MOTIVATION: While it has been well established that drugs affect and help patients differently, personalized drug response predictions remain challenging. Solutions based on single omics measurements have been proposed, and networks provide means to incorporate molecular interactions into reasoning. However, how to integrate the wealth of information contained in multiple omics layers still poses a complex problem. RESULTS: We present DrDimont, Drug response prediction from Differential analysis of multi-omics networks. It allows for comparative conclusions between two conditions and translates them into differential drug response predictions. DrDimont focuses on molecular interactions. It establishes condition-specific networks from correlation within an omics layer that are then reduced and combined into heterogeneous, multi-omics molecular networks. A novel semi-local, path-based integration step ensures integrative conclusions. Differential predictions are derived from comparing the condition-specific integrated networks. DrDimont's predictions are explainable, i.e. molecular differences that are the source of high differential drug scores can be retrieved. We predict differential drug response in breast cancer using transcriptomics, proteomics, phosphosite and metabolomics measurements and contrast estrogen receptor positive and receptor negative patients. DrDimont performs better than drug prediction based on differential protein expression or PageRank when evaluating it on ground truth data from cancer cell lines. We find proteomic and phosphosite layers to carry most information for distinguishing drug response. AVAILABILITY AND IMPLEMENTATION: DrDimont is available on CRAN: https://cran.r-project.org/package=DrDimont. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The computational prediction of drug responses based on the analysis of multiple types of genome-wide molecular data is vital for accomplishing the promise of precision medicine in oncology. This will benefit cancer patients by matching their tumor characteristics to the most effective therapy available. As larger and more diverse layers of patient-related data become available, further demands for new bioinformatics approaches and expertise will arise. This article reviews key strategies, resources and techniques for the prediction of drug sensitivity in cell lines and patient-derived samples. It discusses major advances and challenges associated with the different model development steps. This review highlights major trends in this area, and will assist researchers in the assessment of recent progress and in the selection of approaches to emerging applications in oncology.

The detection of biologically meaningful clusters in protein interaction networks is crucial in systems biology. Among its applications, it can enable the identification of protein complexes. Notwithstanding significant advances, the detection of meaningful clusters faces important challenges, including the need to aid researchers in the prioritization of hundreds or even thousands of clusters. To address this need, we developed a method for the prioritization of network clusters based on the analysis of their functional homogeneity, Horn. Based on Horn scores, clustering results can be statistically ranked and attention directed toward clusters that are more likely to be biologically meaningful. We tested it on a global human protein-protein interaction network and four network clustering algorithms. Our method substantially reduced the space of potentially spurious clusters. Furthermore, we evaluated its protein complex detection capability on an independent reference dataset of protein complexes. Irrespectively of clustering approach, our approach improved protein complex identification capacity.

It has been well recognized that adverse drug reactions (ADRs) are a significant cause of morbidity and mortality. There is a growing interest in investigating biological pathways involved in cellular response to drugs. Based on examining the co-occurrence of drugs in pathway activity and ADR profiles, in this paper, we propose a new method to explore the relationship between biological pathways and ADRs at a large scale. Using sparse canonical correlation analysis of 495 drugs with two profiles for 173 pathways and 1385 ADRs, a total of 80 correlated sets of pathways and ADRs were extracted. To evaluate the performance of our method, extracted correlated components were used to retrieve known ADR profiles from drug pathway profiles using a 5-fold cross validation. A relatively high prediction performance (AUC: 0.881) was achieved. This work provides a foundation for future investigation of ADRs in the context of biological pathways under different conditions.

The identification of drug-target interactions is a crucial step in the drug-discovery process. It has been suggested that drug-target interactions are driven by drug-domain interactions. Based on the integration of two recently published datasets, i.e., Drug-target interactions in myocardial infarction (My-DTome) and drug-domain interaction network, this paper reports the association between drugs and protein domains in the context of myocardial infarction (MI). A MI drug-domain interaction network, My-DDome, was constructed. The functional similarity between domains based on their Gene Ontology (GO) annotations was estimated. The association between domains and therapeutic effects was investigated. Lists of GO annotations and Anatomical Therapeutic Chemical classification (ATC) codes highly enriched in My-DDome were identified. We show that drugs acting on blood and blood forming organs (ATC code B) and sensory organs (ATC code S) are significantly enriched in My-DDome (p <; 0.000001). Top enriched GO terms include GO:0003824 (catalytic activity), GO:0008152 (metabolic process) and GO:0030170 (pyridoxal phosphate binding). By incorporating protein domain information into My-DTome, more detailed insights into the interplay between drugs, their known targets and seemingly unrelated proteins are provided.

