Predictive non-linear modeling of complex data by artificial neural networks.

By J. Almeida. Current Opinion in Biotechnology, 2002

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Improvement in the Prediction of Ventilator Weaning Outcomes by an Artificial Neural Network in a Medical ICU (2015, unknown): “20 Each layer consists of several perceptrons (or neurons), and each perceptron between layers is connected by weights that are capable of autonomous adjustments and learning directly from experience during the training phases.”
Recent Developments in Parameter Estimation and Structure Identification of Biochemical and Genomic Systems (2009, methodology): “Artificial neural networks (ANNs) have been shown to be useful in a number of applications of biochemical pathways modeling [146].”
Decoupling dynamical systems for pathway identification from metabolic profiles (2004, methodology): “General background information on ANNs, as well as on matters of implementation and stopping rules for the regression and validation steps, combined with the selection of optimal network topo l gies, were recently discussed in Almeida (2002). Because ANNs are based on logistic transfer functio s, th”
Use of Machine Learning Methods for Predicting Amount of Bioethanol Obtained from Lignocellulosic Biomass with the Use of Ionic Liquids for Pretreatment (2021, background): “ANNs have gained much attention from significant soft computing tools that are not only limited to data processing and analysis, but can also be used to solve problems in multifaceted and nonlinear processes [16].”