for it, a novel inductive inference using
KGE, and its use in generating recommendations for clinical trial design. We
used publicly available data from clinicaltrials.gov for the study. Results
show that our recommendations approach achieves relevance scores of 70%-83%,
measured as the text similarity to actual clinical trial elements, and the most
relevant recommendation can be found near the top of list. Our study also
suggests potential improvement in training KGE using node semantics.Comment: 13 pages (w/o bibliography), 4 Figures, 6 Table
Empowering AI drug discovery with explicit and implicit knowledge
Motivation: Recently, research on independently utilizing either explicit
knowledge from knowledge graphs or implicit knowledge from biomedical
literature for AI drug discovery has been growing rapidly. These approaches
have greatly improved the prediction accuracy of AI models on multiple
downstream tasks. However, integrating explicit and implicit knowledge
independently hinders their understanding of molecules. Results: We propose
DeepEIK, a unified deep learning framework that incorporates both explicit and
implicit knowledge for AI drug discovery. We adopt feature fusion to process
the multi-modal inputs, and leverage the attention mechanism to denoise the
text information. Experiments show that DeepEIK significantly outperforms
state-of-the-art methods on crucial tasks in AI drug discovery including
drug-target interaction prediction, drug property prediction and
protein-protein interaction prediction. Further studies show that benefiting
from explicit and implicit knowledge, our framework achieves a deeper
understanding of molecules and shows promising potential in facilitating drug
discovery applications.Comment: Bioinformatic
Graph AI in Medicine
In clinical artificial intelligence (AI), graph representation learning,
mainly through graph neural networks (GNNs), stands out for its capability to
capture intricate relationships within structured clinical datasets. With
diverse data -- from patient records to imaging -- GNNs process data
holistically by viewing modalities as nodes interconnected by their
relationships. Graph AI facilitates model transfer across clinical tasks,
enabling models to generalize across patient populations without additional
parameters or minimal re-training. However, the importance of human-centered
design and model interpretability in clinical decision-making cannot be
overstated. Since graph AI models capture information through localized neural
transformations defined on graph relationships, they offer both an opportunity
and a challenge in elucidating model rationale. Knowledge graphs can enhance
interpretability by aligning model-driven insights with medical knowledge.
Emerging graph models integrate diverse data modalities through pre-training,
facilitate interactive feedback loops, and foster human-AI collaboration,
paving the way to clinically meaningful predictions
The role of ontologies in biological and biomedical research: a functional perspective.
Ontologies are widely used in biological and biomedical research. Their success lies in their combination of four main features present in almost all ontologies: provision of standard identifiers for classes and relations that represent the phenomena within a domain; provision of a vocabulary for a domain; provision of metadata that describes the intended meaning of the classes and relations in ontologies; and the provision of machine-readable axioms and definitions that enable computational access to some aspects of the meaning of classes and relations. While each of these features enables applications that facilitate data integration, data access and analysis, a great potential lies in the possibility of combining these four features to support integrative analysis and interpretation of multimodal data. Here, we provide a functional perspective on ontologies in biology and biomedicine, focusing on what ontologies can do and describing how they can be used in support of integrative research. We also outline perspectives for using ontologies in data-driven science, in particular their application in structured data mining and machine learning applications.This is the final version of the article. It first appeared from Oxford University Press via http://dx.doi.org/10.1093/bib/bbv01
Exploiting Latent Features of Text and Graphs
As the size and scope of online data continues to grow, new machine learning techniques become necessary to best capitalize on the wealth of available information. However, the models that help convert data into knowledge require nontrivial processes to make sense of large collections of text and massive online graphs. In both scenarios, modern machine learning pipelines produce embeddings --- semantically rich vectors of latent features --- to convert human constructs for machine understanding. In this dissertation we focus on information available within biomedical science, including human-written abstracts of scientific papers, as well as machine-generated graphs of biomedical entity relationships. We present the Moliere system, and our method for identifying new discoveries through the use of natural language processing and graph mining algorithms. We propose heuristically-based ranking criteria to augment Moliere, and leverage this ranking to identify a new gene-treatment target for HIV-associated Neurodegenerative Disorders. We additionally focus on the latent features of graphs, and propose a new bipartite graph embedding technique. Using our graph embedding, we advance the state-of-the-art in hypergraph partitioning quality. Having newfound intuition of graph embeddings, we present Agatha, a deep-learning approach to hypothesis generation. This system learns a data-driven ranking criteria derived from the embeddings of our large proposed biomedical semantic graph. To produce human-readable results, we additionally propose CBAG, a technique for conditional biomedical abstract generation
Predicting Drug-Drug Interactions Using Knowledge Graphs
In the last decades, people have been consuming and combining more drugs than
before, increasing the number of Drug-Drug Interactions (DDIs). To predict
unknown DDIs, recently, studies started incorporating Knowledge Graphs (KGs)
since they are able to capture the relationships among entities providing
better drug representations than using a single drug property. In this paper,
we propose the medicX end-to-end framework that integrates several drug
features from public drug repositories into a KG and embeds the nodes in the
graph using various translation, factorisation and Neural Network (NN) based KG
Embedding (KGE) methods. Ultimately, we use a Machine Learning (ML) algorithm
that predicts unknown DDIs. Among the different translation and
factorisation-based KGE models, we found that the best performing combination
was the ComplEx embedding method with a Long Short-Term Memory (LSTM) network,
which obtained an F1-score of 95.19% on a dataset based on the DDIs found in
DrugBank version 5.1.8. This score is 5.61% better than the state-of-the-art
model DeepDDI. Additionally, we also developed a graph auto-encoder model that
uses a Graph Neural Network (GNN), which achieved an F1-score of 91.94%.
Consequently, GNNs have demonstrated a stronger ability to mine the underlying
semantics of the KG than the ComplEx model, and thus using higher dimension
embeddings within the GNN can lead to state-of-the-art performance
Biological Applications of Knowledge Graph Embedding Models
Complex biological systems are traditionally modelled as graphs of interconnected biological entities. These graphs, i.e. biological knowledge graphs, are then processed using graph exploratory approaches to perform different types of analytical and predictive tasks. Despite the high predictive accuracy of these approaches, they have limited scalability due to their dependency on time-consuming path exploratory procedures. In recent years, owing to the rapid advances of computational technologies, new approaches for modelling graphs and mining them with high accuracy and scalability have emerged. These approaches, i.e. knowledge graph embedding (KGE) models, operate by learning low-rank vector representations of graph nodes and edges that preserve the graph s inherent structure. These approaches were used to analyse knowledge graphs from different domains where they showed superior performance and accuracy compared to previous graph exploratory approaches. In this work, we study this class of models in the context of biological knowledge graphs and their different applications. We then show how KGE models can be a natural fit for representing complex biological knowledge modelled as graphs. We also discuss their predictive and analytical capabilities in different biology applications. In this regard, we present two example case studies that demonstrate the capabilities of KGE models: prediction of drug target interactions and polypharmacy side effects. Finally, we analyse different practical considerations for KGEs, and we discuss possible opportunities and challenges related to adopting them for modelling biological systems.The work presented in this paper was supported by the CLARIFY project that has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 875160, and by Insight research centre supported by the Science Foundation Ireland (SFI) grant (12/RC/2289_2)peer-reviewed2021-02-1
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