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Comparative Study of Microarray Based Disease Prediction - A Survey

T. Sneka, K. Palanivel
2019 International Journal of Scientific Research in Computer Science Engineering and Information Technology  
This survey shows that how semi-supervised approach evolves and outperforms the existing algorithms.  ...  This survey observes some various techniques of classification, clustering of genes and feature selection methods such as supervised, unsupervised and semi-supervised methods.  ...  • To discuss various clustering, feature selection and classification techniques adopted for microarray based disease prediction. • To determine how supervised, unsupervised and semi-supervised methods  ... 
doi:10.32628/cseit195435 fatcat:jpbopg3mmvczpm4q3bqcdk55pq

Basic microarray analysis: grouping and feature reduction

Soumya Raychaudhuri, Patrick D. Sutphin, Jeffrey T. Chang, Russ B. Altman
2001 Trends in Biotechnology  
Acknowledgements This work is supported by NIH LM06244 and GM61374, as well as NSF DBI-9600637 and a grant from the Burroughs-Wellcome Foundation.  ...  Supervised and unsupervised methods Most techniques for analyzing microarray data can be thought of as either 'supervised' or 'unsupervised'.  ...  Not surprisingly, unsupervised methods are used for exploratory tasks, whereas supervised approaches are used to address more direct questions (e.g. can the DNA microarray technologies are useful for addressing  ... 
doi:10.1016/s0167-7799(01)01599-2 pmid:11301132 fatcat:zk6ys2alwfgcvmvcpeu7n7o3ka

Analyzing array data using supervised methods

Markus Ringnér, Carsten Peterson, Javed Khan
2002 Pharmacogenomics (London)  
and unsupervised approaches.  ...  Approaches to the computational analysis of gene expression data can be separated into two groups: unsupervised and supervised.  ...  TECHNOLOGY REPORT and to identify the most important genes. 27  ... 
doi:10.1517/14622416.3.3.403 pmid:12052147 fatcat:4uuyr7mhpzfsbiymwleoz4homi

Machine Learning Based Computational Gene Selection Models: A Survey, Performance Evaluation, Open Issues, and Future Research Directions

Nivedhitha Mahendran, P. M. Durai Raj Vincent, Kathiravan Srinivasan, Chuan-Yu Chang
2020 Frontiers in Genetics  
The study categorizes various feature selection algorithms under Supervised, Unsupervised, and Semi-supervised learning.  ...  This paper does an extensive review of the various works done on Machine Learning-based gene selection in recent years, along with its performance analysis.  ...  Machine Learning is commonly categorized as Supervised, Unsupervised, and Semi-supervised or Semiunsupervised learning.  ... 
doi:10.3389/fgene.2020.603808 pmid:33362861 pmcid:PMC7758324 fatcat:jhyfsc72tngwhnrl4vxg3k4tii

Cross-Platform Normalization Enables Machine Learning Model Training On Microarray And RNA-Seq Data Simultaneously [article]

Jaclyn N Taroni, Casey S Greene
2017 bioRxiv   pre-print
We performed supervised and unsupervised machine learning evaluations, as well as differential expression analyses, to assess which normalization methods are best suited for combining microarray and RNA-seq  ...  Results: We find that quantile and Training Distribution Matching normalization allow for supervised and unsupervised model training on microarray and RNA-seq data simultaneously.  ...  Acknowledgements We thank Gregory Way for helpful code review and Amy Campbell for proof-reading of the manuscript.  ... 
doi:10.1101/118349 fatcat:mxojgu6hura7fpohrzxpibbltu

Gene expression analysis

Felicia I. Carvalho, Christopher Johns, Marc E. Gillespie
2012 Biochemistry and Molecular Biology Education  
The requirements for methods to handle such amounts of data have arisen.  ...  In the past decade rapid advances of microarray technologies have made it possible to monitor the expression profiles of thousands of genes under various experimental conditions.  ...  The first variant performs unsupervised learning and can be used for data visualization, clustering, and vector quantization.  ... 
doi:10.1002/bmb.20588 pmid:22615226 fatcat:hdb3jwf4gbebhkxj4hgt7fl4qu

A glance at DNA microarray technology and applications

Amir Ata Saei, Yadollah Omidi
2011 BioImpacts  
The main goal of this article is to provide a brief review on different steps of microarray data handling and mining for novices and at last to introduce different PC and/or web-based softwares that can  ...  be used in preprocessing and/or data mining of microarray data.  ...  Acknowledgement Authors are grateful to the Ministry of Health, Care and Medical Education for the financial support.  ... 
doi:10.5681/bi.2011.011 pmid:23678411 pmcid:PMC3648957 fatcat:4ywgkf6i2zaxtg7z6lvzvhfjn4

Feature Selection for Genomic Signal Processing: Unsupervised, Supervised, and Self-Supervised Scenarios

S. Y. Kung, Yuhui Luo, Man-Wai Mak
2008 Journal of Signal Processing Systems  
In terms of training data types, they are divided into the unsupervised and supervised categories. In terms of selection methods, they fall into filter and wrapper categories.  ...  Based on several subcellular localization experiments, and microarray time course experiments, the VIA-SVM algorithm when combined with some filtertype metrics appears to deliver a substantial dimension  ...  For unsupervised learning, the variance can be used as a measurement of relevance. For supervised learning, a feature's SNR across classes can be used as a score for its relevance.  ... 
doi:10.1007/s11265-008-0273-8 fatcat:5vfi7y7eqzgd7lms4p75knnaxy

