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RFMirTarget: Predicting Human MicroRNA Target Genes with a Random Forest Classifier  [PDF]
Mariana R. Mendoza, Guilherme C. da Fonseca, Guilherme Loss-Morais, Ronnie Alves, Rogerio Margis, Ana L. C. Bazzan
PLOS ONE , 2013, DOI: 10.1371/journal.pone.0070153
Abstract: MicroRNAs are key regulators of eukaryotic gene expression whose fundamental role has already been identified in many cell pathways. The correct identification of miRNAs targets is still a major challenge in bioinformatics and has motivated the development of several computational methods to overcome inherent limitations of experimental analysis. Indeed, the best results reported so far in terms of specificity and sensitivity are associated to machine learning-based methods for microRNA-target prediction. Following this trend, in the current paper we discuss and explore a microRNA-target prediction method based on a random forest classifier, namely RFMirTarget. Despite its well-known robustness regarding general classifying tasks, to the best of our knowledge, random forest have not been deeply explored for the specific context of predicting microRNAs targets. Our framework first analyzes alignments between candidate microRNA-target pairs and extracts a set of structural, thermodynamics, alignment, seed and position-based features, upon which classification is performed. Experiments have shown that RFMirTarget outperforms several well-known classifiers with statistical significance, and that its performance is not impaired by the class imbalance problem or features correlation. Moreover, comparing it against other algorithms for microRNA target prediction using independent test data sets from TarBase and starBase, we observe a very promising performance, with higher sensitivity in relation to other methods. Finally, tests performed with RFMirTarget show the benefits of feature selection even for a classifier with embedded feature importance analysis, and the consistency between relevant features identified and important biological properties for effective microRNA-target gene alignment.
Random forest classifier combined with feature selection for breast cancer diagnosis and prognostic  [PDF]
Cuong Nguyen, Yong Wang, Ha Nam Nguyen
Journal of Biomedical Science and Engineering (JBiSE) , 2013, DOI: 10.4236/jbise.2013.65070
Abstract:

As the incidence of this disease has increased significantly in the recent years, expert systems and machine learning techniques to this problem have also taken a great attention from many scholars. This study aims at diagnosing and prognosticating breast cancer with a machine learning method based on random forest classifier and feature selection technique. By weighting, keeping useful features and removing redundant features in datasets, the method was obtained to solve diagnosis problems via classifying Wisconsin Breast Cancer Diagnosis Dataset and to solve prognosis problem via classifying Wisconsin Breast Cancer Prognostic Dataset. On these datasets we obtained classification accuracy of 100% in the best case and of around 99.8% on average. This is very promising compared to the previously reported results. This result is for Wisconsin Breast Cancer Dataset but it states that this method can be used confidently for other breast cancer diagnosis problems, too.

Statistical classification of magnetic resonance images of brain employing random forest classifier
Joshi S.,Deepa Shenoy P.,Venugopal K. R.,Patnaik L.M.
International Journal of Machine Intelligence , 2009,
Abstract: Data mining in brain imaging is an emerging field of high importance for providing prognosis,treatment, and a deeper understanding of how the brain functions. Dementia due to Alzheimer’s diseaseconstitutes the fourth most common disorder among the elderly. Early detection of dementia and correctstaging of the severity of dementia is critical to select the optional treatment. The present study wasdesigned to classify and categorize brain images of dementia patients into three distinct classes i.e., Normal,Moderately diseased, and Severe. Decision Forest Classifier was employed to classify the various MagneticResonance Images (MRIs) of dementia patients. Results of screening the MRIs are organized byclassification and finally grouped into the three categories, i.e., Normal, Moderate and Severe. Experimentalresults obtained indicated that the proposed method performs relatively well with the classification accuracyreaching nearly 99.32% in comparison with the already existing algorithms.
Radiofrequency Ablation In Ventricular Fibrillation  [cached]
Jayachandran Thejus,Johnson Francis,Michel Haissaguerre
Indian Pacing and Electrophysiology Journal , 2008,
Abstract: Ventricular fibrillation (VF) is the most common arrhythmic cause of sudden cardiac death. The present recommendation for prevention of VF is ICD implantation. However, ICDs are costly; the shocks they give are often very uncomfortable for the patient and they may need reimplantation if there is battery depletion. Radiofrequency ablation of VF was pioneered by Haissaguerre1 and is presently being tried by many investigators.
A quantitative measurement of spatial order in ventricular fibrillation  [PDF]
P. V. Bayly,E. E. Johnson,P. D. Wolf,H. S. Greenside,W. M. Smith,R. E. Ideker
Physics , 1993,
Abstract: As an objective measurement of spatial order in ventricular fibrillation (VF), spatial correlation functions and their characteristic lengths were estimated from epicardial electrograms of pigs in VF. The correlation length of the VF in pigs was found to be approximately 4-10 mm, varying as fibrillation progressed. The degree of correlation decreased in the first 4 seconds after fibrillation then increased over the next minute. The correlation length is much smaller than the scale of the heart, suggesting that many independent regions of activity exist on the epicardium at any one time. On the other hand, the correlation length is 4 to 10 times the interelectrode spacing, indicating that some coherence is present. These results imply that the heart behaves during VF as a high-dimensional, but not random, system involving many spatial degrees of freedom, which may explain the lack of convergence of fractal dimension estimates reported in the literature. Changes in the correlation length also suggest that VF reorganizes slightly in the first minute after an initial breakdown in structure.