UNLABELLED: Cloud computing offers low cost and highly flexible opportunities in bioinformatics. Its potential has already been demonstrated in high-throughput sequence data analysis. Pathway-based or gene set analysis of expression data has received relatively less attention. We developed a gene set analysis algorithm for biomarker identification in the cloud. The resulting tool, YunBe, is ready to use on Amazon Web Services. Moreover, here we compare its performance to those obtained with desktop and computing cluster solutions. AVAILABILITY AND IMPLEMENTATION: YunBe is open-source and freely accessible within the Amazon Elastic MapReduce service at s3n://lrcv-crp-sante/app/yunbe.jar. Source code and user's guidelines can be downloaded from http://tinyurl.com/yunbedownload.

Tissue growth and regeneration are fundamental processes underpinning crucial physiological and pathological conditions: ranging from normal blood vessel network development, response to stem cells therapy and cancers. Modelling of such biological phenomena has been addressed through mathematical and algorithmic approaches. The former implements continuous representations based on differential equations. The latter exploit operational descriptions in the form of computing programs to represent and execute the models. Within this area, models that define the cell as the fundamental unit of model development, as well as discrete representations of different model entities, are important to plan in vitro experiments and to generate new testable hypotheses. This article reviews the application of algorithmic discrete models, with a focus on tissue growth and regeneration phenomena in the context of health and disease. The review begins with an overview of basic concepts, problems and approaches of computational discrete models. This will include a discussion of basic assumptions and design principles. An overview of key cell-driven approaches and examples of applications in tissue growth and regeneration is provided. The specification, implementation and analysis of a model are illustrated with a hypothetical example, which mimics the branching and sprouting patterns observed in blood vessel network development. The article concludes with a discussion of current challenges and recommendations.

MOTIVATION: The application of information encoded in molecular networks for prognostic purposes is a crucial objective of systems biomedicine. This approach has not been widely investigated in the cardiovascular research area. Within this area, the prediction of clinical outcomes after suffering a heart attack would represent a significant step forward. We developed a new quantitative prediction-based method for this prognostic problem based on the discovery of clinically relevant transcriptional association networks. This method integrates regression trees and clinical class-specific networks, and can be applied to other clinical domains. RESULTS: Before analyzing our cardiovascular disease dataset, we tested the usefulness of our approach on a benchmark dataset with control and disease patients. We also compared it to several algorithms to infer transcriptional association networks and classification models. Comparative results provided evidence of the prediction power of our approach. Next, we discovered new models for predicting good and bad outcomes after myocardial infarction. Using blood-derived gene expression data, our models reported areas under the receiver operating characteristic curve above 0.70. Our model could also outperform different techniques based on co-expressed gene modules. We also predicted processes that may represent novel therapeutic targets for heart disease, such as the synthesis of leucine and isoleucine. AVAILABILITY: The SATuRNo software is freely available at http://www.lsi.us.es/isanepo/toolsSaturno/.

In recent years there has been a growing trend towards the adoption of ontologies to support comprehensive, large-scale functional genomics research. This paper introduces seGOsa, a user-friendly cross-platform system to support large-scale assessment of Gene Ontology (GO)-driven similarity among gene products. Using information-theoretic approaches, the system exploits both topological features of the GO (i.e., between-term relationships in the hierarchy) and statistical features of the model organism databases annotated to the GO (i.e., term frequency) to assess functional similarity among gene products. Based on the assumption that the more information two terms share in common, the more similar they are, three GO-driven similarity measures (Resnik's, Lin's and Jiang's metrics) have been implemented to measure between-term similarity within each of the GO hierarchies. Meanwhile, seGOsa offers two approaches (simple and highest average similarity) to assessing the similarity between gene products based on the aggregation of between-term similarities. The program is freely available for non-profit use on request from the authors.

