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17 papers · ranked by Valyu relevance

01
PPubMed7 Nov 2022
A comprehensive survey on computational learning methods for analysis of gene expression data Bhandari et al. 10.3389/fmolb.2022.907150
Nikita Bhandari, Rahee Walambe, Ketan Kotecha, Satyajeet P. Khare
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…
88relevance
02
PPubMed1 Oct 2013
Fuzzy support vector machine: an efficient rule-based classification technique for microarrays
Mohsen Hajiloo, Hamid R Rabiee, Mahdi Anooshahpour
Background The abundance of gene expression microarray data has led to the development of machine learning algorithms applicable for tackling disease diagnosis, disease prognosis, and treatment selection problems. However, these algorithms often produce classifiers with weaknesses in terms of accuracy, robustness, and…
87relevance
03
PPubMed11 Mar 2022
Comparative Study of Classification Algorithms for Various DNA Microarray Data
Jingeun Kim, Yourim Yoon, Hye-Jin Park, Yong-Hyuk Kim + 1 more
Microarrays are applications of electrical engineering and technology in biology that allow simultaneous measurement of expression of numerous genes, and they can be used to analyze specific diseases. This study undertakes classification analyses of various microarrays to compare the performances of classification…
87relevance
04
PPubMed30 Mar 2015
Hierarchical Gene Selection and Genetic Fuzzy System for Cancer Microarray Data Classification Gene Selection Genetic Fuzzy System Cancer Microarray
Thanh Nguyen, Abbas Khosravi, Douglas Creighton, Saeid Nahavandi + 1 more
'Enrique Hernandez-Lemus'] This paper introduces a novel approach to gene selection based on a substantial modification of analytic hierarchy process (AHP). The modified AHP systematically integrates outcomes of individual filter methods to select the most informative genes for microarray classification. Five…
85relevance
05
PPubMed6 Aug 2014
A Discrete Wavelet Based Feature Extraction and Hybrid Classification Technique for Microarray Data Analysis
Jaison Bennet, Chilambuchelvan Arul Ganaprakasam, Kannan Arputharaj
Cancer classification by doctors and radiologists was based on morphological and clinical features and had limited diagnostic ability in olden days. The recent arrival of DNA microarray technology has led to the concurrent monitoring of thousands of gene expressions in a single chip which stimulates the progress in…
85relevance
06
PPubMed27 Sep 2004
A combinational feature selection and ensemble neural network method for classification of gene expression data
Bing Liu, Qinghua Cui, Tianzi Jiang, Songde Ma
Background Microarray experiments are becoming a powerful tool for clinical diagnosis, as they have the potential to discover gene expression patterns that are characteristic for a particular disease. To date, this problem has received most attention in the context of cancer research, especially in tumor…
85relevance
07
PPubMed14 Jun 2006
Kernel-based distance metric learning for microarray data classification
Huilin Xiong, Xue-wen Chen
Background The most fundamental task using gene expression data in clinical oncology is to classify tissue samples according to their gene expression levels. Compared with traditional pattern classifications, gene expression-based data classification is typically characterized by high dimensionality and small sample…
84relevance
08
PPubMed28 Oct 2003
Diagnostic and prognostic prediction using gene expression profiles in high-dimensional microarray data Class prediction using microarray data
R Simon
DNA microarrays are a potentially powerful technology for improving diagnostic classification, treatment selection and therapeutics development. There are, however, many potential pitfalls in the use of microarrays that result in false leads and erroneous conclusions. This paper provides a review of the key features to…
84relevance
09
PPubMed11 Oct 2013
Identifying Genes Relevant to Specific Biological Conditions in Time Course Microarray Experiments Relevant Genes in Microarrays Experiments
Nitesh Kumar Singh, Dirk Repsilber, Volkmar Liebscher, Leila Taher + 2 more
'Georg Fuellen' 'Andrew R. Dalby'] Microarrays have been useful in understanding various biological processes by allowing the simultaneous study of the expression of thousands of genes. However, the analysis of microarray data is a challenging task. One of the key problems in microarray analysis is the classification…
83relevance
10
PPubMed25 Apr 2014
Evaluation of Gene Expression Classification Studies: Factors Associated with Classification Performance Evaluation of Gene Expression Classification Studies
Putri W. Novianti, Kit C. B. Roes, Marinus J. C. Eijkemans, Jörg D. Hoheisel
'Jörg D. Hoheisel'] Classification methods used in microarray studies for gene expression are diverse in the way they deal with the underlying complexity of the data, as well as in the technique used to build the classification model. The MAQC II study on cancer classification problems has found that performance was…
80relevance
11
PPubMed16 Jun 2007
Selecting dissimilar genes for multi-class classification, an application in cancer subtyping
Zhipeng Cai, Randy Goebel, Mohammad R Salavatipour, Guohui Lin
Background Gene expression microarray is a powerful technology for genetic profiling diseases and their associated treatments. Such a process involves a key step of biomarker identification, which are expected to be closely related to the disease. A most important task of these identified genes is that they can be used…
80relevance
12
PPubMed20 Feb 2014
Gene selection for cancer identification: a decision tree model empowered by particle swarm optimization algorithm
Kun-Huang Chen, Kung-Jeng Wang, Min-Lung Tsai, Kung-Min Wang + 6 more
'Angelia Melani Adrian' 'Wei-Chung Cheng' 'Tzu-Sen Yang' 'Nai-Chia Teng' 'Kuo-Pin Tan' 'Ku-Shang Chang'] Background In the application of microarray data, how to select a small number of informative genes from thousands of genes that may contribute to the occurrence of cancers is an important issue. Many researchers…
79relevance
13
PPubMed1 Oct 2017
Classification and Biomarker Genes Selection for Cancer Gene Expression Data Using Random Forest
Malihe Ram, Ali Najafi, Mohammad Taghi Shakeri
Background & objective: Microarray and next generation sequencing (NGS) data are the important sources to find helpful molecular patterns. Also, the great number of gene expression data increases the challenge of how to identify the biomarkers associated with cancer. The random forest (RF) is used to effectively…
77relevance
14
PPubMed17 Jul 2024
Metaheuristic integrated machine learning classification of colon cancer using STFT LASSO and EHO feature extraction from microarray gene expressions
Ajin R. Nair, Harikumar Rajaguru, M. S. Karthika, C. Keerthivasan
The microarray gene expression data poses a tremendous challenge due to their curse of dimensionality problem. The sheer volume of features far surpasses available samples, leading to overfitting and reduced classification accuracy. Thus the dimensionality of microarray gene expression data must be reduced with…
77relevance
15
PPubMed18 Jan 2010
SFSSClass: an integrated approach for miRNA based tumor classification
Ramkrishna Mitra, Sanghamitra Bandyopadhyay, Ujjwal Maulik, Michael Q Zhang
'Michael Q Zhang'] Background MicroRNA (miRNA) expression profiling data has recently been found to be particularly important in cancer research and can be used as a diagnostic and prognostic tool. Current approaches of tumor classification using miRNA expression data do not integrate the experimental knowledge…
75relevance
16
PPubMed6 Aug 2023
Evaluation and Exploration of Machine Learning and Convolutional Neural Network Classifiers in Detection of Lung Cancer from Microarray Gene—A Paradigm Shift
Karthika M S, Harikumar Rajaguru, Ajin R. Nair, Antonio Brunetti
Microarray gene expression-based detection and classification of medical conditions have been prominent in research studies over the past few decades. However, extracting relevant data from the high-volume microarray gene expression with inherent nonlinearity and inseparable noise components raises significant…
75relevance
17
PPubMed25 Jan 2007
A perspective on microarrays: current applications, pitfalls, and potential uses
Pratik Jaluria, Konstantinos Konstantopoulos, Michael Betenbaugh, Joseph Shiloach
'Joseph Shiloach'] With advances in robotics, computational capabilities, and the fabrication of high quality glass slides coinciding with increased genomic information being available on public databases, microarray technology is increasingly being used in laboratories around the world. In fact, fields as varied as…
74relevance

