Comprehensive survey of computational learning methods for analysis of gene expression data in genomics

• URL: http://arxiv.org/abs/2202.02958v2
• Date: Wed, 9 Feb 2022 15:51:09 GMT
• Title: Comprehensive survey of computational learning methods for analysis of gene expression data in genomics
• Authors: Nikita Bhandari, Rahee Walambe, Ketan Kotecha, Satyajeet Khare
• Abstract summary: Computational analysis methods including machine learning have a significant impact in the fields of genomics and medicine. In this review, we compile various statistical and computational tools used in analysis of expression microarray data. We specifically discuss methods for missing value (gene expression) imputation, feature gene scaling, selection and extraction of features for dimensionality reduction, and learning and analysis of expression data.
• Score: 7.717214217542406
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Computational analysis methods including machine learning have a significant impact in the fields of genomics and medicine. High-throughput gene expression analysis methods such as microarray technology and RNA sequencing produce enormous amounts of data. Traditionally, statistical methods are used for comparative analysis of the gene expression data. However, more complex analysis for classification and discovery of feature genes or sample observations requires sophisticated computational approaches. In this review, we compile various statistical and computational tools used in analysis of expression microarray data. Even though, the methods are discussed in the context of expression microarray data, they can also be applied for the analysis of RNA sequencing or quantitative proteomics datasets. We specifically discuss methods for missing value (gene expression) imputation, feature gene scaling, selection and extraction of features for dimensionality reduction, and learning and analysis of expression data. We discuss the types of missing values and the methods and approaches usually employed in their imputation. We also discuss methods of data transformation and feature scaling viz. normalization and standardization. Various approaches used in feature selection and extraction are also reviewed. Lastly, learning and analysis methods including class comparison, class prediction, and class discovery along with their evaluation parameters are described in detail. We have described the process of generation of a microarray gene expression data along with advantages and limitations of the above-mentioned techniques. We believe that this detailed review will help the users to select appropriate methods based on the type of data and the expected outcome.

Related papers

• Robust Multi-view Co-expression Network Inference [8.697303234009528]
  Inferring gene co-expression networks from transcriptome data presents many challenges. We introduce a robust method for high-dimensional graph inference from multiple independent studies.
  arXiv  Detail & Related papers  (2024-09-30T06:30:09Z)
• 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)
• RandomSCM: interpretable ensembles of sparse classifiers tailored for omics data [59.4141628321618]
  We propose an ensemble learning algorithm based on conjunctions or disjunctions of decision rules. The interpretability of the models makes them useful for biomarker discovery and patterns discovery in high dimensional data.
  arXiv  Detail & Related papers  (2022-08-11T13:55:04Z)
• Natural language processing for clusterization of genes according to their functions [62.997667081978825]
  We propose an approach that reduces the analysis of several thousand genes to analysis of several clusters. The descriptions are encoded as vectors using the pretrained language model (BERT) and some text processing approaches.
  arXiv  Detail & Related papers  (2022-07-17T12:59:34Z)
• Using ontology embeddings for structural inductive bias in gene expression data analysis [6.587739898387445]
  Stratifying cancer patients based on their gene expression levels allows improving diagnosis, survival analysis and treatment planning. We propose to incorporate biological knowledge about genes into the machine learning system for the task of patient classification given their gene expression data.
  arXiv  Detail & Related papers  (2020-11-22T12:13:29Z)
• Mining Functionally Related Genes with Semi-Supervised Learning [0.0]
  We introduce a rich set of features and use them in conjunction with semisupervised learning approaches. The framework of learning with positive and unlabeled examples (LPU) is shown to be especially appropriate for mining functionally related genes.
  arXiv  Detail & Related papers  (2020-11-05T20:34:09Z)
• Generalized Matrix Factorization: efficient algorithms for fitting generalized linear latent variable models to large data arrays [62.997667081978825]
  Generalized Linear Latent Variable models (GLLVMs) generalize such factor models to non-Gaussian responses. Current algorithms for estimating model parameters in GLLVMs require intensive computation and do not scale to large datasets. We propose a new approach for fitting GLLVMs to high-dimensional datasets, based on approximating the model using penalized quasi-likelihood.
  arXiv  Detail & Related papers  (2020-10-06T04:28:19Z)
• 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 generalised OMP algorithm for feature selection with application to gene expression data [1.969028842568933]
  To apply to molecular data, feature selection algorithms need to be scalable to tens of thousands of available features. We propose gOMP, a highly-scalable generalisation of the Orthogonal Matching Pursuit feature selection algorithm.
  arXiv  Detail & Related papers  (2020-04-01T08:33:02Z)

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.