SUMMARY: A standard systems-based approach to biomarker and drug target discovery consists of placing putative biomarkers in the context of a network of biological interactions, followed by different 'guilt-by-association' analyses. The latter is typically done based on network structural features. Here, an alternative analysis approach in which the networks are analyzed on a 'semantic similarity' space is reported. Such information is extracted from ontology-based functional annotations. We present SimTrek, a Cytoscape plugin for ontology-based similarity assessment in biological networks. AVAILABILITY: http://rosalind.infj.ulst.ac.uk/SimTrek.html CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

There is currently no method powerful enough to identify patients at risk of developing ventricular dysfunction after myocardial infarction (MI). We aimed to identify major mechanisms related to ventricular dysfunction to predict outcome after MI. Based on the combination of domain knowledge, protein-protein interaction networks and gene expression data, a set of potential biomarkers of ventricular dysfunction after MI was identified. Here we propose a new strategy for the prediction of ventricular dysfunction after MI based on "network activity indices" (NAI), which encode gene network-based signatures and distinguishes between prognostic classes. These models outperformed prognostic models based on standard differential expression analysis. NAI-based models reported high classification accuracy, with a maximum area under the receiver operating characteristic curve (AUC) of 0.75. Furthermore, the classification capacity of these models was validated by performing evaluations on an independent patient cohort (maximum AUC=0.75). These results suggest that transcriptional network-based biosignatures can offer both powerful and biologically-meaningful prediction models of ventricular dysfunction after MI. This research reports a new integrative strategy for identifying transcriptional responses that characterize cardiac repair and for predicting clinical outcome after MI. It can be adapted to other clinical domains, such as those constrained by small molecular datasets and limited translational knowledge. Furthermore, it may reflect clinically-meaningful synergistic effects that cannot be identified by standard analyses.

Recent years have seen a growing interest in the incorporation of protein-protein interaction (PPI) networks to support functional genomic research. Often a default depth is assumed by network inference software. This case study considers the impact of network depth on the analysis of PPI networks using seven proteins known to be relevant to heart failure as inputs into the analysis. This paper analyses how the characteristics of a PPI network vary according to the level examined, suggesting that the investigation of network topology is an essential first step in PPI analysis. The classification of nodes, in terms of degree and betweenness centrality, within the network is also considered. The effect of network depth is also proved to be significant in the identification of potentially essential proteins with large connectivity and/or high betweenness centrality values.

Computational biology is essential in the process of translating biological knowledge into clinical practice, as well as in the understanding of biological phenomena based on the resources and technologies originating from the clinical environment. One such key contribution of computational biology is the discovery of biomarkers for predicting clinical outcomes using 'omic' information. This process involves the predictive modelling and integration of different types of data and knowledge for screening, diagnostic or prognostic purposes. Moreover, this requires the design and combination of different methodologies based on statistical analysis and machine learning. This article introduces key computational approaches and applications to biomarker discovery based on different types of 'omic' data. Although we emphasize applications in cardiovascular research, the computational requirements and advances discussed here are also relevant to other domains. We will start by introducing some of the contributions of computational biology to translational research, followed by an overview of methods and technologies used for the identification of biomarkers with predictive or classification value. The main types of 'omic' approaches to biomarker discovery will be presented with specific examples from cardiovascular research. This will include a review of computational methodologies for single-source and integrative data applications. Major computational methods for model evaluation will be described together with recommendations for reporting models and results. We will present recent advances in cardiovascular biomarker discovery based on the combination of gene expression and functional network analyses. The review will conclude with a discussion of key challenges for computational biology, including perspectives from the biosciences and clinical areas.

This paper reports on the evaluation of different machine learning techniques for the automated classification of coding gene sequences obtained from several organisms in terms of their functional role as adhesins. Diverse, biologically-meaningful, sequence-based features were extracted from the sequences and used as inputs to the in silico prediction models. Another contribution of this work is the generation of potentially novel and testable predictions about the surface protein DGF-1 family in Trypanosoma cruzi. Finally, these techniques are potentially useful for the automated annotation of known adhesin-like proteins from the trans-sialidase surface protein family in T. cruzi, the etiological agent of Chagas disease.

