Biologically inspired intelligent decision making

Manning, Timmy; Sleator, Roy D.; Walsh, Paul

doi:10.4161/bioe.26997

Cited by 42 publications

(23 citation statements)

References 103 publications

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“…Neural networks typically consist of multiple designs, and the signal path traverses from front to back ( Figure I in the online-only Data Supplement). 6 ANN-based models are effective at capturing nonlinear relationships that make them ideal candidates for complex and multifactorial disease classification.…”

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confidence: 99%

Novel Screening Tool for Stroke Using Artificial Neural Network

Abedi

Goyal

Tsivgoulis

et al. 2017

Stroke

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Background and Purpose-The timely diagnosis of stroke at the initial examination is extremely important given the disease morbidity and narrow time window for intervention. The goal of this study was to develop a supervised learning method to recognize acute cerebral ischemia (ACI) and differentiate that from stroke mimics in an emergency setting. Methods-Consecutive patients presenting to the emergency department with stroke-like symptoms, within 4.5 hours of symptoms onset, in 2 tertiary care stroke centers were randomized for inclusion in the model. We developed an artificial neural network (ANN) model. The learning algorithm was based on backpropagation. To validate the model, we used a 10-fold cross-validation method. Results-A total of 260 patients (equal number of stroke mimics and ACIs) were enrolled for the development and validation of our ANN model. Our analysis indicated that the average sensitivity and specificity of ANN for the diagnosis of ACI based on the 10-fold cross-validation analysis was 80.0% (95% confidence interval, 71.8-86.3) and 86.2% (95% confidence interval, 78.7-91.4), respectively. The median precision of ANN for the diagnosis of ACI was 92% (95% confidence interval, 88.7-95.3). Conclusions-Our results show that ANN can be an effective tool for the recognition of ACI and differentiation of ACI from stroke mimics at the initial examination.

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mentioning

confidence: 99%

Novel Screening Tool for Stroke Using Artificial Neural Network

Abedi

Goyal

Tsivgoulis

et al. 2017

Stroke

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“…MLP is a powerful machine learning model that autonomously identifies complex data patterns and maps them to defined groups. It belongs to the class of artificial neuronal networks that can be used for gene identification approaches [31]. Machine learning algorithms can be trained for recognizing phenotypes [32].…”

Section: Kinome Screening and Automated Immunofluorescence Microscopymentioning

confidence: 99%

Cyclin-Dependent Kinase 18 Controls Trafficking of Aquaporin-2 and Its Abundance through Ubiquitin Ligase STUB1, Which Functions as an AKAP

Dema

Faust

Lazarow

et al. 2020

Cells

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Arginine-vasopressin (AVP) facilitates water reabsorption in renal collecting duct principal cells through regulation of the water channel aquaporin-2 (AQP2). The hormone binds to vasopressin V2 receptors (V2R) on the surface of the cells and stimulates cAMP synthesis. The cAMP activates protein kinase A (PKA), which initiates signaling that causes an accumulation of AQP2 in the plasma membrane of the cells facilitating water reabsorption from primary urine and fine-tuning of body water homeostasis. AVP-mediated PKA activation also causes an increase in the AQP2 protein abundance through a mechanism that involves dephosphorylation of AQP2 at serine 261 and a decrease in its poly-ubiquitination. However, the signaling downstream of PKA that controls the localization and abundance of AQP2 is incompletely understood. We carried out an siRNA screen targeting 719 kinase-related genes, representing the majority of the kinases of the human genome and analyzed the effect of the knockdown on AQP2 by high-content imaging and biochemical approaches. The screening identified 13 hits whose knockdown inhibited the AQP2 accumulation in the plasma membrane. Amongst the candidates was the so far hardly characterized cyclin-dependent kinase 18 (CDK18). Our further analysis revealed a hitherto unrecognized signalosome comprising CDK18, an E3 ubiquitin ligase, STUB1 (CHIP), PKA and AQP2 that controls the localization and abundance of AQP2. CDK18 controls AQP2 through phosphorylation at serine 261 and STUB1-mediated ubiquitination. STUB1 functions as an A-kinase anchoring protein (AKAP) tethering PKA to the protein complex and bridging AQP2 and CDK18. The modulation of the protein complex may lead to novel concepts for the treatment of disorders which are caused or are associated with dysregulated AQP2 and for which a satisfactory treatment is not available, e.g., hyponatremia, liver cirrhosis, diabetes insipidus, ADPKD or heart failure.

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“…El aprendizaje de la RNA se detiene cuando el índice de error resulta aceptablemente pequeño para cada uno de los patrones aprendidos o cuando el número máximo de iteraciones del proceso ha sido alcanzado [10], [14], [15]. La función de rendimiento utilizada para entrenar la RNA es el error cuadrático medio (MSE), denotado por la Ecuación 4 [10][11][12].…”

Section: Entrenamiento De La Rnaunclassified

“…La función de rendimiento utilizada para entrenar la RNA es el error cuadrático medio (MSE), denotado por la Ecuación 4 [10][11][12]. El error relativo, reflejado aritméticamente por la Ecuación 5, es involucrado en el análisis [10][11][12][13][14][15][16].…”

Section: Entrenamiento De La Rnaunclassified

Diseño de una red neuronal para la predicción del coeficiente de pérdidas primarias en régimen de flujo turbulento

Calderón

Castillo

Moreno

2018

ings

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La presente investigación está orientada al diseño de una red neuronal para la predicción del factor de fricción en régimen de flujo turbulento, siendo este indispensable para el cálculo de pérdidas primarias en conductos cerrados o tuberías. Se utiliza Neural Networks Toolbox de MATLAB® para diseñar la red neuronal artificial (RNA), con retropropagación, cuya base de datos comprende 724 puntos obtenidos del diagrama de Moody. Las variables de entrada de la RNA son el número de Reynolds y la rugosidad relativa de la tubería; la variable de salida es el coeficiente de fricción. Utilizando el algoritmo de entrenamiento de Levenberg-Marquardt se entrena la RNA con distintas topologías, variando el número de capas ocultas y el número de neuronas ocultas en cada capa. Con una estructura 2-30-30-1 de la RNA se obtuvo el mejor resultado, exhibiendo un error cuadrático medio (ECM) de 1,75E-8 y un coeficiente de correlación de Pearson R de 0,99999 entre la salida de la red neuronal y la salida deseada. Además, mediante un análisis descriptivo de variable en el software SPSS®, se obtiene que el error relativo medio es de 0,162 %, indicando que el modelo diseñado es capaz de generalizar con alta precisión.

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Biologically inspired intelligent decision making

Cited by 42 publications

References 103 publications

Novel Screening Tool for Stroke Using Artificial Neural Network

Novel Screening Tool for Stroke Using Artificial Neural Network

Cyclin-Dependent Kinase 18 Controls Trafficking of Aquaporin-2 and Its Abundance through Ubiquitin Ligase STUB1, Which Functions as an AKAP

Diseño de una red neuronal para la predicción del coeficiente de pérdidas primarias en régimen de flujo turbulento

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