Efficient large Pearson correlation matrix computing using hybrid MPI/CUDA

Kijsipongse, Ekasit; U-ruekolan, Suriya; Ngamphiw, Chumpol; Tongsima, Sissades

doi:10.1109/jcsse.2011.5930127

Cited by 28 publications

(10 citation statements)

References 9 publications

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“…In [32], a parallel approach using GPU to compute the Pcc matrix in a Magnetic Resonance Imaging, MRI, images to estimate the functional interactions in human's brain. Another work for calculate the PCC matrix using a hybrid approach of the MPI, and the Compute Unified Device Architecture, CUDA has been developed in [33]. And in [23], a model for estimating the scalability of the parallel algorithms in the Cluster platform has been presented.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparing MapReduce and Spark in Computing the PCC Matrix in Gene Co-expression Networks

Osman¹,

Seoud²

2021

IJACSA

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Correlation between gene expression profiles across multiple samples and the identification of inter-gene interactions is a critical technique for Co-expression networking. Due to the highly intensive processing of calculating the Pearson's Correlation Coefficient, PCC, matrix, it often takes too much processing time to accomplish it. Therefore, in this work, Big Data techniques including MapReduce and Spark have been employed in a cloud environment to calculate the PCC matrix to find the dependencies between genes measured in high throughput microarray. A comparison between the running time of each phase in both of MapReduce and Spark approaches has been held. Both these techniques can dramatically speed up the computation allowing users to work with highly intensive processing. However, Spark has yielded a better performance than the MapReduce as it performs the processing in the main memory of the worker nodes and avoids the unnecessary I/O operations with the disks. Spark has yielded 80 times speed up for calculating the PCC of 22777 genes, however the MapReduce attained barely 8 times speed up.

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Section: Literature Reviewmentioning

confidence: 99%

Comparing MapReduce and Spark in Computing the PCC Matrix in Gene Co-expression Networks

Osman¹,

Seoud²

2021

IJACSA

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“…The Pearson correlation coefficient is the covariance separated by the standard deviation results of the two factors. The types of definitions include "product moments", that is, the mean value of the results of the mean balance irregular factors; therefore, the modifier item comes second in the name [21].…”

Section: Pearson Correlation Matrixmentioning

confidence: 99%

Hybrid Machine Learning Classifier and Ensemble Techniques to Detect Parkinson’s Disease Patients

Yadav

Singh

2021

SN COMPUT. SCI.

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Parkinson's disease is caused by tumors, a progressive nervous system disorder that affects development. Stiffness or slow movement is the basic sign of this problem. There is no cure for Parkinson's disease, but some drugs can improve the condition, and sometimes brain surgery can help patients improve their condition. Using machine learning strategies, we developed a priori model to identify patients affected by Parkinson's disease. By controlling the importance of features, we recognize the most significant indicators of patients who belong to this disease-related estimate. The model-based logic strategies we use include logistic regression (LR), k nearest neighbors (k-NN), support vector classifier (SVC), gradient boosting classifier (GBC), and random forest classifier (RF). The estimated reliability, like the ROC curve and confusion matrix, is fivefold cross-validation. We construct another model that depends on the ensemble method and utilization of majority voting, weighted average, bagging, Ada_boost and Gradient_boosting. The model is also recognized in the five-fold cross-validation and confusion matrix, precision; recall rate and F1 score. The correlation matrix is also drawn to show whether these features are related to each other. Our findings indicate that, compared with different methods, machine learning can provide more reliable clinical outcome assessments for patients with Parkinson's disease. Among the five algorithms, the higher accuracy fluctuates in the middle of 70-95%. Among them, SVC obtains 93.83% accuracy from the five basic classifiers, and Bagging obtains 73.28% accuracy from the ensemble technique.

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“…• Implementação de um método eficiente para o cálculo de coeficiente de correlação de Pearson em matrizes (Kijsipongse et al, 2011).…”

Section: Konectaunclassified

Reflexões Acerca Do Realismo E Da Representação Visual Em Games Tendências De Mercado E Jogos Aaa

Aquino¹,

Obregon²,

Couto³

2019

A Produção Do Conhecimento Na Engenharia Da Computação

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Efficient large Pearson correlation matrix computing using hybrid MPI/CUDA

Cited by 28 publications

References 9 publications

Comparing MapReduce and Spark in Computing the PCC Matrix in Gene Co-expression Networks

Comparing MapReduce and Spark in Computing the PCC Matrix in Gene Co-expression Networks

Hybrid Machine Learning Classifier and Ensemble Techniques to Detect Parkinson’s Disease Patients

Reflexões Acerca Do Realismo E Da Representação Visual Em Games Tendências De Mercado E Jogos Aaa

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