This paper investigates the integration of functional genomic data for the prediction of protein-protein interactions (PPI) in Saccharomyces cerevisiae. A previous benchmark study observed a marginal increase in predictive power when integrating diverse features. Classification performance was evaluated using the Receiver Operating Characteristic (ROC) curve. In this study we propose the implementation of a likelihood ratio based Bayesian classifier to reassess the limits of genomic integration. The classifier combines seven genomic features ranging from co-expression to essentiality. Due to the imbalance of the dataset in this study, ROC curves may present an overly optimistic view of the classification performance. We use the true positive/false positive (TP/FP) rate and sensitivity as comparative predictive measures to the ROC curve. Predicted interactions are verified using a Gold Standard constructed from the Munich Database of Interacting Proteins Complex Catalogue. Using the measures TP/FP and sensitivity, a clear increase in classification performance was observed with the integration of features. This framework could be extended to the analysis of PPI in more complex organisms such as Drosophila melanogaster and Homo sapiens.

Serial analysis of gene expression (SAGE) allows a detailed, simultaneous analysis of thousands of genes without the need for prior, complete gene sequence information. However, due to its inherent complexity and the lack of complete structural and function knowledge, mining vast collections of SAGE data to extract useful knowledge poses great challenges to traditional analytical techniques. Moreover, SAGE data are characterized by a specific statistical model that has not been incorporated into traditional data analysis techniques. The analysis of SAGE data requires advanced, intelligent computational techniques, which consider the underlying biology and the statistical nature of SAGE data. By addressing the statistical properties demonstrated by SAGE data, this paper presents a new self-adaptive neural network, Poisson-based growing self-organizing map (PGSOM), which implements novel weight adaptation and neuron growing strategies. An empirical study of key dynamic mechanisms of PGSOM is presented. It was tested on three datasets, including synthetic and experimental SAGE data. The results indicate that, in comparison to traditional techniques, the PGSOM offers significant advantages in the context of pattern discovery and visualization in SAGE data. The pattern discovery and visualization platform discussed in this paper can be applied to other problem domains where the data are better approximated by a Poisson distribution.

The three papers in this special section focus on machine intelligence approaches to systems biology.

This paper evaluates three classification techniques: Naive Bayesian (NB), multilayer perceptron (MLP) and K-nearest neighbour (KNN) that integrate diverse, large-scale functional data to infer pairwise (PW) and module-based (MB) interaction networks in Saccharomyces cerevisiae. Existing multi-source functional data from S. cerevisiae were merged and transformed to construct MB datasets. The results indicate that selection of a classifier depends upon the specific PPI classification problem. Feature integration and encoding methods proposed significantly impact the predictive performance of the classifiers. Generation of PPI maps for S. cerevisiae and beyond will be improved with new, high-quality, large-scale datasets with increased interactome coverage and the integration of classification methods.

There is a need to design computational methods to support the prediction of gene regulatory networks. Such models should offer both biologically-meaningful and computationally-accurate predictions, which in combination with other techniques may improve large-scale, integrative studies. This paper presents a new machine learning method for the prediction of putative regulatory associations from expression data, which exhibit properties never or only partially addressed by other techniques recently published. The method was tested on a Saccharomyces cerevisiae gene expression dataset. The results were statistically validated and compared with the relationships inferred by two machine learning approaches to gene regulatory network prediction. Furthermore, the resulting predictions were assessed using domain knowledge. The proposed algorithm may be able to accurately predict relevant biological associations between genes. One of the most relevant features of this new method is the prediction of adaptive regulation thresholds for the discretization of gene expression values, which is required prior to the rule association learning process. Moreover, an important advantage consists of its low computational cost to infer association rules. The proposed system may significantly support exploratory, large-scale studies of automated identification of potentially-relevant gene expression associations.

