Asymmetric bagging and feature selection for activities prediction of drug molecules

<p>Abstract</p> <p>Background</p> <p>Activities of drug molecules can be predicted by QSAR (quantitative structure activity relationship) models, which overcomes the disadvantages of high cost and long cycle by employing the traditional experimental method. With the fact that the number of drug molecules with positive activity is rather fewer than that of negatives, it is important to predict molecular activities considering such an unbalanced situation.</p> <p>Results</p> <p>Here, asymmetric bagging and feature selection are introduced into the problem and asymmetric bagging of support vector machines (asBagging) is proposed on predicting drug activities to treat the unbalanced problem. At the same time, the features extracted from the structures of drug molecules affect prediction accuracy of QSAR models. Therefore, a novel algorithm named PRIFEAB is proposed, which applies an embedded feature selection method to remove redundant and irrelevant features for asBagging. Numerical experimental results on a data set of molecular activities show that asBagging improve the AUC and sensitivity values of molecular activities and PRIFEAB with feature selection further helps to improve the prediction ability.</p> <p>Conclusion</p> <p>Asymmetric bagging can help to improve prediction accuracy of activities of drug molecules, which can be furthermore improved by performing feature selection to select relevant features from the drug molecules data sets.</p

Coupling different methods for overcoming the class imbalance problem

Many classification problems must deal with imbalanced datasets where one class \u2013 the majority class \u2013 outnumbers the other classes. Standard classification methods do not provide accurate predictions in this setting since classification is generally biased towards the majority class. The minority classes are oftentimes the ones of interest (e.g., when they are associated with pathological conditions in patients), so methods for handling imbalanced datasets are critical. Using several different datasets, this paper evaluates the performance of state-of-the-art classification methods for handling the imbalance problem in both binary and multi-class datasets. Different strategies are considered, including the one-class and dimension reduction approaches, as well as their fusions. Moreover, some ensembles of classifiers are tested, in addition to stand-alone classifiers, to assess the effectiveness of ensembles in the presence of imbalance. Finally, a novel ensemble of ensembles is designed specifically to tackle the problem of class imbalance: the proposed ensemble does not need to be tuned separately for each dataset and outperforms all the other tested approaches. To validate our classifiers we resort to the KEEL-dataset repository, whose data partitions (training/test) are publicly available and have already been used in the open literature: as a consequence, it is possible to report a fair comparison among different approaches in the literature. Our best approach (MATLAB code and datasets not easily accessible elsewhere) will be available at https://www.dei.unipd.it/node/2357

Doctor of Philosophy

dissertationNanoinformatics is a relatively young field of study that is important due to its implications in the field of nanomedicine, specifically toward the development of nanoparticle drug delivery systems. As more structural, biochemical, and physiochemical data become available regarding nanoparticles, the greater the knowledge-gain from using nanoinformatics methods will become. While there are challenges that exist with nanoparticle data, including heterogeneity of data and complexity of the particles, nanoinformatics will be at the forefront of processing these data and aid in the design of nanoparticles for biomedical applications. In this dissertation, a review of data mining and machine learning studies performed in the field of nanomedicine is presented. Next, the use of natural language processing methods to extract numeric values of biomedical property terms of poly(amido amine) (PAMAM) dendrimers from nanomedicine literature is demonstrated, along with successful extraction results. Following this is an implementation and its results of data mining techniques used for the development of predictive models of cytotoxicity of PAMAM dendrimers using their chemical and structural properties. Finally, a method and its results for using molecular dynamics simulations to test the ability of EDTA, as a gold standard, and generation 3.5 (G3.5) PAMAM dendrimers to chelate calcium

Class prediction for high-dimensional class-imbalanced data

<p>Abstract</p> <p>Background</p> <p>The goal of class prediction studies is to develop rules to accurately predict the class membership of new samples. The rules are derived using the values of the variables available for each subject: the main characteristic of high-dimensional data is that the number of variables greatly exceeds the number of samples. Frequently the classifiers are developed using class-imbalanced data, i.e., data sets where the number of samples in each class is not equal. Standard classification methods used on class-imbalanced data often produce classifiers that do not accurately predict the minority class; the prediction is biased towards the majority class. In this paper we investigate if the high-dimensionality poses additional challenges when dealing with class-imbalanced prediction. We evaluate the performance of six types of classifiers on class-imbalanced data, using simulated data and a publicly available data set from a breast cancer gene-expression microarray study. We also investigate the effectiveness of some strategies that are available to overcome the effect of class imbalance.</p> <p>Results</p> <p>Our results show that the evaluated classifiers are highly sensitive to class imbalance and that variable selection introduces an additional bias towards classification into the majority class. Most new samples are assigned to the majority class from the training set, unless the difference between the classes is very large. As a consequence, the class-specific predictive accuracies differ considerably. When the class imbalance is not too severe, down-sizing and asymmetric bagging embedding variable selection work well, while over-sampling does not. Variable normalization can further worsen the performance of the classifiers.</p> <p>Conclusions</p> <p>Our results show that matching the prevalence of the classes in training and test set does not guarantee good performance of classifiers and that the problems related to classification with class-imbalanced data are exacerbated when dealing with high-dimensional data. Researchers using class-imbalanced data should be careful in assessing the predictive accuracy of the classifiers and, unless the class imbalance is mild, they should always use an appropriate method for dealing with the class imbalance problem.</p

