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Classification based upon gene expression data: bias and precision of error rates
2007
Bioinformatics
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Downward bias of 3-5% was found in an existing study of classification based on gene expression data and may be endemic in similar studies. ...
Motivation: Gene expression data offer a large number of potentially useful predictors for the classification of tissue samples into classes, such as diseased and non-diseased. ...
ACKNOWLEDGEMENTS The authors appreciate discussions with Geoff McLachlan, David Duffy, Ross McVinish, Clair Alston and Georgia Chenevix-Trench and the helpful comments of two anonymous reviewers. ...
doi:10.1093/bioinformatics/btm117
pmid:17392326
fatcat:surozg6qnbbqxjlb6wqclci3oi
BagBoosting for tumor classification with gene expression data
2004
Bioinformatics
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gene expression data. ...
Motivation: Microarray experiments are expected to contribute significantly to the progress in cancer treatment by enabling a precise and early diagnosis. ...
METHODS
Class prediction with gene expression data The main goal in class prediction with gene expression data is a precise and early diagnosis of cancerous malignancies that allows to tailor the patients ...
doi:10.1093/bioinformatics/bth447
pmid:15466910
fatcat:f5qjnqpfs5d6dgum4qdjkr2gsu
SUPERVISED MACHINE LEARNING MODEL FOR MICRORNA EXPRESSION DATA IN CANCER
2017
Jurnal Ilmu Komputer dan Informasi
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The cancer cell gene expression data in general has a very large feature and requires analysis to find out which genes are strongly influencing the specific disease for diagnosis and drug discovery. ...
In this paper several methods of supervised learning (decisien tree, naïve bayes, neural network, and deep learning) are used to classify cancer cells based on the expression of the microRNA gene to obtain ...
Classification techniques of cancer cells based on gene expression data using machine learning methods have been developed rapidly in the analysis and diagnosis of cancer [7] . ...
doi:10.21609/jiki.v10i2.481
fatcat:oj4rd3dawzgfneqtyzwlufbflm
An Empirical Study of Univariate and Genetic Algorithm-Based Feature Selection in Binary Classification with Microarray Data
2006
Cancer Informatics
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clinical applications of gene expression microarrays. ...
An Empirical Study of Feature Selection in Binary Classification with DNA Microarray Data by Michael Louis Lecocke Motivation: Binary classification is a common problem in many types of research including ...
for performing binary classification based on gene expression data from a given dataset. ...
doi:10.1177/117693510600200016
fatcat:fbivbpap7rhltkdc3eavqjnile
Evolutionary Computational Algorithm by Blending of PPCA and EP-Enhanced Supervised Classifier for Microarray Gene Expression Data
2018
IAES International Journal of Artificial Intelligence (IJ-AI)
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The well-trained ANN has the capacity of classifying the gene expression data to the associated classes. ...
In this paper, a classification technique is proposed that classifies the microarray gene expression data well. ...
Classification of Microarray Gene Expression using the Enhanced Classifier In the classification of microarray gene expression data, two phases of operation are performed that include training phase and ...
doi:10.11591/ijai.v7.i2.pp95-104
fatcat:etcvgc4rybg3pnrkdbfvnndw7e
Cancer Subtype Discovery Using Prognosis-Enhanced Neural Network Classifier in Multigenomic Data
2018
Technology in Cancer Research and Treatment
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We analyzed the heterogeneity and identified the subtypes of glioblastoma multiforme, an aggressive adult brain tumor, from 215 samples with microRNA expression (12 042 genes). ...
The samples were classified into 4 different classes such as mesenchymal, classical, proneural, and neural subtypes owing to mutations and gene expression. ...
weights and bias error in the modified error function. ...
doi:10.1177/1533033818790509
pmid:30092720
pmcid:PMC6088521
fatcat:cjptqm3gdrgjzc754v3idtpyba
The properties of high-dimensional data spaces: implications for exploring gene and protein expression data
2008
Nature Reviews. Cancer
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From the perspective of translational science, this Review discusses the properties of high-dimensional data spaces that arise in genomic and proteomic studies and the challenges they can pose for data ...
The properties of high dimensionality are often poorly understood or overlooked in data modelling and analysis. ...
Many of the engineering and computer science works published in 'proceedings' represent peer-reviewed publications. ...
doi:10.1038/nrc2294
pmid:18097463
pmcid:PMC2238676
fatcat:72e62lj4vjcwpfrewtrh7b2ptq
Cancer classification using gene expression data
2003
Information Systems
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With this abundance of gene expression data, researchers have started to explore the possibilities of cancer classification using gene expression data. ...
Cancer classification using gene expression data is known to contain the keys for addressing the fundamental problems relating to cancer diagnosis and drug discovery. ...