Serial analysis of gene expression (SAGE) is a powerful technique for global gene expression profiling, allowing simultaneous analysis of thousands of transcripts without prior structural and functional knowledge. Pattern discovery and visualization have become fundamental approaches to analyzing such large-scale gene expression data. From the pattern discovery perspective, clustering techniques have received great attention. However, due to the statistical nature of SAGE data (i.e., underlying distribution), traditional clustering techniques may not be suitable for SAGE data analysis. Based on the adaptation and improvement of Self-Organizing Maps and hierarchical clustering techniques, this paper presents two new clustering algorithms, namely, PoissonS and PoissonHC, for SAGE data analysis. Tested on synthetic and experimental SAGE data, these algorithms demonstrate several advantages over traditional pattern discovery techniques. The results indicate that, by incorporating statistical properties of SAGE data, PoissonS and PoissonHC, as well as a hybrid approach (neuro-hierarchical approach) based on the combination of PoissonS and PoissonHC, offer significant improvements in pattern discovery and visualization for SAGE data. Moreover, a user-friendly platform, which may improve and accelerate SAGE data mining, was implemented. The system is freely available on request from the authors for nonprofit use.

A supervised machine learning approach that incorporates genetic algorithms (GA) and weighted k-nearest neighbours (WkNN) was applied to classify type 2 diabetes mellitus (T2DM) patients according to the presence or absence of coronary heart disease (CHD) complications. The investigation was carried out by analyzing potential risk factors recorded at the Ulster Hospital in Northern Ireland. A GA initialization technique that integrates medical expert knowledge was compared with traditional data-driven GA initialization techniques. The results indicate that the incorporation of expert knowledge provides only a small improvement of CHD classification performance compared with models based on data-driven initialization techniques. This may be due to data incompleteness and noise or due to the beneficial effects of treatment, which masks the complication of CHD in the dataset. Further incorporation of expert knowledge at different levels of the GA need to be addressed to improve decision support in this domain

Clustering methods aim to organize a collection of cases into groupings, such that cases within one cluster are more similar to each other than to those in other clusters. A small amount of background knowledge may also be used to guide the clustering process and aid in the interpretation of results. This type of knowledge-driven clustering is known as semi-supervised clustering. This knowledge may be represented by pairwise constraints, labelled cases or known data groupings. Pairwise constraints may be specified, for example, as `MustLink' or `CannotLink' associations between cases. This research proposes a semi-supervised clustering method that exploits pairwise constraints and similarity information extracted from constrained cases. This semi-supervised clustering algorithm was first evaluated on publicly-available biomedical datasets. It was then applied to a Type II diabetes dataset to assess coronary heart disease (CHD) complication. This dataset comprises laboratory and physiological information from diabetic patients at the Ulster Hospital (UH) in Northern Ireland. The following methods were compared: traditional k-means, constraint-based k-means with pairwise constraints (CK method) and similarity-driven constraint-based k-means (SCK method). Results showed that the predictive quality, i.e. detection of relevant partitions and significant clusters, on these datasets was improved with a small amount of supervision (i.e. pairwise constraints automatically generated from the predefined class labels). Furthermore, the results from the UH dataset suggest significant associations between clustering outcomes with CHD complication in Type II diabetes patients

Retinal photoreceptor cells are responsible for light detection and phototransduction. The understanding of molecular mechanisms regulating photoreceptor gene expression during retinal development may have important implications in clinical neuroscience. Using self-adaptive neural networks and pattern validation statistical tools, this paper explores large-scale analysis of photoreceptor gene expression. Based on the analysis of data generated by serial analysis of gene expression (SAGE) in the developing mouse retina, significant expression patterns for the in silico detection of photoreceptor-enriched genes were revealed. This study demonstrates how machine learning and statistical techniques may be effectively combined to detect key complex relationships encoded in SAGE data. Such approaches may support inexpensive functional predictions prior to the application of experimental methodologies.

