Current Applications of Artificial Neural Networks in Biochemistry with Emphasis on Cancer Research

Cancilla, John C.; Torrecilla, José S.; Matute, Gemma

doi:10.2174/22127119113019990004

Cited by 12 publications

(23 citation statements)

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“…In this work, a supervised multilayer perceptron (MLP) [Knoerzer et al, 2011], the most commonly applied type of ANN [Cancilla et al, 2014a], was employed to assess the presence of liquid water on the surface and inside of halite pinnacles. The fact that these models are supervised implies that target data are required to train the nonlinear model [Hush and Horne, 1993].…”

Section: Design Of the Artificial Neural Network-based Modelmentioning

confidence: 99%

“…Extra epochs take place until the errors provided by the test done with the verification data set start to increase. This is the moment when the MLP can be considered optimized [Cancilla et al, 2014a]. The above-mentioned verification errors are determined by the ANN through the mean prediction error (MPE; equation (3)).…”

Section: Training the Nonlinear Ann Modelmentioning

confidence: 99%

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Application of artificial neural networks as a tool for moisture prediction in microbially colonized halite in the Atacama Desert

et al. 2015

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The Atacama Desert is the driest and one of the most life-limiting places on Earth. Despite the extreme conditions, microbial endolithic communities have been found inside halite rocks. The presence of these microbial communities is possible due to the hygroscopic properties of evaporitic rocks composed of sodium chloride. It is important to elucidate every possible water source in such a hyperarid environment. Therefore, in the present study, an artificial neural network (ANN) based model has been designed to predict the presence of liquid water on the surface of halite pinnacles. The model predicts the moisture formation using two basic meteorological variables, air temperature, and air relative humidity. ANNs have been successfully employed for the first time as a tool for predicting the appearance of liquid water, a key factor for the endolithic microbial communities living in the driest part of the Atacama Desert. The model developed is able to correctly predict the formation of water on the surface of the halite pinnacles 83% of the cases. We anticipate the future application of this model as an important tool for the prediction of the water availability and therefore potential habitability of lithic substrates in extreme environments on Earth and perhaps elsewhere.

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Section: Design Of the Artificial Neural Network-based Modelmentioning

confidence: 99%

Section: Training the Nonlinear Ann Modelmentioning

confidence: 99%

Application of artificial neural networks as a tool for moisture prediction in microbially colonized halite in the Atacama Desert

et al. 2015

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“…These provide additional structural information on the MLP because the polarisability of the anions and cations allows the correct definition of the electrostatic interaction between the two ions of the ionic liquid (vide supra) and, therefore, permits a clearer understanding of the behaviour of the ionic liquids, leading to a better comprehension of the MLP model. 28,32 The software applied to create the MLP and perform the experimental design to optimise its parameters (ESI †) was Matlab version 7.0.1.24704 (R14). 33…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Estimation of the refractive indices of imidazolium-based ionic liquids using their polarisability values

Díaz-Rodríguez

Cancilla

Plechkova

et al. 2014

Phys. Chem. Chem. Phys.

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Statistical models have been used to estimate the refractive index of 72 imidazolium-based ionic liquids using the electronic polarisability of their ions as the data for two different mathematical approaches: artificial neural networks, in the form of multi-layer perceptrons, and multiple linear regression models. Although the artificial neural networks and linear models have been able to accomplish this task, the multi-layer perceptron model has been shown to be a more accurate method, thanks to its ability of determining non-linear relationships between different dependent variables. Additionally, it is clear that the multiple linear regression presents a systematic deviation in the estimated refractive index values, which confirms that it is an inappropriate model for this system.

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“…The immense amount of data that is created by the FET sensors can be used to create mathematical models with a variety of algorithms. A reliable option is to employ artificial neural networks (ANNs), which are mathematical tools that shine in the modeling of complex databases by finding hidden nonlinear relationships among different independent variables (Cancilla et al, 2014). ANNs were inspired from the actual brain architecture, where signals are transferred from one neuron to the next through phenomena such as synapsis or membrane depolarization (Jain et al, 1996).…”

Section: Introductionmentioning

confidence: 99%

“…The input layer is formed by nodes, and they represent independent variables that are introduced into the MLP. The signals corresponding to each node of the input layer are processed by all of the neurons from the hidden layer, and the resulting calculated values are further processed by every neuron from the output layer (Cancilla et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Volatile Organic Compound Detection with FET Sensors and Neural Network Data Processing as a Preliminary Step to Early Lung Cancer Diagnosis

Cancilla

Wang

Díaz-Rodríguez

et al. 2014

Proceedings of the International Conference on Neural Computation Theory and Applications

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Cancer is currently one of deadliest and most feared diseases in the developed world, and, particularly, lung cancer (LC) is one of the most common types and has one of the highest death/incidence ratios. An early diagnosis for LC is probably the most accessible possibility to try and save patients and lower this ratio. Recently, research concerning LC-related breath biomarkers has provided optimistic results and has become a real option to try and obtain a fast, reliable, and early LC diagnosis. In this paper, a combination of fieldeffect transistor (FET) sensors and artificial neural networks (ANNs) has been employed to classify and estimate the partial pressures of a series of polar and nonpolar volatile organic compounds (VOCs) present in prepared gaseous mixtures. The objective of these preliminary tests is to give an idea of how well this technology can be used to analyze artificial or real breath samples by quantifying the LC-related VOCs or biomarkers. The results of this step are very promising and indicate that this methodology deserves further research using more complex samples to find the existing limitations of the FET-ANN combination. Recently, a new approach for cancer diagnosis is

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Current Applications of Artificial Neural Networks in Biochemistry with Emphasis on Cancer Research

Cited by 12 publications

References 0 publications

Application of artificial neural networks as a tool for moisture prediction in microbially colonized halite in the Atacama Desert

Application of artificial neural networks as a tool for moisture prediction in microbially colonized halite in the Atacama Desert

Estimation of the refractive indices of imidazolium-based ionic liquids using their polarisability values

Volatile Organic Compound Detection with FET Sensors and Neural Network Data Processing as a Preliminary Step to Early Lung Cancer Diagnosis

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