A survey of neural network-based cancer prediction models from microarray data

Daoud, Maisa; Mayo, Michael

doi:10.1016/j.artmed.2019.01.006

Cited by 105 publications

(64 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…in which η w , η γ and η σ are the adaptive steps of w p,n , γ γ γ n and σ 2 n , respectively. Also, ∇ w , ∇ γ and ∇ σ are, respectively, the complex gradient operators of w p,n , γ γ γ n and σ 2 n . The CMM-RBF cost function is the same utilized in the LMS algorithm Equation (4).…”

Section: Complex Mimo Radial Basis Function Neural Network For Beamfomentioning

confidence: 99%

“…In the last decades, artificial neural networks (ANNs) have attracted much attention, performing specific tasks in different applications, such as clustering, prediction, classification, pattern recognition, machine learning and artificial intelligence. As ANNs are mainly designed to mimic the human brain, a considerable number of approaches only handle real-valued signals [1][2][3][4]. However, some engineering problems are intrinsically dependent on complex-valued signals (e.g., channel equalization and beamforming).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Complex MIMO RBF Neural Networks for Transmitter Beamforming over Nonlinear Channels

Mayer

Soares

Arantes

2020

Sensors

View full text Add to dashboard Cite

The use of beamforming for efficient transmission has already been successfully implemented in practical systems and is absolutely necessary to even further increase spectral and energy efficiencies in some configurations of the next-generation wireless systems and for low earth orbit satellites. A remarkable capacity increase is then achieved and spectral congestion is minimized. In this context, this article proposes a novel complex multiple-input multiple-output radial basis function neural network (CMM-RBF) for transmitter beamforming, based on the phase transmittance radial basis function neural network (PTRBFNN). The proposed CMM-RBF is compared with the least mean square (LMS) algorithm for beamforming with six dipoles arranged in a uniform and circular array and with 16 dipoles in a 2D-grid array. Simulation results show that the proposed solution presents lower steady-state mean squared error, faster convergence rate and enhanced half-power beamwidth (HPBW) when compared with the LMS algorithm in a nonlinear scenario.

show abstract

Section: Complex Mimo Radial Basis Function Neural Network For Beamfomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Complex MIMO RBF Neural Networks for Transmitter Beamforming over Nonlinear Channels

Mayer

Soares

Arantes

2020

Sensors

View full text Add to dashboard Cite

show abstract

“…We note that both the representation of the patients based on readily interpretable clinical values and the classification decision that corresponds to assigning a severity-probability are unique to this work. It stands in contrast to most recently published work in machine learning within the clinical domain [29][30][31] where a complex model architecture based on artificial neural networks is used, typically acting as a 'black-box' that provides the categorization of the patient without the ability to track down the justification or explanation.…”

Section: Classificationmentioning

confidence: 99%

HCM-AF-Risk Model to Identify Cases and Predictors of Atrial Fibrillation in Hypertrophic Cardiomyopathy

Bhattacharya

Greenland

et al. 2020

Preprint

View full text Add to dashboard Cite

Background AF in HCM is associated with high stroke risk despite low CHA2DS2-VASc scores. Hence, there is need to understand AF pathophysiology and predict AF in HCM. We develop/apply a data-driven, machine learning-based method to identify AF cases and clinical features associated with AF in HCM, using electronic health record (EHR) data. Methods Patients with documented paroxysmal/ persistent/permanent AF (n=191) were considered AF cases, and the remaining patients in sinus rhythm (n=640) were tagged as No-AF. We evaluated 93 clinical variables; the most informative variables useful for distinguishing AF from No-AF cases were selected based on the 2-sample t-test and information gain criterion. Results We identified 18 highly informative clinical variables: 11 are positively associated (e.g. LA-diameter, LV-diastolic dysfunction, LV-LGE), and 7 are negatively correlated (e.g. several exercise parameters) with AF in HCM. Next, patient records were represented via these 18 variables. Data imbalance resulting from the relatively low number of AF cases was addressed via a combination of over-and under-sampling strategies. We trained and tested multiple classifiers under this sampling approach, showing effective classification. Specifically, an ensemble of logistic regression and naïve Bayes classifiers, trained based on the 18 variables and corrected for data imbalance, proved most effective for separating AF from No-AF cases (sensitivity=0.74, specificity=0.72, C-index=0.80). Conclusions Our model (HCM-AF-Risk Model), the first machine learning-based method for identification of AF cases in HCM, demonstrates good performance, and suggests that AF is associated with a more severe cardiac HCM-phenotype.

show abstract

“…ANNs have the ability to model nonlinear and complex problems and are easy to implement, as numerous libraries for various programming languages are available. The high generalizability of this technique is also a highlight, as data outside the training set is admissible in the system [15,24,29].…”

Section: Related Workmentioning

confidence: 99%

Prediction of Motor Failure Time Using An Artificial Neural Network

Sampaio

Filho

Silva³

et al. 2019

Sensors

View full text Add to dashboard Cite

Industry is constantly seeking ways to avoid corrective maintenance so as to reduce costs. Performing regular scheduled maintenance can help to mitigate this problem, but not necessarily in the most efficient way. In the context of condition-based maintenance, the main contributions of this work were to propose a methodology to treat and transform the collected data from a vibration system that simulated a motor and to build a dataset to train and test an Artificial Neural Network capable of predicting the future condition of the equipment, pointing out when a failure can happen. To achieve this goal, a device model was built to simulate typical motor vibrations, consisting of a computer cooler fan and several magnets. Measurements were made using an accelerometer, and the data were collected and processed to produce a structured dataset. The neural network training with this dataset converged quickly and stably, while the tests performed, k-fold cross-validation and model generalization, presented excellent performance. The same tests were performed with other machine learning techniques, to demonstrate the effectiveness of neural networks mainly in their generalizability. The results of the work confirm that it is possible to use neural networks to perform predictive tasks in relation to the conditions of industrial equipment. This is an important area of study that helps to support the growth of smart industries.

show abstract

A survey of neural network-based cancer prediction models from microarray data

Cited by 105 publications

References 52 publications

Complex MIMO RBF Neural Networks for Transmitter Beamforming over Nonlinear Channels

Complex MIMO RBF Neural Networks for Transmitter Beamforming over Nonlinear Channels

HCM-AF-Risk Model to Identify Cases and Predictors of Atrial Fibrillation in Hypertrophic Cardiomyopathy

Prediction of Motor Failure Time Using An Artificial Neural Network

Contact Info

Product

Resources

About