BACKGROUND: Retinal photoreceptors are highly specialised cells, which detect light and are central to mammalian vision. Many retinal diseases occur as a result of inherited dysfunction of the rod and cone photoreceptor cells. Development and maintenance of photoreceptors requires appropriate regulation of the many genes specifically or highly expressed in these cells. Over the last decades, different experimental approaches have been developed to identify photoreceptor enriched genes. Recent progress in RNA analysis technology has generated large amounts of gene expression data relevant to retinal development. This paper assesses a machine learning methodology for supporting the identification of photoreceptor enriched genes based on expression data. RESULTS: Based on the analysis of publicly-available gene expression data from the developing mouse retina generated by serial analysis of gene expression (SAGE), this paper presents a predictive methodology comprising several in silico models for detecting key complex features and relationships encoded in the data, which may be useful to distinguish genes in terms of their functional roles. In order to understand temporal patterns of photoreceptor gene expression during retinal development, a two-way cluster analysis was firstly performed. By clustering SAGE libraries, a hierarchical tree reflecting relationships between developmental stages was obtained. By clustering SAGE tags, a more comprehensive expression profile for photoreceptor cells was revealed. To demonstrate the usefulness of machine learning-based models in predicting functional associations from the SAGE data, three supervised classification models were compared. The results indicated that a relatively simple instance-based model (KStar model) performed significantly better than relatively more complex algorithms, e.g. neural networks. To deal with the problem of functional class imbalance occurring in the dataset, two data re-sampling techniques were studied. A random over-sampling method supported the implementation of the most powerful prediction models. The KStar model was also able to achieve higher predictive sensitivities and specificities using random over-sampling techniques. CONCLUSION: The approaches assessed in this paper represent an efficient and relatively inexpensive in silico methodology for supporting large-scale analysis of photoreceptor gene expression by SAGE. They may be applied as complementary methodologies to support functional predictions before implementing more comprehensive, experimental prediction and validation methods. They may also be combined with other large-scale, data-driven methods to facilitate the inference of transcriptional regulatory networks in the developing retina. Furthermore, the methodology assessed may be applied to other data domains.

Type II diabetes is one of the most common causes of disability and death in the United Kingdom. This investigation analysed data acquired from diabetic patients at the Ulster Hospital in Northern Ireland in terms of statistical descriptive indicators and missing values. Such data are noisy and incomplete. This paper reports a comprehensive missing data estimation analysis. Five missing value imputation methods were compared, including k-Nearest Neighbours (k-NN) and correlation-based estimation models. From this analysis it can be concluded that a feature-based correlation method known as EMImpute/spl I.bar/Columns is a promising approach to estimating missing values. Nevertheless, k-NN methods may be useful to provide relatively accurate estimations with lower error variability. These estimation techniques will support the implementation of supervised and unsupervised learning tools for coronary heart disease risk assessment, a major complication of diabetes.

The gene ontology (GO) is an important knowledge resource for biologists and bioinformaticians. This paper explores the integration of similarity information derived from GO into clustering-based gene expression analysis. A system that integrates GO annotations, similarity patterns and expression data in yeast is assessed. In comparison with a clustering model based only on expression data correlation, the proposed framework not only produces consistent results, but also it offers alternative, potentially meaningful views of the biological problem under study. Moreover, it provides the basis for developing other automated, knowledge-driven data mining systems in this and related application areas.

UNLABELLED: An integrative classification system for functional genomics is introduced. A comparison with a previous study of the yeast mitochondrial proteome is presented. AVAILABILITY: A demonstration prototype, interSearch, is available on request. SUPPLEMENTARY INFORMATION: http://ijsr32.infj.ulster.ac.uk/~e10110731/interSearch.

UNLABELLED: In this paper we present a data mining system, which allows the application of different clustering and cluster validity algorithms for DNA microarray data. This tool may improve the quality of the data analysis results, and may support the prediction of the number of relevant clusters in the microarray datasets. This systematic evaluation approach may significantly aid genome expression analyses for knowledge discovery applications. The developed software system may be effectively used for clustering and validating not only DNA microarray expression analysis applications but also other biomedical and physical data with no limitations. AVAILABILITY: The program is freely available for non-profit use on request at http://www.cs.tcd.ie/Nadia.Bolshakova/Machaon.html CONTACT: [email protected].

