Spinal Muscle Atrophy Disease Modelling as Bayesian Network

• URL: http://arxiv.org/abs/2311.17521v1
• Date: Wed, 29 Nov 2023 10:45:27 GMT
• Title: Spinal Muscle Atrophy Disease Modelling as Bayesian Network
• Authors: Mohammed Ezzat Helal, Manal Ezzat Helal, Sherif Fadel Fahmy
• Abstract summary: We investigate the molecular gene expressions studies and public databases for disease modelling using Probabilistic Graphical Models and Bayesian Inference. A case study on Spinal Muscle Atrophy Genome-Wide Association Study results is modelled and analyzed. The genes up and down-regulated in two stages of the disease development are linked to prior knowledge published in the public domain and co-expressions network is created and analyzed.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We investigate the molecular gene expressions studies and public databases for disease modelling using Probabilistic Graphical Models and Bayesian Inference. A case study on Spinal Muscle Atrophy Genome-Wide Association Study results is modelled and analyzed. The genes up and down-regulated in two stages of the disease development are linked to prior knowledge published in the public domain and co-expressions network is created and analyzed. The Molecular Pathways triggered by these genes are identified. The Bayesian inference posteriors distributions are estimated using a variational analytical algorithm and a Markov chain Monte Carlo sampling algorithm. Assumptions, limitations and possible future work are concluded.

Related papers

• GENERator: A Long-Context Generative Genomic Foundation Model [66.46537421135996]
  We present GENERator, a generative genomic foundation model featuring a context length of 98k base pairs (bp) and 1.2B parameters. Trained on an expansive dataset comprising 386B bp of DNA, the GENERator demonstrates state-of-the-art performance across both established and newly proposed benchmarks. It also shows significant promise in sequence optimization, particularly through the prompt-responsive generation of enhancer sequences with specific activity profiles.
  arXiv  Detail & Related papers  (2025-02-11T05:39:49Z)
• Spectral Graph Sample Weighting for Interpretable Sub-cohort Analysis in Predictive Models for Neuroimaging [11.60042534415617]
  We propose to model the subject weights as a linear combination of the eigenbases of a spectral population graph. In doing so, the learned weights smoothly vary across the graph, highlighting sub-cohorts with high and low predictability. Compared to existing sample weighting schemes, our sample weights improve interpretability and highlight sub-cohorts with distinct characteristics.
  arXiv  Detail & Related papers  (2024-10-01T16:48:15Z)
• Stacked ensemble\-based mutagenicity prediction model using multiple modalities with graph attention network [0.9736758288065405]
  Mutagenicity is a concern due to its association with genetic mutations which can result in a variety of negative consequences. In this work, we introduce a novel stacked ensemble based mutagenicity prediction model.
  arXiv  Detail & Related papers  (2024-09-03T09:14:21Z)
• Seeing Unseen: Discover Novel Biomedical Concepts via Geometry-Constrained Probabilistic Modeling [53.7117640028211]
  We present a geometry-constrained probabilistic modeling treatment to resolve the identified issues. We incorporate a suite of critical geometric properties to impose proper constraints on the layout of constructed embedding space. A spectral graph-theoretic method is devised to estimate the number of potential novel classes.
  arXiv  Detail & Related papers  (2024-03-02T00:56:05Z)
• 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)
• Bayesian Networks for the robust and unbiased prediction of depression and its symptoms utilizing speech and multimodal data [65.28160163774274]
  We apply a Bayesian framework to capture the relationships between depression, depression symptoms, and features derived from speech, facial expression and cognitive game data collected at thymia.
  arXiv  Detail & Related papers  (2022-11-09T14:48:13Z)
• 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)
• Pan-Cancer Integrative Histology-Genomic Analysis via Interpretable Multimodal Deep Learning [4.764927152701701]
  We integrate whole slide pathology images, RNA-seq abundance, copy number variation, and mutation data from 5,720 patients across 14 major cancer types. Our interpretable, weakly-supervised, multimodal deep learning algorithm is able to fuse these heterogeneous modalities for predicting outcomes. We analyze morphologic and molecular markers responsible for prognostic predictions across all cancer types.
  arXiv  Detail & Related papers  (2021-08-04T20:40:05Z)
• A multi-stage machine learning model on diagnosis of esophageal manometry [50.591267188664666]
  The framework includes deep-learning models at the swallow-level stage and feature-based machine learning models at the study-level stage. This is the first artificial-intelligence-style model to automatically predict CC diagnosis of HRM study from raw multi-swallow data.
  arXiv  Detail & Related papers  (2021-06-25T20:09:23Z)
• 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)
• Recent Advances in Network-based Methods for Disease Gene Prediction [15.625526953844638]
  Disease-gene association through Genome-wide association study (GWAS) is an arduous task for researchers. To provide the researchers with alternative low-cost disease-gene association evidence, computational approaches come into play. Since molecular networks are able to capture complex interplay among molecules in diseases, they become one of the most extensively used data for disease-gene association prediction.
  arXiv  Detail & Related papers  (2020-07-19T14:13:38Z)
• A Sparse Graph-Structured Lasso Mixed Model for Genetic Association with Confounding Correction [28.364820868064893]
  We propose the sparse graph-structured linear mixed model (sGLMM) that can incorporate the relatedness information from traits in a dataset with confounding correction. We show that the proposed model outperforms other existing approaches and can model correlation from both population structure and shared signals. We also discuss the potential causal genetic variation of Human Alzheimer's disease discovered by our model and justify some of the most important genetic loci.
  arXiv  Detail & Related papers  (2017-11-11T16:01:53Z)

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.