798 research outputs found
The implicitome: A resource for rationalizing gene-disease associations
High-throughput experimental methods such as medical sequencing and genome-wide association studies (GWAS) identify increasingly large numbers of potential relations between genetic variants and diseases. Both biological complexity (millions of potential gene-disease associations) and the accelerating rate of data production necessitate computational approaches to prioritize and rationalize potential gene-disease relations. Here, we use concept profile technology to expose from the biomedical literature both explicitly stated gene-disease relations (the explicitome) and a much larger set of implied gene-disease associations (the implicitome). Implicit relations are largely unknown to, or are even unintended by the original authors, but they vastly extend the reach of existing
MGCN: Medical Relation Extraction Based on GCN
With the progress of society and the improvement of living standards, people pay more and more attention to personal health, and WITMED (Wise Information Technology of med) has occupied an important position. The relationship prediction work in the medical field has high requirements on the interpretability of the method, but the relationship between medical entities is complex, and the existing methods are difficult to meet the requirements. This paper proposes a novel medical information relation extraction method MGCN, which combines contextual information to provide global interpretability for relation prediction of medical entities. The method uses Co-occurrence Graph and Graph Convolutional Network to build up a network of relations between entities, uses the Open-world Assumption to construct potential relations between associated entities, and goes through the Knowledge-aware Attention mechanism to give relation prediction for the entity pair of interest. Experiments were conducted on a public medical dataset CTF, MGCN achieved the score of 0.831, demonstrating its effectiveness in medical relation extraction
The Implicitome: A Resource for Rationalizing Gene-Disease Associations
High-throughput experimental methods such as medical sequencing and genome-wide association studies (GWAS) identify increasingly large numbers of potential relations between genetic variants and diseases. Both biological complexity (millions of potential gene-disease associations) and the accelerating rate of data production necessitate computational approaches to prioritize and rationalize potential gene-disease relations. Here, we use concept profile technology to expose from the biomedical literature both explicitly stated gene-disease relations (the explicitome) and a much larger set of implied gene-disease associations (the implicitome). Implicit relations are largely unknown to, or are even unintended by the original authors, but they vastly extend the reach of existing biomedical knowledge for identification and interpretation of gene-disease associations. The implicitome can be used in conjunction with experimental data resources to rationalize both known and novel associations. We demonstrate the usefulness of the implicitome by rationalizing known and novel gene-disease associations, including those from GWAS. To facilitate the re-use of implicit gene-disease associations, we publish our data in compliance with FAIR Data Publishing recommendations [https://www.force11.org/group/fairgroup] using nanopublications. An online tool (http://knowledge.bio) is available to explore established and potential gene-disease associations in the context of other biomedical relations.UB – Publicatie
Matching Patients to Clinical Trials with Large Language Models
Clinical trials are vital in advancing drug development and evidence-based
medicine, but their success is often hindered by challenges in patient
recruitment. In this work, we investigate the potential of large language
models (LLMs) to assist individual patients and referral physicians in
identifying suitable clinical trials from an extensive selection. Specifically,
we introduce TrialGPT, a novel architecture employing LLMs to predict
criterion-level eligibility with detailed explanations, which are then
aggregated for ranking and excluding candidate clinical trials based on
free-text patient notes. We evaluate TrialGPT on three publicly available
cohorts of 184 patients and 18,238 annotated clinical trials. The experimental
results demonstrate several key findings: First, TrialGPT achieves high
criterion-level prediction accuracy with faithful explanations. Second, the
aggregated trial-level TrialGPT scores are highly correlated with expert
eligibility annotations. Third, these scores prove effective in ranking
clinical trials and exclude ineligible candidates. Our error analysis suggests
that current LLMs still make some mistakes due to limited medical knowledge and
domain-specific context understanding. Nonetheless, we believe the explanatory
capabilities of LLMs are highly valuable. Future research is warranted on how
such AI assistants can be integrated into the routine trial matching workflow
in real-world settings to improve its efficiency
Literature Based Discovery (LBD): Towards Hypothesis Generation and Knowledge Discovery in Biomedical Text Mining
Biomedical knowledge is growing in an astounding pace with a majority of this
knowledge is represented as scientific publications. Text mining tools and
methods represents automatic approaches for extracting hidden patterns and
trends from this semi structured and unstructured data. In Biomedical Text
mining, Literature Based Discovery (LBD) is the process of automatically
discovering novel associations between medical terms otherwise mentioned in
disjoint literature sets. LBD approaches proven to be successfully reducing the
discovery time of potential associations that are hidden in the vast amount of
scientific literature. The process focuses on creating concept profiles for
medical terms such as a disease or symptom and connecting it with a drug and
treatment based on the statistical significance of the shared profiles. This
knowledge discovery approach introduced in 1989 still remains as a core task in
text mining. Currently the ABC principle based two approaches namely open
discovery and closed discovery are mostly explored in LBD process. This review
starts with general introduction about text mining followed by biomedical text
mining and introduces various literature resources such as MEDLINE, UMLS, MESH,
and SemMedDB. This is followed by brief introduction of the core ABC principle
and its associated two approaches open discovery and closed discovery in LBD
process. This review also discusses the deep learning applications in LBD by
reviewing the role of transformer models and neural networks based LBD models
and its future aspects. Finally, reviews the key biomedical discoveries
generated through LBD approaches in biomedicine and conclude with the current
limitations and future directions of LBD.Comment: 43 Pages, 5 Figures, 4 Table
Text Fact Transfer
Text style transfer is a prominent task that aims to control the style of
text without inherently changing its factual content. To cover more text
modification applications, such as adapting past news for current events and
repurposing educational materials, we propose the task of text fact transfer,
which seeks to transfer the factual content of a source text between topics
without modifying its style. We find that existing language models struggle
with text fact transfer, due to their inability to preserve the specificity and
phrasing of the source text, and tendency to hallucinate errors. To address
these issues, we design ModQGA, a framework that minimally modifies a source
text with a novel combination of end-to-end question generation and
specificity-aware question answering. Through experiments on four existing
datasets adapted for text fact transfer, we show that ModQGA can accurately
transfer factual content without sacrificing the style of the source text.Comment: Accepted to EMNLP 2023 Main Conferenc
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