This is not adequate in the case of cancer classification. In classifying normal vs. cancerous data, the errors can be grouped into misclassification rate and non-classification rate. ...
doi:10.1016/s0306-4379(02)00072-8
fatcat:telrkleownbq5epdcderjevhwy
A comprehensive survey on computational learning methods for analysis of gene expression data in genomics
[article]
2022
arXiv
pre-print
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2022 pre-print arXiv:2202.02958v3 fatcat:sgy65tyjhvh5tpb26r3mmchpoyOther Versions
High-throughput gene expression analysis methods such as microarray technology and RNA sequencing produce enormous amounts of data. ...
We have described the process of generation of a microarray gene expression data along with advantages and limitations of the above-mentioned techniques. ...
It lowers the bias and increases the variance of all genes. ...
arXiv:2202.02958v4
fatcat:uipvs7ribzdondwraf64n5mzf4
A comprehensive evaluation of machine learning techniques for cancer class prediction based on microarray data
2015
International Journal of Bioinformatics Research and Applications
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2013 pre-print arXiv:1307.7050v1 fatcat:yvmpcc3fabatdd2cym6o2pezgi
Machine learning is effective when number of attributes (genes) are larger than the number of samples which is rarely possible with gene expression data. ...
The genomic level changes can be detected in gene expression data and those changes may serve as standard model for any random cancer data for class prediction. ...
Raza acknowledges the funding from University Grants Commission, Govt. of India through research grant 42-1019/2013(SR). ...
doi:10.1504/ijbra.2015.071940
pmid:26558300
fatcat:r5ps5bualngcxfof4lixn5kqh4
Probabilistic classifiers with high-dimensional data
2010
Biostatistics
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Based on simulation studies and analysis of gene expression microarray data, we found that proper probabilistic classification is more difficult than deterministic classification. ...
We also present a cross-validation method for evaluating the calibration and refinement of any probabilistic classifier on any data set. ...
ACKNOWLEDGMENT Conflict of Interest: None declared. ...
doi:10.1093/biostatistics/kxq069
pmid:21087946
pmcid:PMC3138069
fatcat:ukokk2ugu5affkfsynzmvuq7na
Enhanced Cancer Subtyping via Pan-Transcriptomics Data Fusion, Monte-Carlo Consensus Clustering, and Auto Classifier Creation
[article]
2019
bioRxiv
pre-print
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However, the reproducibility of these subtyping based studies is poor. There are multiple reports which have conflicting subtype and gene-survival time relationship results. ...
This problem arises from the routine analysis of small cohorts (< 100 individuals) and use of biased traditional consensus clustering techniques. ...
ACKNOWLEDGMENTS The authors would like to thank everyone at and involved with Cambridge Cancer Genomics whom made this project possible and offered insightful constructive feedback throughout this study ...
doi:10.1101/2019.12.16.870188
fatcat:fnvd3almbzc6tp4fjljpne6tdm
Improved shrunken centroid classifiers for high-dimensional class-imbalanced data
2013
BMC Bioinformatics
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The NSC methods base their classification rules on shrunken centroids; in practice the amount of shrinkage is estimated minimizing the overall cross-validated (CV) error rate. ...
PAM, a nearest shrunken centroid method (NSC), is a popular classification method for high-dimensional data. ALP and AHP are NSC algorithms that were proposed to improve upon PAM. ...
Acknowledgements The high-performance computation facilities were kindly provided by Bioinformatics and Genomics unit at Department of Molecular Biotechnology and Heath Sciences, University of Torino, ...
doi:10.1186/1471-2105-14-64
pmid:23433084
pmcid:PMC3687811
fatcat:co5erqwmkvhufop5uuabt6dkhe
Entropy-based gene ranking without selection bias for the predictive classification of microarray data
2003
BMC Bioinformatics
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Thus, a process for gene selection and error estimation is made practical, ensuring control of the selection bias, and providing additional diagnostic indicators of gene importance. ...
The method supports a practical modeling scheme designed to avoid the construction of classification rules based on the selection of too small gene subsets (an effect known as the selection bias, in which ...
BJ is supported by the FUPAT post-graduate project 'Algorithms and software environments for microarray gene expression experiments'. We thank T. Poggio, G. Anzellotti and B. ...
doi:10.1186/1471-2105-4-54
pmid:14604446
pmcid:PMC293475
fatcat:6cvsarfcfjhdri5o22xn3h5zs4
A fuzzy based feature selection from independent component subspace for machine learning classification of microarray data
2016
Genomics Data
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Feature (gene) selection and classification of microarray data are the two most interesting machine learning challenges. ...
The curve between the average error rate and number of genes with each dataset represents the selection of required number of genes for the highest accuracy with our proposed method for both the classifiers ...
Figs. 9 and 10 show the graph of the average error rate of SVM and NB classifiers respectively, for the five datasets with different gene selection methods. ...
doi:10.1016/j.gdata.2016.02.012
pmid:27081632
pmcid:PMC4818349
fatcat:wfvdu4zkwbddzltbmfsrskzfom
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