Gene Subset Selection for Leukemia Classification Using Microarray Data

2019 ◽  
Vol 14 (4) ◽  
pp. 353-358 ◽  
Author(s):  
Mohamed Nisper Fathima Fajila
Keyword(s):  
Evolutionary Algorithm ◽  
Microarray Data ◽  
Gene Selection ◽  
Subset Selection ◽  
Research Field ◽  
Small Subset ◽  
Gene Subset ◽  
Highly Active ◽  
Selection Gene ◽  
Active Research

Background: Cancer subtype identification is an active research field which helps in the diagnosis of various cancers with proper treatments. Leukemia is one such cancer with various subtypes. High throughput technologies such as Deoxyribo Nucleic Acid (DNA) microarray are highly active in the field of cancer detection and classification alternatively. Objective: Yet, a precise analysis is important in microarray data applications as microarray experiments provide huge amount of data. Gene selection techniques promote microarray usage in the field of medicine. The objective of gene selection is to select a small subset of genes, which are the most informative in classification. associations while known disease-lncRNA associations are required only. Method: In this study, multi-objective evolutionary algorithm is used for gene subset selection in Leukemia classification. An initial redundant and irrelevant gene removal is followed by multiobjective evolutionary based gene subset selection. Gene subset selection highly influences the perfect classification. Thus, selecting the appropriate algorithm for subset selection is important. Results: The performance of the proposed method is compared against the standard genetic algorithm and evolutionary algorithm. Three Leukemia microarray datasets were used to evaluate the performance of the proposed method. Perfect classification was achieved for all the datasets only with few significant genes using the proposed approach. Conclusion: Thus, it is obvious that the proposed study perfectly classifies Leukemia with only few significant genes.</P>

Mining Hidden Association Rules from Real-Life Data

2010 ◽  
pp. 168-184
Author(s):  
Marco-Antonio Balderas Cepeda
Keyword(s):  
Association Rules ◽  
Real Life ◽  
Research Field ◽  
Good Reliability ◽  
Highly Active ◽  
Network Intrusion ◽  
Real Life Data ◽  
Alternative Approach ◽  
Rule Sets ◽  
Active Research

Association rule mining has been a highly active research field over the past decade. Extraction of frequency-related patterns has been applied to several domains. However, the way association rules are defined has limited people’s ability to obtain all the patterns of interest. In this chapter, the authors present an alternative approach that allows us to obtain new kinds of association rules that represent deviations from common behaviors. These new rules are called anomalous rules. To obtain such rules requires that we extract all the most frequent patterns together with certain extension patterns that may occur very infrequently. An approach that relies on anomalous rules has possible application in the areas of counterterrorism, fraud detection, pharmaceutical data analysis and network intrusion detection. They provide an adaption of measures of interest to our anomalous rule sets, and we propose an algorithm that can extract anomalous rules as well. Their experiments with benchmark and real-life datasets suggest that the set of anomalous rules is smaller than the set of association rules. Their work also provides evidence that our proposed approach can discover hidden patterns with good reliability.

scholarly journals RECORDING AND MODELLING OF MONUMENTS' INTERIOR SPACE USING RANGE AND OPTICAL SENSORS

2016 ◽  
Vol XLI-B5 ◽  
pp. 939-944
Author(s):  
Charalampos Georgiadis ◽  
Petros Patias ◽  
Vasilios Tsioukas
Keyword(s):  
Optical Sensors ◽  
Laser Scanning ◽  
Three Dimensional ◽  
Point Clouds ◽  
Research Field ◽  
Interior Space ◽  
Range Camera ◽  
Highly Active ◽  
Fixed Base ◽  
Active Research

Three dimensional modelling of artefacts and building interiors is a highly active research field in our days. Several techniques are being utilized to perform such a task, spanning from traditional surveying techniques and photogrammetry to structured light scanners, laser scanners and so on. New technological advancements in both hardware and software create new recording techniques, tools and approaches. In this paper we present a new recording and modelling approach based on the SwissRanger SR4000 range camera coupled with a Canon 400D dSLR camera. The hardware component of our approach consists of a fixed base, which encloses the range and SLR cameras. The two sensors are fully calibrated and registered to each other thus we were able to produce colorized point clouds acquired from the range camera. In this paper we present the initial design and calibration of the system along with experimental data regarding the accuracy of the proposed approach. We are also providing results regarding the modelling of interior spaces and artefacts accompanied with accuracy tests from other modelling approaches based on photogrammetry and laser scanning.

HYBRIDIZATION OF GENETIC AND QUANTUM ALGORITHM FOR GENE SELECTION AND CLASSIFICATION OF MICROARRAY DATA

2012 ◽  
Vol 23 (02) ◽  
pp. 431-444 ◽  
Author(s):  
ALLANI ABDERRAHIM ◽  
EL-GHAZALI TALBI ◽  
MELLOULI KHALED
Keyword(s):  
Microarray Data ◽  
Gene Selection ◽  
Quantum Algorithm ◽  
High Accuracy ◽  
High Dimensional ◽  
Support Vector ◽  
Small Subset ◽  
Microarray Experiments ◽  
Vector Machines

In this work, we hybridize the Genetic Quantum Algorithm with the Support Vector Machines classifier for gene selection and classification of high dimensional Microarray Data. We named our algorithm GQA SVM. Its purpose is to identify a small subset of genes that could be used to separate two classes of samples with high accuracy. A comparison of the approach with different methods of literature, in particular GA SVM and PSO SVM [2], was realized on six different datasets issued of microarray experiments dealing with cancer (leukemia, breast, colon, ovarian, prostate, and lung) and available on Web. The experiments clearified the very good performances of the method. The first contribution shows that the algorithm GQA SVM is able to find genes of interest and improve the classification on a meaningful way. The second important contribution consists in the actual discovery of new and challenging results on datasets used.

