Expectile Neural Networks for Genetic Data Analysis of Complex Diseases

• URL: http://arxiv.org/abs/2010.13898v1
• Date: Mon, 26 Oct 2020 21:07:40 GMT
• Title: Expectile Neural Networks for Genetic Data Analysis of Complex Diseases
• Authors: Jinghang Lin, Xiaoran Tong, Chenxi Li, Qing Lu
• Abstract summary: We develop an expectile neural network (ENN) method for genetic data analyses of complex diseases. Similar to expectile regression, ENN provides a comprehensive view of relationships between genetic variants and disease phenotypes. We show that the proposed method outperformed an existing expectile regression when there exist complex relationships between genetic variants and disease phenotypes.
• Score: 3.0088453915399747
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The genetic etiologies of common diseases are highly complex and heterogeneous. Classic statistical methods, such as linear regression, have successfully identified numerous genetic variants associated with complex diseases. Nonetheless, for most complex diseases, the identified variants only account for a small proportion of heritability. Challenges remain to discover additional variants contributing to complex diseases. Expectile regression is a generalization of linear regression and provides completed information on the conditional distribution of a phenotype of interest. While expectile regression has many nice properties and holds great promise for genetic data analyses (e.g., investigating genetic variants predisposing to a high-risk population), it has been rarely used in genetic research. In this paper, we develop an expectile neural network (ENN) method for genetic data analyses of complex diseases. Similar to expectile regression, ENN provides a comprehensive view of relationships between genetic variants and disease phenotypes and can be used to discover genetic variants predisposing to sub-populations (e.g., high-risk groups). We further integrate the idea of neural networks into ENN, making it capable of capturing non-linear and non-additive genetic effects (e.g., gene-gene interactions). Through simulations, we showed that the proposed method outperformed an existing expectile regression when there exist complex relationships between genetic variants and disease phenotypes. We also applied the proposed method to the genetic data from the Study of Addiction: Genetics and Environment(SAGE), investigating the relationships of candidate genes with smoking quantity.

Related papers

• Nonparametric independence tests in high-dimensional settings, with applications to the genetics of complex disease [55.2480439325792]
  We show how defining adequate premetric structures on the support spaces of the genetic data allows for novel approaches to such testing. For each problem, we provide mathematical results, simulations and the application to real data.
  arXiv  Detail & Related papers  (2024-07-29T01:00:53Z)
• Interpreting artificial neural networks to detect genome-wide association signals for complex traits [0.0]
  Investigating the genetic architecture of complex diseases is challenging due to the highly polygenic and interactive landscape of genetic and environmental factors. We trained artificial neural networks for predicting complex traits using both simulated and real genotype/phenotype datasets.
  arXiv  Detail & Related papers  (2024-07-26T15:20:42Z)
• Predicting Genetic Mutation from Whole Slide Images via Biomedical-Linguistic Knowledge Enhanced Multi-label Classification [119.13058298388101]
  We develop a Biological-knowledge enhanced PathGenomic multi-label Transformer to improve genetic mutation prediction performances. BPGT first establishes a novel gene encoder that constructs gene priors by two carefully designed modules. BPGT then designs a label decoder that finally performs genetic mutation prediction by two tailored modules.
  arXiv  Detail & Related papers  (2024-06-05T06:42:27Z)
• Genetic heterogeneity analysis using genetic algorithm and network science [2.6166087473624318]
  Genome-wide association studies (GWAS) can identify disease susceptible genetic variables. Genetic variables intertwined with genetic effects often exhibit lower effect-size. This paper introduces a novel feature selection mechanism for GWAS, named Feature Co-selection Network (FCSNet)
  arXiv  Detail & Related papers  (2023-08-12T01:28:26Z)
• Machine Learning Methods for Cancer Classification Using Gene Expression Data: A Review [77.34726150561087]
  Cancer is the second major cause of death after cardiovascular diseases. Gene expression can play a fundamental role in the early detection of cancer. This study reviews recent progress in gene expression analysis for cancer classification using machine learning methods.
  arXiv  Detail & Related papers  (2023-01-28T15:03:03Z)
• Unsupervised ensemble-based phenotyping helps enhance the discoverability of genes related to heart morphology [57.25098075813054]
  We propose a new framework for gene discovery entitled Un Phenotype Ensembles. It builds a redundant yet highly expressive representation by pooling a set of phenotypes learned in an unsupervised manner. These phenotypes are then analyzed via (GWAS), retaining only highly confident and stable associations.
  arXiv  Detail & Related papers  (2023-01-07T18:36:44Z)
• rfPhen2Gen: A machine learning based association study of brain imaging phenotypes to genotypes [71.1144397510333]
  We learned machine learning models to predict SNPs using 56 brain imaging QTs. SNPs within the known Alzheimer disease (AD) risk gene APOE had lowest RMSE for lasso and random forest. Random forests identified additional SNPs that were not prioritized by the linear models but are known to be associated with brain-related disorders.
  arXiv  Detail & Related papers  (2022-03-31T20:15:22Z)
• VEGN: Variant Effect Prediction with Graph Neural Networks [19.59965282985234]
  We propose VEGN, which models variant effect prediction using a graph neural network (GNN) that operates on a heterogeneous graph with genes and variants. The graph is created by assigning variants to genes and connecting genes with an gene-gene interaction network. VeGN improves the performance of existing state-of-the-art models.
  arXiv  Detail & Related papers  (2021-06-25T13:51:46Z)
• Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction [55.94378672172967]
  We focus on few-shot disease subtype prediction problem, identifying subgroups of similar patients. We introduce meta learning techniques to develop a new model, which can extract the common experience or knowledge from interrelated clinical tasks. Our new model is built upon a carefully designed meta-learner, called Prototypical Network, that is a simple yet effective meta learning machine for few-shot image classification.
  arXiv  Detail & Related papers  (2020-09-02T02:50:30Z)
• Handling highly correlated genes in prediction analysis of genomic studies [0.0]
  High correlation among genes introduces technical problems, such as multi-collinearity issues, leading to unreliable prediction models. We propose a grouping algorithm, which treats highly correlated genes as a group and uses their common pattern to represent the group's biological signal in feature selection. Our proposed grouping method has two advantages. First, using the gene group's common patterns makes the prediction more robust and reliable under condition change.
  arXiv  Detail & Related papers  (2020-07-05T22:14:03Z)
• A Semi-Supervised Generative Adversarial Network for Prediction of Genetic Disease Outcomes [0.0]
  We introduce genetic Generative Adversarial Networks (gGAN) to create large synthetic genetic data sets. Our goal is to determine the propensity of a new individual to develop the severe form of the illness from their genetic profile alone. The proposed model is self-aware and capable of determining whether a new genetic profile has enough compatibility with the data on which the network was trained.
  arXiv  Detail & Related papers  (2020-07-02T15:35:14Z)

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.