Summary: This paper presents an approach to assessing cluster validity based on similarity knowledge extracted from the Gene Ontology. Availability: The program is freely available for non-profit use on request from the authors. Contact: [email protected] Supplementary information: http://www.cs.tcd.ie/Nadia.Bolshakova/GOtool.html

BACKGROUND: Several supervised and unsupervised learning tools are available to classify functional genomics data. However, relatively less attention has been given to exploratory, visualisation-driven approaches. Such approaches should satisfy the following factors: Support for intuitive cluster visualisation, user-friendly and robust application, computational efficiency and generation of biologically meaningful outcomes. This research assesses a relaxation method for non-linear mapping that addresses these concerns. Its applications to gene expression and protein-protein interaction data analyses are investigated. RESULTS: Publicly available expression data originating from leukaemia, round blue-cell tumours and Parkinson disease studies were analysed. The method distinguished relevant clusters and critical analysis areas. The system does not require assumptions about the inherent class structure of the data, its mapping process is controlled by only one parameter and the resulting transformations offer intuitive, meaningful visual displays. Comparisons with traditional mapping models are presented. As a way of promoting potential, alternative applications of the methodology presented, an example of exploratory data analysis of interactome networks is illustrated. Data from the C. elegans interactome were analysed. Results suggest that this method might represent an effective solution for detecting key network hubs and for clustering biologically meaningful groups of proteins. CONCLUSION: A relaxation method for non-linear mapping provided the basis for visualisation-driven analyses using different types of data. This study indicates that such a system may represent a user-friendly and robust approach to exploratory data analysis. It may allow users to gain better insights into the underlying data structure, detect potential outliers and assess assumptions about the cluster composition of the data.

This research explores the feasibility of semantic similarity approaches to supporting predictive tasks in functional genomics. It aims to establish potential relationships between ontology-based similarity of gene products and important functional properties, such as gene expression correlation. Similarity measures based on the information content of the Gene Ontology (GO) were analyzed. Models have been implemented using data obtained from well-known studies in S. cerevisiae. Results suggest that there may exist significant relationships between gene expression correlation and semantic similarity. Analyses of protein complex data show that, in general, there is a significant correlation between the semantic similarity exhibited by a pair of genes and the probability of finding them in the same complex. These results can also be interpreted as an assessment of the quality and consistency of the information represented in the GO.

Genome Database were analyzed to calculate functional similarity of gene products. Three methods for measuring similarity (including a distance-based approach) were implemented. Significant, quantitative relationships between similarity and expression correlation of pairs of genes were detected. Using a known gene expression dataset in yeast, this study compared more than three million pairs of gene products on the basis of these functional properties. Highly correlated genes exhibit strong similarity based on information originating from the gene ontology taxonomies. Such a similarity is significantly stronger than that observed between weakly correlated genes. This study supports the feasibility of applying gene ontology-driven similarity methods to functional prediction tasks, such as the validation of gene expression analyses and the identification of false positives in protein interaction studies.

Recent progress in medical sciences has led to an explosive growth of data. Due to its inherent complexity and diversity, mining such volumes of data to extract relevant knowledge represents an enormous challenge and opportunity. Interactive pattern discovery and visualization systems for biomedical data mining have received relatively little attention. Emphasis has been traditionally placed on automation and supervised classification problems. Based on self-adaptive neural networks and pattern-validation statistical tools, this paper presents a user-friendly platform to support biomedical pattern discovery and visualization. It has been tested on several types of biomedical data, such as dermatology and cardiology data sets. The results indicate that in comparison to traditional techniques, such as Kohonen Maps, this platform may significantly improve the effectiveness and efficiency of pattern discovery and classification tasks, including problems described by several classes. Furthermore, this study shows how the combination of graphical and statistical tools may make these patterns more meaningful.

This is a reply to the comment by H.-C. Wu and C.-T. Sun on the paper "A Computational Evolutionary Approach to Evolving Game Strategies and Cooperation." Key design problems, limitations and potential applications are discussed.

This article focuses on clustering techniques for the analysis of microarray data and discusses contributions and applications for the implementation of intelligent diagnostic systems and therapy design studies. Approaches to validating and visualising expression clustering results and software and other relevant resources to support clustering-based analyses are reviewed. Finally, this paper addresses current limitations and problems that need to be investigated for the development of an advanced generation of pattern discovery tools.

