Understanding Adverse Biological Effect Predictions Using Knowledge Graphs

• URL: http://arxiv.org/abs/2210.15985v1
• Date: Fri, 28 Oct 2022 08:32:11 GMT
• Title: Understanding Adverse Biological Effect Predictions Using Knowledge Graphs
• Authors: Erik Bryhn Myklebust, Ernesto Jimenez-Ruiz, Jiaoyan Chen, Raoul Wolf, Knut Erik Tollefsen
• Abstract summary: We extrapolate effects based on a knowledge graph (KG) consisting of the most relevant effect data as domain-specific background knowledge. Background knowledge improves the model prediction performance by up to 40% in terms of $R2$ (ie coefficient of determination)
• Score: 11.607236829607135
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Extrapolation of adverse biological (toxic) effects of chemicals is an important contribution to expand available hazard data in (eco)toxicology without the use of animals in laboratory experiments. In this work, we extrapolate effects based on a knowledge graph (KG) consisting of the most relevant effect data as domain-specific background knowledge. An effect prediction model, with and without background knowledge, was used to predict mean adverse biological effect concentration of chemicals as a prototypical type of stressors. The background knowledge improves the model prediction performance by up to 40\% in terms of $R^2$ (\ie coefficient of determination). We use the KG and KG embeddings to provide quantitative and qualitative insights into the predictions. These insights are expected to improve the confidence in effect prediction. Larger scale implementation of such extrapolation models should be expected to support hazard and risk assessment, by simplifying and reducing testing needs.

Related papers

• Research on Adverse Drug Reaction Prediction Model Combining Knowledge Graph Embedding and Deep Learning [36.703773706187256]
  This paper develops an adverse drug reaction prediction model based on knowledge graph embedding and deep learning. The obtained prediction model has good prediction accuracy and stability, and can provide an effective reference for later safe medication guidance.
  arXiv  Detail & Related papers  (2024-07-23T03:25:55Z)
• Smoke and Mirrors in Causal Downstream Tasks [59.90654397037007]
  This paper looks at the causal inference task of treatment effect estimation, where the outcome of interest is recorded in high-dimensional observations. We compare 6 480 models fine-tuned from state-of-the-art visual backbones, and find that the sampling and modeling choices significantly affect the accuracy of the causal estimate. Our results suggest that future benchmarks should carefully consider real downstream scientific questions, especially causal ones.
  arXiv  Detail & Related papers  (2024-05-27T13:26:34Z)
• C-XGBoost: A tree boosting model for causal effect estimation [8.246161706153805]
  Causal effect estimation aims at estimating the Average Treatment Effect as well as the Conditional Average Treatment Effect of a treatment to an outcome from the available data. We propose a new causal inference model, named C-XGBoost, for the prediction of potential outcomes.
  arXiv  Detail & Related papers  (2024-03-31T17:43:37Z)
• Learning to Denoise Biomedical Knowledge Graph for Robust Molecular Interaction Prediction [50.7901190642594]
  We propose BioKDN (Biomedical Knowledge Graph Denoising Network) for robust molecular interaction prediction. BioKDN refines the reliable structure of local subgraphs by denoising noisy links in a learnable manner. It maintains consistent and robust semantics by smoothing relations around the target interaction.
  arXiv  Detail & Related papers  (2023-12-09T07:08:00Z)
• Benchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability [82.29775890542967]
  Estimating personalized effects of treatments is a complex, yet pervasive problem. Recent developments in the machine learning literature on heterogeneous treatment effect estimation gave rise to many sophisticated, but opaque, tools. We use post-hoc feature importance methods to identify features that influence the model's predictions.
  arXiv  Detail & Related papers  (2022-06-16T17:59:05Z)
• Modular multi-source prediction of drug side-effects with DruGNN [3.229607826010618]
  Drug Side-Effects (DSEs) have a high impact on public health, care system costs, and drug discovery processes. To predict their occurrence, it is necessary to integrate data from heterogeneous sources. In this work, such heterogeneous data is integrated into a graph dataset, expressively representing the relational information between different entities. Graph Neural Networks (GNNs) are exploited to predict DSEs on our dataset with very promising results.
  arXiv  Detail & Related papers  (2022-02-15T09:41:05Z)
• Prediction of Adverse Biological Effects of Chemicals Using Knowledge Graph Embeddings [5.1168938454615205]
  We show that using knowledge graph embeddings can increase the accuracy of effect prediction with neural networks. We evaluate certain characteristics of the knowledge graph embedding models to shed light on the individual model performance.
  arXiv  Detail & Related papers  (2021-12-08T22:19:16Z)
• Label scarcity in biomedicine: Data-rich latent factor discovery enhances phenotype prediction [102.23901690661916]
  Low-dimensional embedding spaces can be derived from the UK Biobank population dataset to enhance data-scarce prediction of health indicators, lifestyle and demographic characteristics. Performances gains from semisupervison approaches will probably become an important ingredient for various medical data science applications.
  arXiv  Detail & Related papers  (2021-10-12T16:25:50Z)
• Understanding the Performance of Knowledge Graph Embeddings in Drug Discovery [14.839673015887275]
  Knowledge Graphs (KGs) and associated Knowledge Graph Embedding (KGE) models have recently begun to be explored in the context of drug discovery. In this study we investigate, over the course of many thousands of experiments, the predictive performance of five KGE models on two public drug discovery-oriented KGs. Our results highlight that these factors have significant impact on performance and can even affect the ranking of models.
  arXiv  Detail & Related papers  (2021-05-17T11:39:54Z)
• Influence Functions in Deep Learning Are Fragile [52.31375893260445]
  influence functions approximate the effect of samples in test-time predictions. influence estimates are fairly accurate for shallow networks. Hessian regularization is important to get highquality influence estimates.
  arXiv  Detail & Related papers  (2020-06-25T18:25:59Z)
• Enabling Counterfactual Survival Analysis with Balanced Representations [64.17342727357618]
  Survival data are frequently encountered across diverse medical applications, i.e., drug development, risk profiling, and clinical trials. We propose a theoretically grounded unified framework for counterfactual inference applicable to survival outcomes.
  arXiv  Detail & Related papers  (2020-06-14T01:15:00Z)

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.