Analysing Biomedical Knowledge Graphs using Prime Adjacency Matrices

• URL: http://arxiv.org/abs/2305.10467v1
• Date: Wed, 17 May 2023 13:40:55 GMT
• Title: Analysing Biomedical Knowledge Graphs using Prime Adjacency Matrices
• Authors: Konstantinos Bougiatiotis and Georgios Paliouras
• Abstract summary: We introduce the use of a new representation framework, the Prime Adjacency Matrix (PAM) for biomedical KGs. PAM enables representing the whole KG with a single adjacency matrix and the fast of multiple properties of the network. We show that we achieve better results than the original proposed methods that require no training, in considerably less time.
• Score: 1.6752182911522517
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Most phenomena related to biomedical tasks are inherently complex, and in many cases, are expressed as signals on biomedical Knowledge Graphs (KGs). In this work, we introduce the use of a new representation framework, the Prime Adjacency Matrix (PAM) for biomedical KGs, which allows for very efficient network analysis. PAM utilizes prime numbers to enable representing the whole KG with a single adjacency matrix and the fast computation of multiple properties of the network. We illustrate the applicability of the framework in the biomedical domain by working on different biomedical knowledge graphs and by providing two case studies: one on drug-repurposing for COVID-19 and one on important metapath extraction. We show that we achieve better results than the original proposed workflows, using very simple methods that require no training, in considerably less time.

Related papers

• From Primes to Paths: Enabling Fast Multi-Relational Graph Analysis [5.008498268411793]
  Multi-relational networks capture intricate relationships in data and have diverse applications across fields such as biomedical, financial, and social sciences. This work extends the Prime Adjacency Matrices framework, which employs prime numbers to represent distinct relations within a network uniquely.
  arXiv  Detail & Related papers  (2024-11-17T18:43:01Z)
• Graph Relation Distillation for Efficient Biomedical Instance Segmentation [80.51124447333493]
  We propose a graph relation distillation approach for efficient biomedical instance segmentation. We introduce two graph distillation schemes deployed at both the intra-image level and the inter-image level. Experimental results on a number of biomedical datasets validate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2024-01-12T04:41:23Z)
• Diversifying Knowledge Enhancement of Biomedical Language Models using Adapter Modules and Knowledge Graphs [54.223394825528665]
  We develop an approach that uses lightweight adapter modules to inject structured biomedical knowledge into pre-trained language models. We use two large KGs, the biomedical knowledge system UMLS and the novel biochemical OntoChem, with two prominent biomedical PLMs, PubMedBERT and BioLinkBERT. We show that our methodology leads to performance improvements in several instances while keeping requirements in computing power low.
  arXiv  Detail & Related papers  (2023-12-21T14:26:57Z)
• Neurosymbolic AI for Reasoning on Biomedical Knowledge Graphs [0.9085310904484414]
  Biomedical datasets are often modeled as knowledge graphs (KGs) because they capture the multi-relational, heterogeneous, and dynamic natures of biomedical systems. KG completion (KGC), can, therefore, help researchers make predictions to inform tasks like drug repositioning.
  arXiv  Detail & Related papers  (2023-07-17T11:47:05Z)
• 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)
• SEMPAI: a Self-Enhancing Multi-Photon Artificial Intelligence for prior-informed assessment of muscle function and pathology [48.54269377408277]
  We introduce the Self-Enhancing Multi-Photon Artificial Intelligence (SEMPAI), that integrates hypothesis-driven priors in a data-driven Deep Learning approach. SEMPAI performs joint learning of several tasks to enable prediction for small datasets. SEMPAI outperforms state-of-the-art biomarkers in six of seven predictive tasks, including those with scarce data.
  arXiv  Detail & Related papers  (2022-10-28T17:03:04Z)
• BioIE: Biomedical Information Extraction with Multi-head Attention Enhanced Graph Convolutional Network [9.227487525657901]
  We propose Biomedical Information Extraction, a hybrid neural network to extract relations from biomedical text and unstructured medical reports. We evaluate our model on two major biomedical relationship extraction tasks, chemical-disease relation and chemical-protein interaction, and a cross-hospital pan-cancer pathology report corpus.
  arXiv  Detail & Related papers  (2021-10-26T13:19:28Z)
• Scientific Language Models for Biomedical Knowledge Base Completion: An Empirical Study [62.376800537374024]
  We study scientific LMs for KG completion, exploring whether we can tap into their latent knowledge to enhance biomedical link prediction. We integrate the LM-based models with KG embedding models, using a router method that learns to assign each input example to either type of model and provides a substantial boost in performance.
  arXiv  Detail & Related papers  (2021-06-17T17:55:33Z)
• Biomedical Knowledge Graph Refinement and Completion using Graph Representation Learning and Top-K Similarity Measure [1.4660617536303606]
  This work demonstrates learning discrete representations of the integrated biomedical knowledge graph Chem2Bio2RD. We perform a knowledge graph completion and refinement task using a simple top-K cosine similarity measure between the learned embedding vectors.
  arXiv  Detail & Related papers  (2020-12-18T22:19:57Z)
• Cross-Modal Information Maximization for Medical Imaging: CMIM [62.28852442561818]
  In hospitals, data are siloed to specific information systems that make the same information available under different modalities. This offers unique opportunities to obtain and use at train-time those multiple views of the same information that might not always be available at test-time. We propose an innovative framework that makes the most of available data by learning good representations of a multi-modal input that are resilient to modality dropping at test-time.
  arXiv  Detail & Related papers  (2020-10-20T20:05:35Z)
• SumGNN: Multi-typed Drug Interaction Prediction via Efficient Knowledge Graph Summarization [64.56399911605286]
  We propose SumGNN: knowledge summarization graph neural network, which is enabled by a subgraph extraction module. SumGNN outperforms the best baseline by up to 5.54%, and the performance gain is particularly significant in low data relation types.
  arXiv  Detail & Related papers  (2020-10-04T00:14:57Z)

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.