UNLABELLED: This paper presents a cluster validation tool for gene expression data. Machaon CVE (Clustering and Validation Environment) system aims to partition samples or genes into groups characterized by similar expression patterns, and to evaluate the quality of the clusters obtained. AVAILABILITY: The program is freely available for non-profit use on request at http://www.cs.tcd.ie/Nadia.Bolshakova/Machaon.html SUPPLEMENTARY INFORMATION: http://www.cs.tcd.ie/Nadia.Bolshakova/Machaon.html

BACKGROUND: Supervised classification is fundamental in bioinformatics. Machine learning models, such as neural networks, have been applied to discover genes and expression patterns. This process is achieved by implementing training and test phases. In the training phase, a set of cases and their respective labels are used to build a classifier. During testing, the classifier is used to predict new cases. One approach to assessing its predictive quality is to estimate its accuracy during the test phase. Key limitations appear when dealing with small-data samples. This paper investigates the effect of data sampling techniques on the assessment of neural network classifiers. RESULTS: Three data sampling techniques were studied: Cross-validation, leave-one-out, and bootstrap. These methods are designed to reduce the bias and variance of small-sample estimations. Two prediction problems based on small-sample sets were considered: Classification of microarray data originating from a leukemia study and from small, round blue-cell tumours. A third problem, the prediction of splice-junctions, was analysed to perform comparisons. Different accuracy estimations were produced for each problem. The variations are accentuated in the small-data samples. The quality of the estimates depends on the number of train-test experiments and the amount of data used for training the networks. CONCLUSION: The predictive quality assessment of biomolecular data classifiers depends on the data size, sampling techniques and the number of train-test experiments. Conservative and optimistic accuracy estimations can be obtained by applying different methods. Guidelines are suggested to select a sampling technique according to the complexity of the prediction problem under consideration.

This paper describes an approach to the co-evolution of competing virtual creatures. Pairs of individuals enter one-on-one contests in which they contend to establish contact with a common resource. The individuals are subjected to competitive fitness functions. In each tournament one type of organism implements a game rule based on a set of basic cognitive capabilities. The second type of contestant genetically determines its game strategy. Interesting strategy patterns are identified when this evolutionary process is simulated on populations of competing individuals. These experiments show how cooperation emerges in order to improve both individual and collective game performances.

Abstract Summary: This paper presents a method for the assessment of expression cluster validity. Availability: Executable programs are available on request from the author. Contact: [email protected] Supplementary information: http://www.cs.tcd.ie/Francisco.Azuaje/Cval.html

Artificial life simulations represent an opportunity to study adaptation phenomena observed in living organisms. One such phenomenon is reverse evolution, which has been observed in microbes and some sexual populations. This paper shows how populations of digital organisms achieve reverse evolution using a genetic algorithm approach. Patterns of reverse evolution analysed among a number of phenotypic characters provide an insight into the complexity of this evolutionary process. Similarly, this type of approach may assist the modelling of evolutionary processes, whose outcomes would require many years to be tested in a natural environment.

Despite more than 30 years of experimental research, there have been no generic models to classify tumours and identify new types of cancer. Similarly, advances in the molecular classification of tumours may play a central role in cancer treatment. In this paper, a new approach to genome expression pattern interpretation is described and applied to the recognition of B-cell malignancies as a test set. Using DNA microarray data generated by A.A. Alizadeh et al. (2000), a neural network model known as a simplified fuzzy ARTMAP is able to identify normal and diffuse large B-cell lymphoma (DLBCL) patients. Furthermore, it discovers the distinction between patients with molecularly distinct forms of DLBCL without previous knowledge of those subtypes.

Both information retrieval and case-based reasoning systems rely on effective and efficient selection of relevant data. Typically, relevance in such systems is approximated by similarity or indexing models. However, the definition of what makes data items similar or how they should be indexed is often nontrivial and time-consuming. Based on growing cell structure artificial neural networks, this paper presents a method that automatically constructs a case retrieval model from existing data. Within the case-based reasoning (CBR) framework, the method is evaluated for two medical prognosis tasks, namely, colorectal cancer survival and coronary heart disease risk prognosis. The results of the experiments suggest that the proposed method is effective and robust. To gain a deeper insight and understanding of the underlying mechanisms of the proposed model, a detailed empirical analysis of the models structural and behavioral properties is also provided.