Classification of RNA-Seq Data via Bagging Support Vector Machines

Zararsız, Gökmen; Göksülük, Dinçer; Korkmaz, Selçuk; Eldem, Vahap; Duru, İzzet Paruğ; Öztürk, Ahmet; Ünver, Turgay

doi:10.1101/007526

Cited by 6 publications

(11 citation statements)

References 59 publications

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“…Model validation step is critical step to cross check the efficiency of the model. Various model validation approaches like Holdout validation, k-fold cross-validation and bootstrapping have been emerged till date [2]. 8 Machine Learning-based state-of-the-art methods for the classification of RNA-Seq data…”

Section: Steps For Classification Model Buildingmentioning

confidence: 99%

“…Ensemble method for learning improves the predictive performance of a classifier in generating an accurate model. It is technically a collection of several base (weak) classifiers whose individual decisive result is summed up in a way that can classify new data points more effectively [2,29,30]. Fig.…”

Section: Categorization Of Machine Learning Techniquementioning

confidence: 99%

“…A machine learning approach called Bagging support vector machines (bagSVM) is a bagged ensemble of SVMs for classification of RNA-Seq data. bagSVM trains each of the SVM separately using bootstrapping technique then combines result from each trained SVM model using majority voting [2].…”

Section: Bagging Support Vector Machines (Bagsvm)mentioning

confidence: 99%

“…Method: Zararsiz et al, 2014 applied a bagSVM on simulated data [2] and real dataset [2]. The simulated datasets taken here was generated using a negative binomial distribution model and two of the real RNA-Seq datasets taken were obtained from public resources.…”

Section: Bagging Support Vector Machines (Bagsvm)mentioning

confidence: 99%

“…By applying such information one is able to develop various classification algorithms based on expression levels. It is now considered an emerging method for disease classification and diagnosis and identification of potential biomarkers of disease [2]. They are also being used to identify exogenous RNA contents (Viruses) from RNA-Seq data.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning-based state-of-the-art methods for the classification of RNA-Seq data

Jabeen

Ahmad

Raza

2017

Preprint

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Abstract. RNA-Seq measures expression levels of several transcripts simultaneously. The identified reads can be gene, exon, or other region of interest. Various computational tools have been developed for studying pathogen or virus from RNA-Seq data by classifying them according to the attributes in several predefined classes, but still computational tools and approaches to analyze complex datasets are still lacking. The development of classification models is highly recommended for disease diagnosis and classification, disease monitoring at molecular level as well as researching for potential disease biomarkers. In this chapter, we are going to discuss various machine learning approaches for RNA-Seq data classification and their implementation. Advancements in bioinformatics, along with developments in machine learning based classification, would provide powerful toolboxes for classifying transcriptome information available through RNA-Seq data.

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Section: Steps For Classification Model Buildingmentioning

confidence: 99%

Section: Categorization Of Machine Learning Techniquementioning

confidence: 99%

Section: Bagging Support Vector Machines (Bagsvm)mentioning

confidence: 99%

Section: Bagging Support Vector Machines (Bagsvm)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Machine Learning-based state-of-the-art methods for the classification of RNA-Seq data

Jabeen

Ahmad

Raza

2017

Preprint

View full text Add to dashboard Cite

show abstract

Machine Learning-Based State-of-the-Art Methods for the Classification of RNA-Seq Data

Jabeen

Ahmad

Raza

2017

Lecture Notes in Computational Vision and Biomechanics

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Computational systems biology approaches for Parkinson’s disease

Glaab

2017

Cell Tissue Res

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Parkinson’s disease (PD) is a prime example of a complex and heterogeneous disorder, characterized by multifaceted and varied motor- and non-motor symptoms and different possible interplays of genetic and environmental risk factors. While investigations of individual PD-causing mutations and risk factors in isolation are providing important insights to improve our understanding of the molecular mechanisms behind PD, there is a growing consensus that a more complete understanding of these mechanisms will require an integrative modeling of multifactorial disease-associated perturbations in molecular networks. Identifying and interpreting the combinatorial effects of multiple PD-associated molecular changes may pave the way towards an earlier and reliable diagnosis and more effective therapeutic interventions. This review provides an overview of computational systems biology approaches developed in recent years to study multifactorial molecular alterations in complex disorders, with a focus on PD research applications. Strengths and weaknesses of different cellular pathway and network analyses, and multivariate machine learning techniques for investigating PD-related omics data are discussed, and strategies proposed to exploit the synergies of multiple biological knowledge and data sources. A final outlook provides an overview of specific challenges and possible next steps for translating systems biology findings in PD to new omics-based diagnostic tools and targeted, drug-based therapeutic approaches.

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Classification of RNA-Seq Data via Bagging Support Vector Machines

Abstract: Background

Cited by 6 publications

References 59 publications

Machine Learning-based state-of-the-art methods for the classification of RNA-Seq data

Machine Learning-based state-of-the-art methods for the classification of RNA-Seq data

Machine Learning-Based State-of-the-Art Methods for the Classification of RNA-Seq Data

Computational systems biology approaches for Parkinson’s disease

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