Automating Microarray Classification Using General Regression Neural Networks

Soares, Caio; Montgomery, Lacey S.; Rouse, Kenneth; Gilbert, Juan E.

doi:10.1109/icmla.2008.95

Cited by 6 publications

(5 citation statements)

References 19 publications

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“…The average success rate in classification is higher than 95% if the number of virtual neurons is above 400. Similar results have been obtained using ELMs 22 23 . The experimental and the numerical results show a very good agreement.…”

Section: Resultssupporting

confidence: 87%

A Unified Framework for Reservoir Computing and Extreme Learning Machines based on a Single Time-delayed Neuron

Ortín

Soriano

Pesquera

et al. 2015

Sci Rep

150

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In this paper we present a unified framework for extreme learning machines and reservoir computing (echo state networks), which can be physically implemented using a single nonlinear neuron subject to delayed feedback. The reservoir is built within the delay-line, employing a number of “virtual” neurons. These virtual neurons receive random projections from the input layer containing the information to be processed. One key advantage of this approach is that it can be implemented efficiently in hardware. We show that the reservoir computing implementation, in this case optoelectronic, is also capable to realize extreme learning machines, demonstrating the unified framework for both schemes in software as well as in hardware.

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Section: Resultssupporting

confidence: 87%

A Unified Framework for Reservoir Computing and Extreme Learning Machines based on a Single Time-delayed Neuron

Ortín

Soriano

Pesquera

et al. 2015

Sci Rep

150

View full text Add to dashboard Cite

show abstract

“…The General regression neural network (GRNN) is a memory-based learning algorithm proposed by Donald Specht in 1991 . The most notable merit of GRNN is fast learning speed and converges to the optimal regression surface as the number of samples increases. , GRNN is similar to a radial basis function network in topology structure, which is composed of four parts: input layer, patter layer, summation layer, and outputs layer. The GRNN diagram has been shown in Figure .…”

Section: Methodsmentioning

confidence: 99%

Protein–Protein Interaction Prediction Based on Spectral Radius and General Regression Neural Network

Zhang

et al. 2021

J. Proteome Res.

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Protein–protein interaction (PPI) not only plays a critical role in cell life activities, but also plays an important role in discovering the mechanism of biological activity, protein function, and disease states. Developing computational methods is of great significance for PPIs prediction since experimental methods are time-consuming and laborious. In this paper, we proposed a PPI prediction algorithm called GRNN-PPI only using the amino acid sequence information based on general regression neural network and two feature extraction methods. Specifically, we designed a new feature extraction method named Mutation Spectral Radius (MSR) to extract evolutionary information by the BLOSUM62 matrix. Meanwhile, we integrated another feature extraction method, autocorrelation description, which can completely extract information on physicochemical properties and protein sequences. The principal component analysis was applied to eliminate noise, and the general regression neural network was adopted as a classifier. The prediction accuracy of the yeast, human, and Helicobacter pylori1 (H. pylori1) data sets were 97.47%, 99.63%, and 99.97%, respectively. In addition, we also conducted experiments on two important PPI networks and six independent data sets. All results were significantly higher than some state-of-the-art methods used for comparison, showing that our method is feasible and robust.

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“…The dataset for colon cancer includes 62 samples whereas the leukemia dataset had 72 samples. The accuracy achieved on the colon dataset is 78.4% and the leukemia dataset is 89.5% 84 . The use of ANNs for genetics is innovative and demonstrates its remarkable processing power.…”

Section: Applications Of Anns In the Field Of Medicinementioning

confidence: 99%

“…Achieving high accuracy using an unsupervised ANN for medical diagnosis is especially captivating as it is rarely applied in this domain due to low accuracy 23 . Soares et al explore the micro‐array classification which compares genes of an unknown patient with the known ones for diagnosing and predicting diseases 84 . They have used GRNN with particle swarm optimizer for the prediction of micro‐array classification.…”

Section: Applications Of Anns In the Field Of Medicinementioning

confidence: 99%

Artificial neural networks as clinical decision support systems

Shafi

Ansari

Din

et al. 2021

Concurrency and Computation

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Summary In the last decade, artificial intelligent systems based on neural networks have gradually become primary source for clinical decision support systems (CDSS) and are being used in diverse areas of medical diagnosis, classification, and prediction. An artificial neural network (ANN) consists of a large number of processing units which performs the computation in a parallel and distributed environment. They learn the pattern from the examples provided to it and then generalize based on the concepts they have learned while training. This paper presents a review of the current status of ANN and its variants as CDSS in various medical disciplines. The work focuses and describes the methods making use of simple ANN and use of real‐time approaches based on big data using ANN in cloud computing environment for various medical applications. Critical analysis of various methods based on smart approaches indicates that feed‐forward back propagation ANN performs sufficiently better in the domain of medicines with a high degree of accuracy.

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Automating Microarray Classification Using General Regression Neural Networks

Cited by 6 publications

References 19 publications

A Unified Framework for Reservoir Computing and Extreme Learning Machines based on a Single Time-delayed Neuron

A Unified Framework for Reservoir Computing and Extreme Learning Machines based on a Single Time-delayed Neuron

Protein–Protein Interaction Prediction Based on Spectral Radius and General Regression Neural Network

Artificial neural networks as clinical decision support systems

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