A comparative study of different machine learning methods on microarray gene expression data

Pirooznia, Mehdi; Yang, Jack; Yang, Mary Qu; Deng, Youping

doi:10.1186/1471-2164-9-s1-s13

Cited by 224 publications

(156 citation statements)

References 27 publications

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“…However, machine-learning techniques have been used extensively in medicine, 21 in gene expression studies, [22][23][24] for classification of cardiac arrhythmias, 25 for predicting morbidity after coronary artery bypass surgery, 26 and for predicting when weaning from ventilator support should begin. 27 Gaussian processes have been applied in adults with ALI to model the pressure-volume curve to titrate PEEP.…”

Section: Discussionmentioning

confidence: 99%

Algorithms to Estimate P_aCO₂and pH Using Noninvasive Parameters for Children with Hypoxemic Respiratory Failure

et al. 2013

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BACKGROUND: Ventilator management for children with hypoxemic respiratory failure may benefit from ventilator protocols, which rely on blood gases. Accurate noninvasive estimates for pH or P aCO 2 could allow frequent ventilator changes to optimize lung-protective ventilation strategies. If these models are highly accurate, they can facilitate the development of closed-loop ventilator systems. We sought to develop and test algorithms for estimating pH and P aCO 2 from measures of ventilator support, pulse oximetry, and end-tidal carbon dioxide pressure (P ETCO 2 ). We also sought to determine whether surrogates for changes in dead space can improve prediction. METHODS: Algorithms were developed and tested using 2 data sets from previously published investigations. A baseline model estimated pH and P aCO 2 from P ETCO 2 using the previously observed relationship between P ETCO 2 and P aCO 2 or pH (using the Henderson-Hasselbalch equation). We developed a multivariate gaussian process (MGP) model incorporating other available noninvasive measurements. RESULTS: The training data set had 2,386 observations from 274 children, and the testing data set had 658 observations from 83 children. The baseline model predicted P aCO 2 within ؎ 7 mm Hg of the observed P aCO 2 80% of the time. The MGP model improved this to ؎ 6 mm Hg. When the MGP model predicted P aCO 2 between 35 and 60 mm Hg, the 80% prediction interval narrowed to ؎ 5 mm Hg. The baseline model predicted pH within ؎ 0.07 of the observed pH 80% of the time. The MGP model improved this to ؎ 0.05. CONCLUSIONS: We have demonstrated a conceptual first step for predictive models that estimate pH and P aCO 2 to facilitate clinical decision making for children with lung injury. These models may have some applicability when incorporated in ventilator protocols to encourage practitioners to maintain permissive hypercapnia when using high ventilator support. Refinement with additional data may improve model accuracy.

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Section: Discussionmentioning

confidence: 99%

Algorithms to Estimate P_aCO₂and pH Using Noninvasive Parameters for Children with Hypoxemic Respiratory Failure

et al. 2013

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“…The algorithms divide all objects into a predetermined number of groups in a manner that maximizes a similarity function. There are two different approaches, that are commonly used in medical studies ( [35] and [36]): the Expectation Maximization (EM) probabilistic method and deterministic k-means algorithm.…”

Section: Unsupervised Learning With Clusteringmentioning

confidence: 99%

Supervised and Unsupervised Machine Learning for Improved Identification of Intrauterine Growth Restriction Types

Wosiak¹,

Zamecznik²,

Niewiadomska-Jarosik³

2016

Annals of Computer Science and Information Systems

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Abstract-This paper concerns automated identification of intrauterine growth restriction (IUGR) types by use of machine learning methods. The research presents a comparison of supervised and unsupervised learning covering single and hybrid classification, as well as clustering. Supervised learning techniques included bagging with Naïve Bayes, k-nearest neighbours (kNN), C4.5 and SMO as base classifiers, random forest as a variant of bagging with a decision tree as a base classifier, boosting with Naïve Bayes, SMO, kNN and C4.5 as base classifiers, and voting by all single classifiers using majority as a combination rule, as well as five single classification strategies: kNN, C4.5, Naïve Bayes, random tree and sequential minimal optimization algorithm for training support vector machines. Unsupervised learning encompassed k-means and expectation-maximization algorithms. The major conclusion drawn from the study was that hybrid classifiers have demonstrated their potential ability to identify more accurately symmetrical and asymmetrical types of IUGR, whereas the unsupervised learning techniques produced the worst results.

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“…In [9] the efficiency of the classification methods including SVM, RBF Neural Nets, MLP Neural Nets, Bayesian, Decision Tree and Random Forrest methods were compared. Some of the common clustering techniques including Kmeans, DBC, and EM algorithms were applied to the datasets and the efficiency of these methods has been analyzed.…”

Section: Related Workmentioning

confidence: 99%

“…They divide the objects into a predetermined number of groups in a manner that maximizes a similarity function. During investigations of the proposed methodology, two different approaches, commonly used in medical studies ( [9]) will be considered: the Expectation Maximization (EM) probabilistic approach and deterministic k-means algorithm.…”

Section: Clusteringmentioning

confidence: 99%

Feature Selection for Classification Incorporating Less Meaningful Attributes in Medical Diagnostics

Wosiak

Zakrzewska

2014

Annals of Computer Science and Information Systems

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Abstract-In medical diagnostics there is a constant need of searching for new methods of attribute acquiring, but it is difficult to asses if these new features can support the existing ones and can be useful in medical inference. In the paper the methodology of discovering features which are less informative while considering independently, however meaningful for diagnosis making, is investigated. The proposed methodology can contribute to better use of attributes, which have not been considered in the diagnostics process so far. The experimental study, which concerns arterial hypertension as one of the civilization diseases demanding early detection and improved treatment is presented. The experiments confirmed that additional attributes enable obtaining the diagnostic results comparable to the ones received by using the most obvious features.

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A comparative study of different machine learning methods on microarray gene expression data

Cited by 224 publications

References 27 publications

Algorithms to Estimate P_aCO₂and pH Using Noninvasive Parameters for Children with Hypoxemic Respiratory Failure

Algorithms to Estimate P_aCO₂and pH Using Noninvasive Parameters for Children with Hypoxemic Respiratory Failure

Supervised and Unsupervised Machine Learning for Improved Identification of Intrauterine Growth Restriction Types

Feature Selection for Classification Incorporating Less Meaningful Attributes in Medical Diagnostics

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A comparative study of different machine learning methods on microarray gene expression data

Cited by 224 publications

References 27 publications

Algorithms to Estimate PaCO2and pH Using Noninvasive Parameters for Children with Hypoxemic Respiratory Failure

Algorithms to Estimate PaCO2and pH Using Noninvasive Parameters for Children with Hypoxemic Respiratory Failure

Supervised and Unsupervised Machine Learning for Improved Identification of Intrauterine Growth Restriction Types

Feature Selection for Classification Incorporating Less Meaningful Attributes in Medical Diagnostics

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Product

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Algorithms to Estimate P_aCO₂and pH Using Noninvasive Parameters for Children with Hypoxemic Respiratory Failure

Algorithms to Estimate P_aCO₂and pH Using Noninvasive Parameters for Children with Hypoxemic Respiratory Failure