Results for microarray classification

A comprehensive survey on computational learning methods for analysis of gene expression data Bhandari et al. 10.3389/fmolb.2022.907150

Fuzzy support vector machine: an efficient rule-based classification technique for microarrays

Comparative Study of Classification Algorithms for Various DNA Microarray Data

Hierarchical Gene Selection and Genetic Fuzzy System for Cancer Microarray Data Classification Gene Selection Genetic Fuzzy System Cancer Microarray

A Discrete Wavelet Based Feature Extraction and Hybrid Classification Technique for Microarray Data Analysis

A combinational feature selection and ensemble neural network method for classification of gene expression data

Kernel-based distance metric learning for microarray data classification

Diagnostic and prognostic prediction using gene expression profiles in high-dimensional microarray data Class prediction using microarray data

Identifying Genes Relevant to Specific Biological Conditions in Time Course Microarray Experiments Relevant Genes in Microarrays Experiments

Evaluation of Gene Expression Classification Studies: Factors Associated with Classification Performance Evaluation of Gene Expression Classification Studies

Selecting dissimilar genes for multi-class classification, an application in cancer subtyping

Gene selection for cancer identification: a decision tree model empowered by particle swarm optimization algorithm

Classification and Biomarker Genes Selection for Cancer Gene Expression Data Using Random Forest

Metaheuristic integrated machine learning classification of colon cancer using STFT LASSO and EHO feature extraction from microarray gene expressions

SFSSClass: an integrated approach for miRNA based tumor classification

Evaluation and Exploration of Machine Learning and Convolutional Neural Network Classifiers in Detection of Lung Cancer from Microarray Gene—A Paradigm Shift

A perspective on microarrays: current applications, pitfalls, and potential uses

A comprehensive survey on computational learning methods for analysis of gene expression data Bhandari et al. 10.3389/fmolb.2022.907150

Fuzzy support vector machine: an efficient rule-based classification technique for microarrays

Comparative Study of Classification Algorithms for Various DNA Microarray Data

Hierarchical Gene Selection and Genetic Fuzzy System for Cancer Microarray Data Classification Gene Selection Genetic Fuzzy System Cancer Microarray

A Discrete Wavelet Based Feature Extraction and Hybrid Classification Technique for Microarray Data Analysis

A combinational feature selection and ensemble neural network method for classification of gene expression data

Kernel-based distance metric learning for microarray data classification

Diagnostic and prognostic prediction using gene expression profiles in high-dimensional microarray data Class prediction using microarray data

Identifying Genes Relevant to Specific Biological Conditions in Time Course Microarray Experiments Relevant Genes in Microarrays Experiments

Evaluation of Gene Expression Classification Studies: Factors Associated with Classification Performance Evaluation of Gene Expression Classification Studies

Selecting dissimilar genes for multi-class classification, an application in cancer subtyping

Gene selection for cancer identification: a decision tree model empowered by particle swarm optimization algorithm

Classification and Biomarker Genes Selection for Cancer Gene Expression Data Using Random Forest

Metaheuristic integrated machine learning classification of colon cancer using STFT LASSO and EHO feature extraction from microarray gene expressions

SFSSClass: an integrated approach for miRNA based tumor classification

Evaluation and Exploration of Machine Learning and Convolutional Neural Network Classifiers in Detection of Lung Cancer from Microarray Gene—A Paradigm Shift

A perspective on microarrays: current applications, pitfalls, and potential uses