Unsupervised pattern recognition: An introduction to the whys and wherefores of clustering microarray data

P. C. Boutros, A. B. Okey
2005 Briefings in Bioinformatics  
Clustering has become an integral part of microarray data analysis and interpretation.  ...  The algorithmic basis of clustering -the application of unsupervised machine-learning techniques to identify the patterns inherent in a data set -is well established.  ...  Acknowledgments The authors gratefully thank Dr Albert Wong and Ms Mawahib Semeralul for permission to use their  ... 
doi:10.1093/bib/6.4.331 pmid:16420732 fatcat:iresoac4bzcqfjy4v5gmuwphii

Microarray learning with ABC

D. Amaratunga, J. Cabrera, V. Kovtun
2007 Biostatistics  
We propose, as an alternative, a novel method for unsupervised classification of DNA microarray data.  ...  The method produces a measure of dissimilarity between each pair of samples that can be used in conjunction with (a) a method like Ward's procedure to generate a cluster analysis and (b) multidimensional  ...  DISCUSSION We have introduced an ensemble method based on weighted resampling for unsupervised learning.  ... 
doi:10.1093/biostatistics/kxm017 pmid:17573363 fatcat:vw2qon7pbbg6pmbyz3abtvsl3y

A Review on Recent Progress in Machine Learning and Deep Learning Methods for Cancer Classification on Gene Expression Data

Aina Umairah Mazlan, Noor Azida Sahabudin, Muhammad Akmal Remli, Nor Syahidatul Nadiah Ismail, Mohd Saberi Mohamad, Hui Wen Nies, Nor Bakiah Abd Warif
2021 Processes  
The development of many machine learning methods for insight analysis in cancer classification has brought a lot of improvement in healthcare.  ...  This paper provides a recent review on recent progress in ML and deep learning (DL) for cancer classification, which has received increasing attention in bioinformatics and computational biology.  ...  Hybrid of Supervised and Unsupervised Learning (UL) Two major UL methods are clustering and principal component analysis (PCA) [22] .  ... 
doi:10.3390/pr9081466 fatcat:mehvzadmn5dafauq4lqifb2vky

Review on Data Mining Techniques in Bioinformatics for Extracting Enzymes Names from Literature

Dr. Gulhane V. S., Prof. Gautam L. K., Sahu V. G.
2017 IJARCCE  
It also highlights some of the current challenges and opportunities of data mining in bioinformatics.  ...  This paper highlights some of the basic concepts of bioinformatics and data mining. The major research areas of bioinformatics are highlighted.  ...  Learning from data falls into two categories: directed ("supervised") and undirected ("unsupervised") learning.  ... 
doi:10.17148/ijarcce.2017.6367 fatcat:kzbfskxbtjhgdcsa7x6ti7jzya

CompareSVM: supervised, Support Vector Machine (SVM) inference of gene regularity networks

Zeeshan Gillani, Muhammad Sajid Hamid Akash, MD Matiur Rahaman, Ming Chen
2014 BMC Bioinformatics  
There is a need for comprehensive analysis on prediction accuracy of supervised method SVM using different kernels on different biological experimental conditions and network size.  ...  There are many methods that have been developed to address this challenge ranging from supervised to unsupervised methods. Most promising methods are based on support vector machine (SVM).  ...  Acknowledgments This project was supported by the grant of Zhejiang University Scholarship program for foreign students, and partly supported by the National Natural Sciences Foundation of China (No. 31371328  ... 
doi:10.1186/s12859-014-0395-x pmid:25433465 pmcid:PMC4260380 fatcat:dqsvssapurey5orvflghlmexdm

Integrative data mining: the new direction in bioinformatics

P. Bertone, M. Gerstein
2001 IEEE Engineering in Medicine and Biology Magazine  
Machine Learning Approaches to Genomic Data Analysis Unsupervised Learning and Clustering A general problem in data analysis is how to structure information into meaningful taxonomies or categories.  ...  Supervised Learning and Classification Analysis of large data sets that contain diverse information often involves the explicit application of supervised learning.  ...  , gene expression analysis, and Bayesian systems for data mining.  ... 
doi:10.1109/51.940042 pmid:11494767 fatcat:kh4u7xxslve4ddmnkwurjmayp4

DNA microarrays: from profiles to biology

Bertrand R Jordan
2007 Pharmacogenomics (London)  
In a Follow-up to the very successful Euroscicon 2005 meeting "DNA micro arrays: from target collections to profiles", our 2007 meeting will focus more on downstream applications and will run by the title  ...  arrays: from profiles to biology" The focus of this meeting will not as much be on the in-house preparation of micro-arrays, but rather on the subsequent data processing steps, validation strategies, and  ...  Colin Campbell, Engineering Mathematics, University of Bristol, UK We start with an outline of novel probabilistic methods for unsupervised, semi-supervised and supervised learning and their application  ... 
doi:10.2217/14622416.8.7.701 pmid:18240902 fatcat:2zvoj2uy3fadfbesmnnqjo5fce
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