Decision Tree Classifiers in Bioinformatics

Poļaka, Inese; Tom, Igor; Borisov, Arkady

doi:10.2478/v10143-010-0052-4

Cited by 13 publications

(11 citation statements)

References 18 publications

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“…Accurate conventional end procedure is a significant tree with too huge, overfitted and trivial trees, underfitted and suffers loss in accuracy. Algorithms have assembled overfitting strategies, labelled trimming, classifying new instances by leading the tree basis down a leaf, with respect to the examination result along the pathway [26]. Competent models are discovered using decision tree classifiers and ensembles, with unbalanced varying trained datasets, with resultant models totally unalike.…”

Section: Decision Treesmentioning

confidence: 99%

A genetic algorithm approach for predicting ribonucleic acid sequencing data classification using KNN and decision tree

Arowolo¹,

Adebiyi²,

Adebiyi³

2021

TELKOMNIKA

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Malaria larvae accept explosive variable lifecycle as they spread across numerous mosquito vector stratosphere. Transcriptomes arise in thousands of diverse parasites. Ribonucleic acid sequencing (RNA-seq) is a prevalent gene expression that has led to enhanced understanding of genetic queries. RNAseq tests transcript of gene expression, and provides methodological enhancements to machine learning procedures. Researchers have proposed several methods in evaluating and learning biological data. Genetic algorithm (GA) as a feature selection process is used in this study to fetch relevant information from the RNA-Seq Mosquito Anopheles gambiae malaria vector dataset, and evaluates the results using kth nearest neighbor (KNN) and decision tree classification algorithms. The experimental results obtained a classification accuracy of 88.3 and 98.3 percents respectively.

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Section: Decision Treesmentioning

confidence: 99%

A genetic algorithm approach for predicting ribonucleic acid sequencing data classification using KNN and decision tree

Arowolo¹,

Adebiyi²,

Adebiyi³

2021

TELKOMNIKA

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“…Most of the algorithms have a mechanism built in that deals with overfitting; it is called pruning. Each new instance is classified by navigating them from the root of the tree down lo a leaf, according to the outcome of the tests along the path [22]. Although decision trees produce efficient models, they are unstable -if the training data sets differ only slightly, the resulting models can be completely different for those two sets.…”

Section: Iiiiiiii Decision Treesmentioning

confidence: 99%

PCA Model For RNA-Seq Malaria Vector Data Classification Using KNN And Decision Tree Algorithm

Arowolo

Adebiyi

et al. 2020

2020 International Conference in Mathematics, Computer Engineering and Computer Science (ICMCECS)

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Malaria parasites adopt unresolved discrepancy of life segments as they grow through various mosquito vector stratospheres. Transcriptomes of thousands of individual parasites exists. Ribonucleic acid sequencing (RNA-seq) is a widespread method for gene expression which has resulted into improved understandings of genetical queries. RNA-seq compute transcripts of gene expressions. RNA-seq data necessitates analytical improvements of machine learning techniques. Several learning approached have been proposed by researchers for analyzing biological data. In this study, PCA feature extraction algorithm is used to fetch latent components out of a high dimensional malaria vector RNA-seq dataset, and evaluates it classification performance using KNN and Decision Tree classification algorithms. The effectiveness of this experiment is validated on a mosquito anopheles gambiae RNA-Seq dataset. The experiment result achieved a relevant performance metrics with a classification accuracy of 86.7% and 83.3% respectively.

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“…Each new instance is classified by navigating them from the root of the tree down to a leaf, according to the outcome of the tests along the path [20,21].…”

Section: Deterministic Classifiers Overviewmentioning

confidence: 99%

Deterministic Classifiers Accuracy Optimization for Cancer Microarray Data

Rodrigues

Deusdado

2019

Practical Applications of Computational Biology and Bioinformatics, 13th International Conference

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The objective of this study was to improve classification accuracy in cancer microarray gene expression data using a collection of machine learning algorithms available in WEKA. State of the art deterministic classification methods, such as: Kernel Logistic Regression, Support Vector Machine, Stochastic Gradient Descent and Logistic Model Trees were applied on publicly available cancer microarray datasets aiming to discover regularities that provide insights to help characterization and diagnosis correctness on each cancer typology. The implemented models, relying on 10-fold cross-validation, parameterized to enhance accuracy, reached accuracy above 90%. Moreover, although the variety of methodologies, no significant statistic differences were registered between them, at significance level 0.05, confirming that all the selected methods are effective for this type of analysis.

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Decision Tree Classifiers in Bioinformatics

Cited by 13 publications

References 18 publications

A genetic algorithm approach for predicting ribonucleic acid sequencing data classification using KNN and decision tree

A genetic algorithm approach for predicting ribonucleic acid sequencing data classification using KNN and decision tree

PCA Model For RNA-Seq Malaria Vector Data Classification Using KNN And Decision Tree Algorithm

Deterministic Classifiers Accuracy Optimization for Cancer Microarray Data

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