scholarly journals Human Attribute Recognition— A Comprehensive Survey

2020 ◽  
Vol 10 (16) ◽  
pp. 5608
Author(s):  
Ehsan Yaghoubi ◽  
Farhad Khezeli ◽  
Diana Borza ◽  
SV Aruna Kumar ◽  
João Neves ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Deep Learning ◽  
Algorithm Design ◽  
Research Field ◽  
Highly Active ◽  
Depth Analysis ◽  
Comprehensive Survey ◽  
Attribute Recognition ◽  
Active Research ◽  
Comprehensive Discussion

Human Attribute Recognition (HAR) is a highly active research field in computer vision and pattern recognition domains with various applications such as surveillance or fashion. Several approaches have been proposed to tackle the particular challenges in HAR. However, these approaches have dramatically changed over the last decade, mainly due to the improvements brought by deep learning solutions. To provide insights for future algorithm design and dataset collections, in this survey, (1) we provide an in-depth analysis of existing HAR techniques, concerning the advances proposed to address the HAR’s main challenges; (2) we provide a comprehensive discussion over the publicly available datasets for the development and evaluation of novel HAR approaches; (3) we outline the applications and typical evaluation metrics used in the HAR context.

scholarly journals Statistical Redundancy Testing for Improved Gene Selection in Cancer Classification Using Microarray Data

2007 ◽  
Vol 3 ◽  
pp. 117693510700300 ◽  
Author(s):  
Simin Hu ◽  
J. Sunil Rao
Keyword(s):  
Microarray Data ◽  
Gene Selection ◽  
Real Data ◽  
Cancer Classification ◽  
Simulation Studies ◽  
Selection Methods ◽  
Gene Subset ◽  
High Quality ◽  
Eigenvalue Ratio ◽  
Selection For

In gene selection for cancer classification using microarray data, we define an eigenvalue-ratio statistic to measure a gene's contribution to the joint discriminability when this gene is included into a set of genes. Based on this eigenvalue-ratio statistic, we define a novel hypothesis testing for gene statistical redundancy and propose two gene selection methods. Simulation studies illustrate the agreement between statistical redundancy testing and gene selection methods. Real data examples show the proposed gene selection methods can select a compact gene subset which can not only be used to build high quality cancer classifiers but also show biological relevance.

scholarly journals Simulation and Experimental Evaluation of the EKF Simultaneous Localization and Mapping Algorithm on the Wifibot Mobile Robot

2018 ◽  
Vol 8 (2) ◽  
pp. 91-101 ◽  
Author(s):  
Noura Ayadi ◽  
Nabil Derbel ◽  
Nicolas Morette ◽  
Cyril Novales ◽  
Gérard Poisson
Keyword(s):  
Mobile Robot ◽  
Autonomous Navigation ◽  
Large Scale ◽  
Simultaneous Localization And Mapping ◽  
Research Field ◽  
Small Scale ◽  
Mapping Algorithm ◽  
Highly Active ◽  
Localization And Mapping ◽  
Active Research

Abstract In recent years, autonomous navigation for mobile robots has been considered a highly active research field. Within this context, we are interested to apply the Simultaneous Localization And Mapping (SLAM) approach for a wheeled mobile robot. The Extended Kalman Filter has been chosen to perform the SLAM algorithm. In this work, we explicit all steps of the approach. Performances of the developed algorithm have been assessed through simulation in the case of a small scale map. Then, we present several experiments on a real robot that are proceeded in order to exploit a programmed SLAM unit and to generate the navigation map. Based on experimental results, simulation of the SLAM method in the case of a large scale map is then realized. Obtained results are exploited in order to evaluate and compare the algorithm’s consistency and robustness for both cases.

scholarly journals Gene Selection via a New Hybrid Ant Colony Optimization Algorithm for Cancer Classification in High-Dimensional Data

2019 ◽  
Vol 2019 ◽  
pp. 1-20 ◽  
Author(s):  
Ahmed Bir-Jmel ◽  
Sidi Mohamed Douiri ◽  
Souad Elbernoussi
Keyword(s):  
Data Analysis ◽  
Microarray Data ◽  
Gene Selection ◽  
Hybrid Approach ◽  
The Body ◽  
Cancer Classification ◽  
Microarray Data Analysis ◽  
High Dimensional ◽  
Small Subset ◽  
Classification Problems

The recent advance in the microarray data analysis makes it easy to simultaneously measure the expression levels of several thousand genes. These levels can be used to distinguish cancerous tissues from normal ones. In this work, we are interested in gene expression data dimension reduction for cancer classification, which is a common task in most microarray data analysis studies. This reduction has an essential role in enhancing the accuracy of the classification task and helping biologists accurately predict cancer in the body; this is carried out by selecting a small subset of relevant genes and eliminating the redundant or noisy genes. In this context, we propose a hybrid approach (MWIS-ACO-LS) for the gene selection problem, based on the combination of a new graph-based approach for gene selection (MWIS), in which we seek to minimize the redundancy between genes by considering the correlation between the latter and maximize gene-ranking (Fisher) scores, and a modified ACO coupled with a local search (LS) algorithm using the classifier 1NN for measuring the quality of the candidate subsets. In order to evaluate the proposed method, we tested MWIS-ACO-LS on ten well-replicated microarray datasets of high dimensions varying from 2308 to 12600 genes. The experimental results based on ten high-dimensional microarray classification problems demonstrated the effectiveness of our proposed method.

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