Unsupervised Feature Selection for Tumor Profiles using Autoencoders and Kernel Methods

• URL: http://arxiv.org/abs/2007.06106v1
• Date: Sun, 12 Jul 2020 21:59:05 GMT
• Title: Unsupervised Feature Selection for Tumor Profiles using Autoencoders and Kernel Methods
• Authors: Martin Palazzo, Pierre Beauseroy, Patricio Yankilevich
• Abstract summary: This work aims to learn meaningful and low dimensional representations of tumor samples and find tumor subtype clusters. The proposed method named Latent Kernel Feature Selection (LKFS) is an unsupervised approach for gene selection in tumor gene expression profiles.
• Score: 1.9078991171384014
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Molecular data from tumor profiles is high dimensional. Tumor profiles can be characterized by tens of thousands of gene expression features. Due to the size of the gene expression feature set machine learning methods are exposed to noisy variables and complexity. Tumor types present heterogeneity and can be subdivided in tumor subtypes. In many cases tumor data does not include tumor subtype labeling thus unsupervised learning methods are necessary for tumor subtype discovery. This work aims to learn meaningful and low dimensional representations of tumor samples and find tumor subtype clusters while keeping biological signatures without using tumor labels. The proposed method named Latent Kernel Feature Selection (LKFS) is an unsupervised approach for gene selection in tumor gene expression profiles. By using Autoencoders a low dimensional and denoised latent space is learned as a target representation to guide a Multiple Kernel Learning model that selects a subset of genes. By using the selected genes a clustering method is used to group samples. In order to evaluate the performance of the proposed unsupervised feature selection method the obtained features and clusters are analyzed by clinical significance. The proposed method has been applied on three tumor datasets which are Brain, Renal and Lung, each one composed by two tumor subtypes. When compared with benchmark unsupervised feature selection methods the results obtained by the proposed method reveal lower redundancy in the selected features and a better clustering performance.

Related papers

• An Evolutional Neural Network Framework for Classification of Microarray Data [0.0]
  This research aims to apply a hybrid model of Genetic Algorithm and Neural Network to overcome the problem during subset selection of informative genes. Experimental results show the proposed method suggested high accuracy and minimum number of selected genes in comparison with other machine learning algorithms.
  arXiv  Detail & Related papers  (2024-11-20T13:48:40Z)
• A Weakly Supervised and Globally Explainable Learning Framework for Brain Tumor Segmentation [8.253446049933483]
  Machine-based brain tumor segmentation can help doctors make better diagnoses. The complex structure of brain tumors and expensive pixel-level annotations present challenges for automatic tumor segmentation. We propose a counterfactual generation framework that achieves exceptional brain tumor segmentation performance without the need for pixel-level annotations.
  arXiv  Detail & Related papers  (2024-08-02T11:18:32Z)
• Exhaustive Exploitation of Nature-inspired Computation for Cancer Screening in an Ensemble Manner [20.07173196364489]
  This study presents a framework termed Evolutionary Optimized Diverse Ensemble Learning (EODE) to improve ensemble learning for cancer classification from gene expression data. Experiments were conducted across 35 gene expression benchmark datasets encompassing varied cancer types.
  arXiv  Detail & Related papers  (2024-04-06T08:07:48Z)
• 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)
• 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)
• Triplet Contrastive Learning for Brain Tumor Classification [99.07846518148494]
  We present a novel approach of directly learning deep embeddings for brain tumor types, which can be used for downstream tasks such as classification. We evaluate our method on an extensive brain tumor dataset which consists of 27 different tumor classes, out of which 13 are defined as rare.
  arXiv  Detail & Related papers  (2021-08-08T11:26:34Z)
• Cancer Gene Profiling through Unsupervised Discovery [49.28556294619424]
  We introduce a novel, automatic and unsupervised framework to discover low-dimensional gene biomarkers. Our method is based on the LP-Stability algorithm, a high dimensional center-based unsupervised clustering algorithm. Our signature reports promising results on distinguishing immune inflammatory and immune desert tumors.
  arXiv  Detail & Related papers  (2021-02-11T09:04:45Z)
• Topological Data Analysis of copy number alterations in cancer [70.85487611525896]
  We explore the potential to capture information contained in cancer genomic information using a novel topology-based approach. We find that this technique has the potential to extract meaningful low-dimensional representations in cancer somatic genetic data.
  arXiv  Detail & Related papers  (2020-11-22T17:31:23Z)
• 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)
• Latent regularization for feature selection using kernel methods in tumor classification [1.9078991171384014]
  Feature selection is a useful approach to select the key genes which helps to classify tumors. We propose a feature selection method based on Multiple Kernel Learning that results in a reduced subset of genes and a custom kernel. An improvement of the generalization capacity is obtained and assessed by the tumor classification performance on new unseen test samples.
  arXiv  Detail & Related papers  (2020-04-10T00:46:02Z)
• A New Gene Selection Algorithm using Fuzzy-Rough Set Theory for Tumor Classification [0.0]
  We present a new technique for gene selection using a discernibility matrix of fuzzy-rough sets. The proposed technique takes into account the similarity of those instances that have the same and different class labels to improve the gene selection results. Experimental results demonstrate that this technique provides better efficiency compared to the state-of-the-art approaches.
  arXiv  Detail & Related papers  (2020-03-26T13:43:25Z)

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.