VICatMix: variational Bayesian clustering and variable selection for discrete biomedical data

• URL: http://arxiv.org/abs/2406.16227v1
• Date: Sun, 23 Jun 2024 21:45:04 GMT
• Title: VICatMix: variational Bayesian clustering and variable selection for discrete biomedical data
• Authors: Paul D. W. Kirk, Jackie Rao,
• Abstract summary: We present VICatMix, a variational Bayesian finite mixture model designed for the clustering of categorical data. The proposed model incorporates summarisation and model averaging to mitigate poor local optima in VI, allowing for improved estimation of the true number of clusters. We demonstrate VICatMix's utility in integrative cluster analysis with different omics datasets, enabling the discovery of novel subtypes.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Effective clustering of biomedical data is crucial in precision medicine, enabling accurate stratifiction of patients or samples. However, the growth in availability of high-dimensional categorical data, including `omics data, necessitates computationally efficient clustering algorithms. We present VICatMix, a variational Bayesian finite mixture model designed for the clustering of categorical data. The use of variational inference (VI) in its training allows the model to outperform competitors in term of efficiency, while maintaining high accuracy. VICatMix furthermore performs variable selection, enhancing its performance on high-dimensional, noisy data. The proposed model incorporates summarisation and model averaging to mitigate poor local optima in VI, allowing for improved estimation of the true number of clusters simultaneously with feature saliency. We demonstrate the performance of VICatMix with both simulated and real-world data, including applications to datasets from The Cancer Genome Atlas (TCGA), showing its use in cancer subtyping and driver gene discovery. We demonstrate VICatMix's utility in integrative cluster analysis with different `omics datasets, enabling the discovery of novel subtypes. \textbf{Availability:} VICatMix is freely available as an R package, incorporating C++ for faster computation, at \url{https://github.com/j-ackierao/VICatMix}.

Related papers

• Predicting Drug Effects from High-Dimensional, Asymmetric Drug Datasets by Using Graph Neural Networks: A Comprehensive Analysis of Multitarget Drug Effect Prediction [1.1970409518725493]
  Graph neural networks (GNNs) have emerged as one of the most effective ML techniques for drug effect prediction from drug molecular graphs. Despite having immense potential, GNN models lack performance when using datasets that contain high-dimensional, asymmetrically co-occurrent drug effects. We propose a new data oversampling technique to improve multilabel classification performances on all the given imbalanced molecular graph datasets.
  arXiv  Detail & Related papers  (2024-10-11T22:09:29Z)
• Artificial Data Point Generation in Clustered Latent Space for Small Medical Datasets [4.542616945567623]
  This paper introduces a novel method, Artificial Data Point Generation in Clustered Latent Space (AGCL) AGCL is designed to enhance classification performance on small medical datasets through synthetic data generation. It was applied to Parkinson's disease screening, utilizing facial expression data.
  arXiv  Detail & Related papers  (2024-09-26T09:51:08Z)
• An improved tabular data generator with VAE-GMM integration [9.4491536689161]
  We propose a novel Variational Autoencoder (VAE)-based model that addresses limitations of current approaches. Inspired by the TVAE model, our approach incorporates a Bayesian Gaussian Mixture model (BGM) within the VAE architecture. We thoroughly validate our model on three real-world datasets with mixed data types, including two medically relevant ones.
  arXiv  Detail & Related papers  (2024-04-12T12:31:06Z)
• The effect of data augmentation and 3D-CNN depth on Alzheimer's Disease detection [51.697248252191265]
  This work summarizes and strictly observes best practices regarding data handling, experimental design, and model evaluation. We focus on Alzheimer's Disease (AD) detection, which serves as a paradigmatic example of challenging problem in healthcare. Within this framework, we train predictive 15 models, considering three different data augmentation strategies and five distinct 3D CNN architectures.
  arXiv  Detail & Related papers  (2023-09-13T10:40:41Z)
• Self-Evolution Learning for Mixup: Enhance Data Augmentation on Few-Shot Text Classification Tasks [75.42002070547267]
  We propose a self evolution learning (SE) based mixup approach for data augmentation in text classification. We introduce a novel instance specific label smoothing approach, which linearly interpolates the model's output and one hot labels of the original samples to generate new soft for label mixing up.
  arXiv  Detail & Related papers  (2023-05-22T23:43:23Z)
• Boosting Differentiable Causal Discovery via Adaptive Sample Reweighting [62.23057729112182]
  Differentiable score-based causal discovery methods learn a directed acyclic graph from observational data. We propose a model-agnostic framework to boost causal discovery performance by dynamically learning the adaptive weights for the Reweighted Score function, ReScore.
  arXiv  Detail & Related papers  (2023-03-06T14:49:59Z)
• Learning with MISELBO: The Mixture Cookbook [62.75516608080322]
  We present the first ever mixture of variational approximations for a normalizing flow-based hierarchical variational autoencoder (VAE) with VampPrior and a PixelCNN decoder network. We explain this cooperative behavior by drawing a novel connection between VI and adaptive importance sampling. We obtain state-of-the-art results among VAE architectures in terms of negative log-likelihood on the MNIST and FashionMNIST datasets.
  arXiv  Detail & Related papers  (2022-09-30T15:01:35Z)
• Adversarially-regularized mixed effects deep learning (ARMED) models for improved interpretability, performance, and generalization on clustered data [0.974672460306765]
  Mixed effects models separate cluster-invariant, population-level fixed effects from cluster-specific random effects. We propose a general-purpose framework for building Adversarially-Regularized Mixed Effects Deep learning (ARMED) models through 3 non-intrusive additions to existing networks. We apply this framework to dense feedforward neural networks (DFNNs), convolutional neural networks, and autoencoders on 4 applications including simulations, dementia prognosis and diagnosis, and cell microscopy.
  arXiv  Detail & Related papers  (2022-02-23T20:58:22Z)
• No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data [78.69828864672978]
  A central challenge in training classification models in the real-world federated system is learning with non-IID data. We propose a novel and simple algorithm called Virtual Representations (CCVR), which adjusts the classifier using virtual representations sampled from an approximated ssian mixture model. Experimental results demonstrate that CCVR state-of-the-art performance on popular federated learning benchmarks including CIFAR-10, CIFAR-100, and CINIC-10.
  arXiv  Detail & Related papers  (2021-06-09T12:02:29Z)
• Robust Finite Mixture Regression for Heterogeneous Targets [70.19798470463378]
  We propose an FMR model that finds sample clusters and jointly models multiple incomplete mixed-type targets simultaneously. We provide non-asymptotic oracle performance bounds for our model under a high-dimensional learning framework. The results show that our model can achieve state-of-the-art performance.
  arXiv  Detail & Related papers  (2020-10-12T03:27:07Z)
• iCVI-ARTMAP: Accelerating and improving clustering using adaptive resonance theory predictive mapping and incremental cluster validity indices [1.160208922584163]
  iCVI-ARTMAP uses incremental cluster validity indices (iCVIs) to perform unsupervised learning. It can achieve running times up to two orders of magnitude shorter than when using batch CVI computations.
  arXiv  Detail & Related papers  (2020-08-22T19:37:01Z)

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.