Enhancing Biomedical Knowledge Discovery for Diseases: An Open-Source Framework Applied on Rett Syndrome and Alzheimer's Disease

• URL: http://arxiv.org/abs/2407.13492v2
• Date: Fri, 6 Sep 2024 12:28:31 GMT
• Title: Enhancing Biomedical Knowledge Discovery for Diseases: An Open-Source Framework Applied on Rett Syndrome and Alzheimer's Disease
• Authors: Christos Theodoropoulos, Andrei Catalin Coman, James Henderson, Marie-Francine Moens,
• Abstract summary: We introduce an open-source framework designed to construct knowledge around specific diseases directly from raw text. To facilitate research in disease-related knowledge discovery, we create two annotated datasets focused on Rett syndrome and Alzheimer's disease.
• Score: 28.68816381566995
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The ever-growing volume of biomedical publications creates a critical need for efficient knowledge discovery. In this context, we introduce an open-source end-to-end framework designed to construct knowledge around specific diseases directly from raw text. To facilitate research in disease-related knowledge discovery, we create two annotated datasets focused on Rett syndrome and Alzheimer's disease, enabling the identification of semantic relations between biomedical entities. Extensive benchmarking explores various ways to represent relations and entity representations, offering insights into optimal modeling strategies for semantic relation detection and highlighting language models' competence in knowledge discovery. We also conduct probing experiments using different layer representations and attention scores to explore transformers' ability to capture semantic relations.

Related papers

• Unified Representation of Genomic and Biomedical Concepts through Multi-Task, Multi-Source Contrastive Learning [45.6771125432388]
  We introduce GENomic REpresentation with Language Model (GENEREL) GENEREL is a framework designed to bridge genetic and biomedical knowledge bases. Our experiments demonstrate GENEREL's ability to effectively capture the nuanced relationships between SNPs and clinical concepts.
  arXiv  Detail & Related papers  (2024-10-14T04:19:52Z)
• Generalized knowledge-enhanced framework for biomedical entity and relation extraction [0.6856896119187885]
  We develop a novel framework to construct a task-independent and reusable background knowledge graph for biomedical entity and relation extraction. The design of our model is inspired by how humans learn domain-specific topics. Our framework employs such common-knowledge-sharing mechanism to build a general neural-network knowledge graph that is learning transferable to different domain-specific biomedical texts effectively.
  arXiv  Detail & Related papers  (2024-08-13T04:06:45Z)
• Knowledge-enhanced Visual-Language Pretraining for Computational Pathology [68.6831438330526]
  We consider the problem of visual representation learning for computational pathology, by exploiting large-scale image-text pairs gathered from public resources. We curate a pathology knowledge tree that consists of 50,470 informative attributes for 4,718 diseases requiring pathology diagnosis from 32 human tissues.
  arXiv  Detail & Related papers  (2024-04-15T17:11:25Z)
• Graph-Based Retriever Captures the Long Tail of Biomedical Knowledge [2.2814097119704058]
  Large language models (LLMs) are transforming the way information is retrieved with vast amounts of knowledge being summarized and presented. LLMs are prone to highlight the most frequently seen pieces of information from the training set and to neglect the rare ones. We introduce a novel information-retrieval method that leverages a knowledge graph to downsample these clusters and mitigate the information overload problem.
  arXiv  Detail & Related papers  (2024-02-19T18:31:11Z)
• MLIP: Enhancing Medical Visual Representation with Divergence Encoder and Knowledge-guided Contrastive Learning [48.97640824497327]
  We propose a novel framework leveraging domain-specific medical knowledge as guiding signals to integrate language information into the visual domain through image-text contrastive learning. Our model includes global contrastive learning with our designed divergence encoder, local token-knowledge-patch alignment contrastive learning, and knowledge-guided category-level contrastive learning with expert knowledge. Notably, MLIP surpasses state-of-the-art methods even with limited annotated data, highlighting the potential of multimodal pre-training in advancing medical representation learning.
  arXiv  Detail & Related papers  (2024-02-03T05:48:50Z)
• Improving Biomedical Abstractive Summarisation with Knowledge Aggregation from Citation Papers [24.481854035628434]
  Existing language models struggle to generate technical summaries that are on par with those produced by biomedical experts. We propose a novel attention-based citation aggregation model that integrates domain-specific knowledge from citation papers. Our model outperforms state-of-the-art approaches and achieves substantial improvements in abstractive biomedical text summarisation.
  arXiv  Detail & Related papers  (2023-10-24T09:56:46Z)
• PathLDM: Text conditioned Latent Diffusion Model for Histopathology [62.970593674481414]
  We introduce PathLDM, the first text-conditioned Latent Diffusion Model tailored for generating high-quality histopathology images. Our approach fuses image and textual data to enhance the generation process. We achieved a SoTA FID score of 7.64 for text-to-image generation on the TCGA-BRCA dataset, significantly outperforming the closest text-conditioned competitor with FID 30.1.
  arXiv  Detail & Related papers  (2023-09-01T22:08:32Z)
• Knowledge-Enhanced Hierarchical Information Correlation Learning for Multi-Modal Rumor Detection [82.94413676131545]
  We propose a novel knowledge-enhanced hierarchical information correlation learning approach (KhiCL) for multi-modal rumor detection. KhiCL exploits cross-modal joint dictionary to transfer the heterogeneous unimodality features into the common feature space. It extracts visual and textual entities from images and text, and designs a knowledge relevance reasoning strategy.
  arXiv  Detail & Related papers  (2023-06-28T06:08:20Z)
• BERT Based Clinical Knowledge Extraction for Biomedical Knowledge Graph Construction and Analysis [0.4893345190925178]
  We propose an end-to-end approach for knowledge extraction and analysis from biomedical clinical notes. The proposed framework can successfully extract relevant structured information with high accuracy.
  arXiv  Detail & Related papers  (2023-04-21T14:45:33Z)
• EBOCA: Evidences for BiOmedical Concepts Association Ontology [55.41644538483948]
  This paper proposes EBOCA, an ontology that describes (i) biomedical domain concepts and associations between them, and (ii) evidences supporting these associations. Test data coming from a subset of DISNET and automatic association extractions from texts has been transformed to create a Knowledge Graph that can be used in real scenarios.
  arXiv  Detail & Related papers  (2022-08-01T18:47:03Z)
• G-MIND: An End-to-End Multimodal Imaging-Genetics Framework for Biomarker Identification and Disease Classification [49.53651166356737]
  We propose a novel deep neural network architecture to integrate imaging and genetics data, as guided by diagnosis, that provides interpretable biomarkers. We have evaluated our model on a population study of schizophrenia that includes two functional MRI (fMRI) paradigms and Single Nucleotide Polymorphism (SNP) data.
  arXiv  Detail & Related papers  (2021-01-27T19:28:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.