Related papers: Unsupervised Feature Selection for Tumor Profiles using Autoencoders and Kernel Methods

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

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)
Automated Cancer Subtyping via Vector Quantization Mutual Information Maximization [10.191396978971168]
We propose a novel clustering method for exploiting genetic expression profiles and distinguishing subtypes in an unsupervised manner. Our method can refine existing controversial labels, and, by further medical analysis, this refinement is proven to have a high correlation with cancer survival rates.
arXiv Detail & Related papers (2022-06-22T01:55:08Z)
Optimize Deep Learning Models for Prediction of Gene Mutations Using Unsupervised Clustering [6.494144125433731]
Deep learning has become the mainstream methodological choice for analyzing and interpreting whole-slide digital pathology images. In this paper, we proposed an unsupervised clustering-based multiple-instance learning, and apply our method to develop deep-learning models for prediction of gene mutations using WSIs from three cancer types. We showed that unsupervised clustering of image patches could help identify predictive patches, exclude patches lack of predictive information, and therefore improve prediction on gene mutations in all three different cancer types.
arXiv Detail & Related papers (2022-03-31T11:48:21Z)
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.