Amiodarone for the treatment and prevention of ventricular fibrillation and ventricular tachycardia
Hugo Van Herendael, Paul Dorian
Vascular Health and Risk Management , 2010, DOI: http://dx.doi.org/10.2147/VHRM.S6611
Abstract: miodarone for the treatment and prevention of ventricular fibrillation and ventricular tachycardia Review (5002) Total Article Views Authors: Hugo Van Herendael, Paul Dorian Published Date June 2010 Volume 2010:6 Pages 465 - 472 DOI: http://dx.doi.org/10.2147/VHRM.S6611 Hugo Van Herendael, Paul Dorian Division of Cardiology, St. Michael’s Hospital, University of Toronto, Toronto, Canada Abstract: Amiodarone has emerged as the leading antiarrhythmic therapy for termination and prevention of ventricular arrhythmia in different clinical settings because of its proven efficacy and safety. In patients with shock refractory out-of-hospital cardiac arrest and hemodynamically destabilizing ventricular arrhythmia, amiodarone is the most effective drug available to assist in resuscitation. Although the superiority of the transvenous implantable cardioverter defibrillator (ICD) over amiodarone has been well established in the preventive treatment of patients at high risk of life-threatening ventricular arrhythmias, amiodarone (if used with a beta-blocker) is the most effective antiarrhythmic drug to prevent ICD shocks and treat electrical storm. Both the pharmacokinetics and the electrophysiologic profile of amiodarone are complex, and its optimal and safe use requires careful patient surveillance with respect to potential adverse effects.
Amiodarone for the treatment and prevention of ventricular fibrillation and ventricular tachycardia  [cached]
Hugo Van Herendael,Paul Dorian
Vascular Health and Risk Management , 2010,
Abstract: Hugo Van Herendael, Paul DorianDivision of Cardiology, St. Michael’s Hospital, University of Toronto, Toronto, CanadaAbstract: Amiodarone has emerged as the leading antiarrhythmic therapy for termination and prevention of ventricular arrhythmia in different clinical settings because of its proven efficacy and safety. In patients with shock refractory out-of-hospital cardiac arrest and hemodynamically destabilizing ventricular arrhythmia, amiodarone is the most effective drug available to assist in resuscitation. Although the superiority of the transvenous implantable cardioverter defibrillator (ICD) over amiodarone has been well established in the preventive treatment of patients at high risk of life-threatening ventricular arrhythmias, amiodarone (if used with a beta-blocker) is the most effective antiarrhythmic drug to prevent ICD shocks and treat electrical storm. Both the pharmacokinetics and the electrophysiologic profile of amiodarone are complex, and its optimal and safe use requires careful patient surveillance with respect to potential adverse effects.Keywords: amiodarone, ventricular fibrillation, unstable ventricular tachycardia
May Fever Trigger Ventricular Fibrillation?  [cached]
Jean Luc Pasquié
Indian Pacing and Electrophysiology Journal , 2005,
Abstract: The clinical precipitants of ventricular fibrillation (VF) remain poorly understood. Clinical factors such as hypoxemia, acidosis or electrolyte imbalance, drug-related toxicity, autonomic nervous system disorders as well as viral myocarditis have been proposed to be associated with sudden cardiac death particularly in patients with structural heart disease. However, In the Brugada syndrome, concurrent febrile illness has been reported to unmask the electrocardiographic features of the Brugada syndrome and be associated with an increased propensity for VF. More recently, a febrile illnesses of infectious etiology was associated to polymorphic ventricular tachycardia or VF in patients with normal hearts and without known repolarization abnormality. In this review we detail this phenomenon and its putative mechanisms.
Electrophysiological Mechanisms of Ventricular Fibrillation Induction  [cached]
Nipon Chattipakorn,Kirkwit Shinlapawittayatorn,Siriporn Chattipakorn
Indian Pacing and Electrophysiology Journal , 2005,
Abstract: Ventricular fibrillation (VF) is known as a main responsible cause of sudden cardiac death which claims thousands of lives each year. Although the mechanism of VF induction has been investigated for over a century, its definite mechanism is still unclear. In the past few decades, the development of new advance technologies has helped investigators to understand how the strong stimulus or the shock induces VF. New hypotheses have been proposed to explain the mechanism of VF induction. This article reviews most commonly proposed hypotheses that are believed to be the mechanism of VF induction.
Monitoring the complexity of ventricular response in atrial fibrillation  [PDF]
H. K smacher,S. Wiese,M. Lahl
Discrete Dynamics in Nature and Society , 2000, DOI: 10.1155/s1026022600000066
Abstract: Atrial fibrillation does not present a uniform extent of variability of the ventricular response exemplifying periodicities and more complex fluctuations, due to varying number and shape of atrial wavelets and aberrant conduction in the AV-junction. It was sought to categorise different degrees of complexity introducing an uncomplicated monitoring method for that objective.
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