Genetic heterogeneity analysis using genetic algorithm and network
science
- URL: http://arxiv.org/abs/2308.06429v1
- Date: Sat, 12 Aug 2023 01:28:26 GMT
- Title: Genetic heterogeneity analysis using genetic algorithm and network
science
- Authors: Zhendong Sha, Yuanzhu Chen, Ting Hu
- Abstract summary: 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)
- Score: 2.6166087473624318
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Through genome-wide association studies (GWAS), disease susceptible genetic
variables can be identified by comparing the genetic data of individuals with
and without a specific disease. However, the discovery of these associations
poses a significant challenge due to genetic heterogeneity and feature
interactions. Genetic variables intertwined with these effects often exhibit
lower effect-size, and thus can be difficult to be detected using machine
learning feature selection methods. To address these challenges, this paper
introduces a novel feature selection mechanism for GWAS, named Feature
Co-selection Network (FCSNet). FCS-Net is designed to extract heterogeneous
subsets of genetic variables from a network constructed from multiple
independent feature selection runs based on a genetic algorithm (GA), an
evolutionary learning algorithm. We employ a non-linear machine learning
algorithm to detect feature interaction. We introduce the Community Risk Score
(CRS), a synthetic feature designed to quantify the collective disease
association of each variable subset. Our experiment showcases the effectiveness
of the utilized GA-based feature selection method in identifying feature
interactions through synthetic data analysis. Furthermore, we apply our novel
approach to a case-control colorectal cancer GWAS dataset. The resulting
synthetic features are then used to explain the genetic heterogeneity in an
additional case-only GWAS dataset.
Related papers
- 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) - Single-Cell Deep Clustering Method Assisted by Exogenous Gene
Information: A Novel Approach to Identifying Cell Types [50.55583697209676]
We develop an attention-enhanced graph autoencoder, which is designed to efficiently capture the topological features between cells.
During the clustering process, we integrated both sets of information and reconstructed the features of both cells and genes to generate a discriminative representation.
This research offers enhanced insights into the characteristics and distribution of cells, thereby laying the groundwork for early diagnosis and treatment of diseases.
arXiv Detail & Related papers (2023-11-28T09:14:55Z) - Causal machine learning for single-cell genomics [94.28105176231739]
We discuss the application of machine learning techniques to single-cell genomics and their challenges.
We first present the model that underlies most of current causal approaches to single-cell biology.
We then identify open problems in the application of causal approaches to single-cell data.
arXiv Detail & Related papers (2023-10-23T13:35:24Z) - Genetic InfoMax: Exploring Mutual Information Maximization in
High-Dimensional Imaging Genetics Studies [50.11449968854487]
Genome-wide association studies (GWAS) are used to identify relationships between genetic variations and specific traits.
Representation learning for imaging genetics is largely under-explored due to the unique challenges posed by GWAS.
We introduce a trans-modal learning framework Genetic InfoMax (GIM) to address the specific challenges of GWAS.
arXiv Detail & Related papers (2023-09-26T03:59:21Z) - Fast and Functional Structured Data Generators Rooted in
Out-of-Equilibrium Physics [62.997667081978825]
We address the challenge of using energy-based models to produce high-quality, label-specific data in structured datasets.
Traditional training methods encounter difficulties due to inefficient Markov chain Monte Carlo mixing.
We use a novel training algorithm that exploits non-equilibrium effects.
arXiv Detail & Related papers (2023-07-13T15:08:44Z) - Cancer-inspired Genomics Mapper Model for the Generation of Synthetic
DNA Sequences with Desired Genomics Signatures [0.0]
Cancer-inspired genomics mapper model (CGMM) combines genetic algorithm (GA) and deep learning (DL) methods.
We demonstrate that CGMM can generate synthetic genomes of selected phenotypes such as ancestry and cancer.
arXiv Detail & Related papers (2023-05-01T07:16:40Z) - High-dimensional multi-trait GWAS by reverse prediction of genotypes [3.441021278275805]
Reverse regression is a promising approach to perform multi-trait GWAS in high-dimensional settings.
We analyzed different machine learning methods for reverse regression in multi-trait GWAS.
Model feature coefficients correlated with the strength of association between variants and individual traits, and were predictive of true trans-eQTL target genes.
arXiv Detail & Related papers (2021-10-29T22:34:35Z) - 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) - Expectile Neural Networks for Genetic Data Analysis of Complex Diseases [3.0088453915399747]
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.
arXiv Detail & Related papers (2020-10-26T21:07:40Z) - 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) - 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.