Artificial Neural Networks (ANNs) are being used increasingly to predict and forecast water resources variables. In this paper, the steps that should be followed in the development of such models are outlined. These include the choice of performance criteria, the division and pre-processing of the available data, the determination of appropriate model inputs and network architecture, optimisation of the connection weights (training) and model validation. The options available to modellers at each of these steps are discussed and the issues that should be considered are highlighted. A review of 43 papers dealing with the use of neural network models for the prediction and forecasting of water resources variables is undertaken in terms of the modelling process adopted. In all but two of the papers reviewed, feedforward networks are used. The vast majority of these networks are trained using the backpropagation algorithm. Issues in relation to the optimal division of the available data, data pre-processing and the choice of appropriate model inputs are seldom considered. In addition, the process of choosing appropriate stopping criteria and optimising network geometry and internal network parameters is generally described poorly or carried out inadequately. All of the above factors can result in non-optimal model performance and an inability to draw meaningful comparisons between different models. Future research efforts should be directed towards the development of guidelines which assist with the development of ANN models and the choice of when ANNs should be used in preference to alternative approaches, the assessment of methods for extracting the knowledge that is contained in the connection weights of trained ANNs and the incorporation of uncertainty into ANN models.

Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications

This paper emphasizes the importance of rigorous validation as a crucial, integral component of Quantitative Structure Property Relationship (QSPR) model development. We consider some examples of published QSPR models, which in spite of their high fitted accuracy for the training sets and apparent mechanistic appeal, fail rigorous validation tests, and, thus, may lack practical utility as reliable screening tools. We present a set of simple guidelines for developing validated and predictive QSPR models. To this end, we discuss several validation strategies including (1) randomization of the modelled property, also called Y-scrambling, (2) multiple leave-many-out cross-validations, and (3) external validation using rational division of a dataset into training and test sets. We also highlight the need to establish the domain of model applicability in the chemical space to flag molecules for which predictions may be unreliable, and discuss some algorithms that can be used for this purpose. We advocate the broad use of these guidelines in the development of predictive QSPR models.

The Importance of Being Earnest: Validation is the Absolute Essential for Successful Application and Interpretation of QSPR Models

This article reviews some of the recent advances on FTIR spectroscopy in areas related to natural tissues and cell biology. It is the second review publication resulting from a detailed study on the applications of spectroscopic methods in biological studies and summarizes some of the most widely used peak frequencies and their assignments. The aim of these studies is to prepare a database of molecular fingerprints, which will help researchers in defining the chemical structure of the biological tissues introducing most of the important peaks present in the natural tissues. In spite of applying different methods, there seems to be a considerable similarity in defining the peaks of identical areas of the FTIR spectra. As a result, it is believed that preparing a unique collection of the frequencies encountered in FTIR spectroscopic studies can lead to significant improvements both in the quantity and quality of research and their outcomes. This article is the first review of its kind that provides...

Fourier Transform Infrared (FTIR) Spectroscopy of Biological Tissues

The characteristics of the OPLS method have been investigated for the purpose of discriminant analysis (OPLS-DA). We demonstrate how class-orthogonal variation can be exploited to augment classific ...

OPLS discriminant analysis: combining the strengths of PLS-DA and SIMCA classification†

This article provides an overview of methods for reliability assessment of quantitative structure-activity relationship (QSAR) models in the context of regulatory acceptance of human health and environmental QSARs. Useful diagnostic tools and data analytical approaches are highlighted and exemplified. Particular emphasis is given to the question of how to define the applicability borders of a QSAR and how to estimate parameter and prediction uncertainty. The article ends with a discussion regarding QSAR acceptability criteria. This discussion contains a list of recommended acceptability criteria, and we give reference values for important QSAR performance statistics. Finally, we emphasize that rigorous and independent validation of QSARs is an essential step toward their regulatory acceptance and implementation.

Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs.

A comparison is made of a number of computationally efficient molecular indices with a view to the screening of very large virtual data sets of molecules. The use of Bayesian regularized neural networks is discussed, and their virtue in eliminating the need for validation sets, and potentially even test sets, is emphasized. The concept of a virtual receptor is introduced, and this is illustrated by the results of screening a database of 40 000 molecules.

New QSAR Methods Applied to Structure—Activity Mapping and Combinatorial Chemistry.

The differentiation pathway of the nematode worm model organism C. elegans has been studied as a surrogate for future work on the human embryonic stem cell genetic networks. We extend earlier work on recursive networks by the introduction of a regularizer and more robust convergence algorithms, and by training the model to recapitulate experimental gene expression patterns rather than random expression patterns. We also assess the ability of the model to predict the expression profile on the next cell(s) in the lineage. The weight matrix from the model may be interpreted as a set of rules that guides the differentiation of the cells via a set of regulatory factors: internal genes or external entities. The activity of the regulatory factors shows patterns across the differentiation pathway that reflect the left-or right-hand split. Using these patterns, it may be possible to identify the actual factors responsible for the differentiation and to interpret the associated weights. The model was able to predict expression profiles of cells not used in training the model with a relatively low error rate.

Predictive mesoscale network model of cell fate decisions during c. elegans embryogenesis

Quadrupole hyperfine structure of the microwave spectrum of 1,2,4-triazole and N-deuterotriazole

The development of robust and predictive QSAR models is highly dependent on the use of molecular descriptors that contain information relevant to the property being modelled. Selection of these relevant features from a large pool of possibilities is difficult to achieve effectively. Modern Bayesian methods provide substantial advantages over conventional feature selection methods for feature selection and QSAR modelling. We illustrate the importance of descriptor choice and the beneficial properties of Bayesian methods to select context-dependent relevant descriptors and build robust QSAR models, using data on anaesthetics. Our results show the effectiveness of Bayesian feature selection methods in choosing the best descriptors when these are mixed with less informative descriptors. They also demonstrate the efficacy of the Abraham descriptors and identify deficiencies in ParaSurf descriptors for modelling anaesthetic action.

Modelling Inhalational Anaesthetics Using Bayesian Feature Selection and QSAR Modelling Methods

Complex systems science is making substantial contributions to the study of biological systems, and has made a substantial contribution to the new field of systems biology. Systems biology focuses on the systematic study of complex interactions in biological systems using an integrative rather than reductionist perspective. One of the goals of systems biology is to study, model, and understand new emergent properties of biological systems from a complex systems perspective [1, 2]. This integrative approach to biology is generating substantial benefits in facilitating study of larger more complicated systems, providing improved understanding of nonlinear system properties, and provides an ability to model systems at appropriate levels of detail where the model is matched to data density and research questions. Various aspects of systems biology have been reviewed recently [3 – 11]. Chemistry has lagged behind most other disciplines in adopting complex systems approaches, possibly because it has largely been a reductionist science, and reductionist approaches have been very successful. Adopting a complementary complex systems approach to chemistry will build on this success to study more complex matter.

Frank R. Burden

Papers

New QSAR Methods Applied to Structure—Activity Mapping and Combinatorial Chemistry.

Predictive mesoscale network model of cell fate decisions during c. elegans embryogenesis

Quadrupole hyperfine structure of the microwave spectrum of 1,2,4-triazole and N-deuterotriazole

Modelling Inhalational Anaesthetics Using Bayesian Feature Selection and QSAR Modelling Methods

Modelling for regenerative medicine: systems biology meets systems chemistry