A Feature Selection Algorithm Based on Qualitative Mutual Information for Cancer Microarray Data

Nagpal, Arpita; Singh, Vijendra

doi:10.1016/j.procs.2018.05.195

Cited by 26 publications

(6 citation statements)

References 24 publications

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“…It achieves feature selection by removing features that have low mutual information with the class labels but high mutual information with other features and retaining those that have high mutual information with the class labels [ 39 ]. Mutual information was used because it is a commonly used method in genomics studies such as microarray studies [ 40 ] or gene expression studies [ 41 ], as those studies contain data of a high-dimensional feature space similar to this work. We used the mutual_info_classif implementation in scikit-learn [ 37 , 38 ].…”

Section: Methodsmentioning

confidence: 99%

The Relative Power of Structural Genomic Variation versus SNPs in Explaining the Quantitative Trait Growth in the Marine Teleost Chrysophrys auratus

Ruigrok

Xue

Catanach

et al. 2022

Genes

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Background: Genetic diversity provides the basic substrate for evolution. Genetic variation consists of changes ranging from single base pairs (single-nucleotide polymorphisms, or SNPs) to larger-scale structural variants, such as inversions, deletions, and duplications. SNPs have long been used as the general currency for investigations into how genetic diversity fuels evolution. However, structural variants can affect more base pairs in the genome than SNPs and can be responsible for adaptive phenotypes due to their impact on linkage and recombination. In this study, we investigate the first steps needed to explore the genetic basis of an economically important growth trait in the marine teleost finfish Chrysophrys auratus using both SNP and structural variant data. Specifically, we use feature selection methods in machine learning to explore the relative predictive power of both types of genetic variants in explaining growth and discuss the feature selection results of the evaluated methods. Methods: SNP and structural variant callers were used to generate catalogues of variant data from 32 individual fish at ages 1 and 3 years. Three feature selection algorithms (ReliefF, Chi-square, and a mutual-information-based method) were used to reduce the dataset by selecting the most informative features. Following this selection process, the subset of variants was used as features to classify fish into small, medium, or large size categories using KNN, naïve Bayes, random forest, and logistic regression. The top-scoring features in each feature selection method were subsequently mapped to annotated genomic regions in the zebrafish genome, and a permutation test was conducted to see if the number of mapped regions was greater than when random sampling was applied. Results: Without feature selection, the prediction accuracies ranged from 0 to 0.5 for both structural variants and SNPs. Following feature selection, the prediction accuracy increased only slightly to between 0 and 0.65 for structural variants and between 0 and 0.75 for SNPs. The highest prediction accuracy for the logistic regression was achieved for age 3 fish using SNPs, although generally predictions for age 1 and 3 fish were very similar (ranging from 0–0.65 for both SNPs and structural variants). The Chi-square feature selection of SNP data was the only method that had a significantly higher number of matches to annotated genomic regions of zebrafish than would be explained by chance alone. Conclusions: Predicting a complex polygenic trait such as growth using data collected from a low number of individuals remains challenging. While we demonstrate that both SNPs and structural variants provide important information to help understand the genetic basis of phenotypic traits such as fish growth, the full complexities that exist within a genome cannot be easily captured by classical machine learning techniques. When using high-dimensional data, feature selection shows some increase in the prediction accuracy of classification models and provides the potential to identify unknown genomic correlates with growth. Our results show that both SNPs and structural variants significantly impact growth, and we therefore recommend that researchers interested in the genotype–phenotype map should strive to go beyond SNPs and incorporate structural variants in their studies as well. We discuss how our machine learning models can be further expanded to serve as a test bed to inform evolutionary studies and the applied management of species.

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Section: Methodsmentioning

confidence: 99%

The Relative Power of Structural Genomic Variation versus SNPs in Explaining the Quantitative Trait Growth in the Marine Teleost Chrysophrys auratus

Ruigrok

Xue

Catanach

et al. 2022

Genes

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“…Here, we consider two statistical parameters, i.e., mutual information and analysis of variance (ANOVA). Mutual information (MI) is a feature selection that measures the relationship between two variables [10]. Suppose a class c and probabilities define as p(c) and p(t) [10].…”

Section: Feature Selectionmentioning

confidence: 99%

“…Mutual information (MI) is a feature selection that measures the relationship between two variables [10]. Suppose a class c and probabilities define as p(c) and p(t) [10]. Then, mutual information, I (t,c) is defined as follows:…”

Section: Feature Selectionmentioning

confidence: 99%

Implementation of Ensemble Method in Schizophrenia Identification Based on Microarray Data

Purba

Nhita

Kurniawan

2022

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Schizophrenia is a chronic mental illness that leads the patient to hallucinations and delusions with a prevalence of 0.4% worldwide. The importance early detection of Schizophrenia is tracking the pre-syndrome of Schizophrenia during the active phase, and could reduce psychosis symptomatic. However, the method sometimes cannot detect the symptoms accurately. As an alternative, machine learning can be implemented on microarray data for early detection. This study aimed to implement three ensemble methods, i.e., Random Forest (RF), Adaptive Boosting (AdaBoost), and Extreme Gradient Boosting (XGBoost) to identify Schizophrenia. Hyperparameter tuning was performed to improve the performance of the models. Based on the results, we found that the model 6, which is developed by the XGBoost method, performs better than other models with the value of accuracy and F1-score are 0.87 and 0.87, respectively.

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“…Serum protein also used in the prediction of the mortality of severe COVID-19 patients (Shirvaliloo, 2021). Likewise, DNA methylation is used in this field to identify new methylations of the infection and in the predictions of other complications (Castro de Moura, et al, 2021;Nagpal & Singh, 2018). Research on multi-omic data also provide very prominent insights in this field.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Machine Learning Analysis on the Phenotypes of COVID-19 Patients Using Transcriptome Data

Jeyananthan

2022

AGJSR

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Purpose: Evolving technologies allow us to measure human molecular data in a wide reach. Those data are extensively used by researchers in many studies and help in advancements of medical field. Transcriptome, proteome, metabolome, and epigenome are few such molecular data. This study utilizes the transcriptome data of COVID-19 patients to uncover the dysregulated genes in the SARS-COV-2. Method: Selected genes are used in machine learning models to predict various phenotypes of those patients. Ten different phenotypes are studied here such as time since onset, COVID-19 status, connection between age and COVID-19, hospitalization status and ICU status, using classification models. Further, this study compares molecular characterization of COVID-19 patients with other respiratory diseases. Results: Gene ontology analysis on the selected features shows that they are highly related to viral infection. Features are selected using two methods and selected features are individually used in the classification of patients using six different machine learning algorithms. For each of the selected phenotype, results are compared to find the best prediction model. Conclusion: Even though, there are not any significant differences between the feature selection methods, random forest and SVM performs very well throughout all the phenotype studies.

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A Feature Selection Algorithm Based on Qualitative Mutual Information for Cancer Microarray Data

Cited by 26 publications

References 24 publications

The Relative Power of Structural Genomic Variation versus SNPs in Explaining the Quantitative Trait Growth in the Marine Teleost Chrysophrys auratus

The Relative Power of Structural Genomic Variation versus SNPs in Explaining the Quantitative Trait Growth in the Marine Teleost Chrysophrys auratus

Implementation of Ensemble Method in Schizophrenia Identification Based on Microarray Data

Comprehensive Machine Learning Analysis on the Phenotypes of COVID-19 Patients Using Transcriptome Data

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