Bayesian networks classifiers for gene-expression data

Campos, Luis M. de; Cano, Andrés; Castellano, Javier G.; Moral, Serafı́n

doi:10.1109/isda.2011.6121822

Cited by 13 publications

(6 citation statements)

References 49 publications

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“…Figure 2 shows the steps involved in filter based gene selection method. Various kind of filter gene selection techniques have been used in ranking and selecting significant genes such as correlation based filter (CFS) [6], Bayesian scoring functions, signal-to-noise ratio [7], Euclidean distance, Information Theory based scoring, fold change analysis, t-test, information gain, Whitney rank sum, Gini index and many others [10,11].These techniques mainly rely on general characteristics of the training data to select some features without involving any learning algorithm. Therefore, the results of filter model will not affecting any classification algorithm [11].…”

Section: Filter Based Gene Selection Methodsmentioning

confidence: 99%

A Comparative Study on Gene Selection Methods for Tissues Classification on Large Scale Gene Expression Data

Ahmad

2016

Jurnal Teknologi

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Deoxyribonucleic acid (DNA) microarray technology is the recent invention that provided colossal opportunities to measure a large scale of gene expressions simultaneously. However, interpreting large scale of gene expression data remain a challenging issue due to their innate nature of “high dimensional low sample size”. Microarray data mainly involved thousands of genes, n in a very small size sample, p which complicates the data analysis process. For such a reason, feature selection methods also known as gene selection methods have become apparently need to select significant genes that present the maximum discriminative power between cancerous and normal tissues. Feature selection methods can be structured into three basic factions; a) filter methods; b) wrapper methods and c) embedded methods. Among these methods, filter gene selection methods provide easy way to calculate the informative genes and can simplify reduce the large scale microarray datasets. Although filter based gene selection techniques have been commonly used in analyzing microarray dataset, these techniques have been tested separately in different studies. Therefore, this study aims to investigate and compare the effectiveness of these four popular filter gene selection methods namely Signal-to-Noise ratio (SNR), Fisher Criterion (FC), Information Gain (IG) and t-Test in selecting informative genes that can distinguish cancer and normal tissues. In this experiment, common classifiers, Support Vector Machine (SVM) is used to train the selected genes. These gene selection methods are tested on three large scales of gene expression datasets, namely breast cancer dataset, colon dataset, and lung dataset. This study has discovered that IG and SNR are more suitable to be used with SVM. Furthermore, this study has shown SVM performance remained moderately unaffected unless a very small size of genes was selected.

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Section: Filter Based Gene Selection Methodsmentioning

confidence: 99%

A Comparative Study on Gene Selection Methods for Tissues Classification on Large Scale Gene Expression Data

Ahmad

2016

Jurnal Teknologi

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“…Applications of Bayesian networks range from predicting a disease/treatment for a patient [15] to performing profit analysis for a company [16] and to creating genetic maps [17]. The most important properties of the Bayesian network are its ability to provide real-time solutions and its ability to handle missing data under uncertainty [18].…”

Section: Literature Review: Applications Of Bayesian Belief Network I...mentioning

confidence: 99%

A Bayesian Approach towards Modelling the Interrelationships of Pavement Deterioration Factors

Philip

Jassmi

2022

Buildings

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In this study, Bayesian Belief Networks (BBN) are proposed to model the relationships between factors contributing to pavement deterioration, where their values are probabilistically estimated based on their interdependencies. Such probabilistic inferences are deemed to provide a reasonable alternative over costly data collection campaigns and assist in road condition diagnoses and assessment efforts in cases where data are only partially available. The BBN models examined in this study are based on a vast database of pavement deterioration factors including road distress data, namely cracking, deflection, the International Roughness Index (IRI) and rutting, from major road sections in the United Arab Emirates (UAE) along with the corresponding traffic and climatic factors. The dataset for the analysis consisted of 3272 road sections, each of 10 m length. The test results showed that the most critical parameter representing the whole process of road deterioration is the IRI with the highest nodal force. Additionally, IRI is strongly correlated with rutting and deflection, with mutual information of 0.147 and 0.143, respectively. Furthermore, a Bayesian network structure with a contingency table fit of over 90% illustrates how the road distress parameters change in the presence of external factors, such as traffic and climatic conditions.

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“…The class-conditional probability density f (.| H ) for each attribute and the prior P ( H ) can be obtained from the learning process. For the estimation of f (.| H ) the nonparametric kernel density estimation method is used [13] , [40] , [43] . As a result, the general Bayesian classifier given by Eq.…”

Section: Proposed Approachmentioning

confidence: 99%

A fuzzy based feature selection from independent component subspace for machine learning classification of microarray data

2016

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Feature (gene) selection and classification of microarray data are the two most interesting machine learning challenges. In the present work two existing feature selection/extraction algorithms, namely independent component analysis (ICA) and fuzzy backward feature elimination (FBFE) are used which is a new combination of selection/extraction. The main objective of this paper is to select the independent components of the DNA microarray data using FBFE to improve the performance of support vector machine (SVM) and Naïve Bayes (NB) classifier, while making the computational expenses affordable. To show the validity of the proposed method, it is applied to reduce the number of genes for five DNA microarray datasets namely; colon cancer, acute leukemia, prostate cancer, lung cancer II, and high-grade glioma. Now these datasets are then classified using SVM and NB classifiers. Experimental results on these five microarray datasets demonstrate that gene selected by proposed approach, effectively improve the performance of SVM and NB classifiers in terms of classification accuracy. We compare our proposed method with principal component analysis (PCA) as a standard extraction algorithm and find that the proposed method can obtain better classification accuracy, using SVM and NB classifiers with a smaller number of selected genes than the PCA. 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. ROC shows best subset of genes for both the classifier of different datasets with propose method.

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Bayesian networks classifiers for gene-expression data

Cited by 13 publications

References 49 publications

A Comparative Study on Gene Selection Methods for Tissues Classification on Large Scale Gene Expression Data

A Comparative Study on Gene Selection Methods for Tissues Classification on Large Scale Gene Expression Data

A Bayesian Approach towards Modelling the Interrelationships of Pavement Deterioration Factors

A fuzzy based feature selection from independent component subspace for machine learning classification of microarray data

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