Diversified Ensemble Classifiers for Highly Imbalanced Data Learning and their Application in Bioinformatics

In this dissertation, the problem of learning from highly imbalanced data is studied. Imbalance data learning is of great importance and challenge in many real applications. Dealing with a minority class normally needs new concepts, observations and solutions in order to fully understand the underlying complicated models. We try to systematically review and solve this special learning task in this dissertation.We propose a new ensemble learning framework—Diversified Ensemble Classifiers for Imbal-anced Data Learning (DECIDL), based on the advantages of existing ensemble imbalanced learning strategies. Our framework combines three learning techniques: a) ensemble learning, b) artificial example generation, and c) diversity construction by reversely data re-labeling. As a meta-learner, DECIDL utilizes general supervised learning algorithms as base learners to build an ensemble committee. We create a standard benchmark data pool, which contains 30 highly skewed sets with diverse characteristics from different domains, in order to facilitate future research on imbalance data learning. We use this benchmark pool to evaluate and compare our DECIDL framework with several ensemble learning methods, namely under-bagging, over-bagging, SMOTE-bagging, and AdaBoost. Extensive experiments suggest that our DECIDL framework is comparable with other methods. The data sets, experiments and results provide a valuable knowledge base for future research on imbalance learning. We develop a simple but effective artificial example generation method for data balancing. Two new methods DBEG-ensemble and DECIDL-DBEG are then designed to improve the power of imbalance learning. Experiments show that these two methods are comparable to the state-of-the-art methods, e.g., GSVM-RU and SMOTE-bagging. Furthermore, we investigate learning on imbalanced data from a new angle—active learning. By combining active learning with the DECIDL framework, we show that the newly designed Active-DECIDL method is very effective for imbalance learning, suggesting the DECIDL framework is very robust and flexible.Lastly, we apply the proposed learning methods to a real-world bioinformatics problem—protein methylation prediction. Extensive computational results show that the DECIDL method does perform very well for the imbalanced data mining task. Importantly, the experimental results have confirmed our new contributions on this particular data learning problem

Microarray Data Mining and Gene Regulatory Network Analysis

The novel molecular biological technology, microarray, makes it feasible to obtain quantitative measurements of expression of thousands of genes present in a biological sample simultaneously. Genome-wide expression data generated from this technology are promising to uncover the implicit, previously unknown biological knowledge. In this study, several problems about microarray data mining techniques were investigated, including feature(gene) selection, classifier genes identification, generation of reference genetic interaction network for non-model organisms and gene regulatory network reconstruction using time-series gene expression data. The limitations of most of the existing computational models employed to infer gene regulatory network lie in that they either suffer from low accuracy or computational complexity. To overcome such limitations, the following strategies were proposed to integrate bioinformatics data mining techniques with existing GRN inference algorithms, which enables the discovery of novel biological knowledge. An integrated statistical and machine learning (ISML) pipeline was developed for feature selection and classifier genes identification to solve the challenges of the curse of dimensionality problem as well as the huge search space. Using the selected classifier genes as seeds, a scale-up technique is applied to search through major databases of genetic interaction networks, metabolic pathways, etc. By curating relevant genes and blasting genomic sequences of non-model organisms against well-studied genetic model organisms, a reference gene regulatory network for less-studied organisms was built and used both as prior knowledge and model validation for GRN reconstructions. Networks of gene interactions were inferred using a Dynamic Bayesian Network (DBN) approach and were analyzed for elucidating the dynamics caused by perturbations. Our proposed pipelines were applied to investigate molecular mechanisms for chemical-induced reversible neurotoxicity

Development and application of distributed computing tools for virtual screening of large compound libraries

Im derzeitigen Drug Discovery Prozess ist die Identifikation eines neuen Targetproteins und dessen potenziellen Liganden langwierig, teuer und zeitintensiv. Die Verwendung von in silico Methoden gewinnt hier zunehmend an Bedeutung und hat sich als wertvolle Strategie zur Erkennung komplexer Zusammenhänge sowohl im Bereich der Struktur von Proteinen wie auch bei Bioaktivitäten erwiesen. Die zunehmende Nachfrage nach Rechenleistung im wissenschaftlichen Bereich sowie eine detaillierte Analyse der generierten Datenmengen benötigen innovative Strategien für die effiziente Verwendung von verteilten Computerressourcen, wie z.B. Computergrids. Diese Grids ergänzen bestehende Technologien um einen neuen Aspekt, indem sie heterogene Ressourcen zur Verfügung stellen und koordinieren. Diese Ressourcen beinhalten verschiedene Organisationen, Personen, Datenverarbeitung, Speicherungs- und Netzwerkeinrichtungen, sowie Daten, Wissen, Software und Arbeitsabläufe. Das Ziel dieser Arbeit war die Entwicklung einer universitätsweit anwendbaren Grid-Infrastruktur - UVieCo (University of Vienna Condor pool) -, welche für die Implementierung von akademisch frei verfügbaren struktur- und ligandenbasierten Drug Discovery Anwendungen verwendet werden kann. Firewall- und Sicherheitsprobleme wurden mittels eines virtuellen privaten Netzwerkes gelöst, wohingegen die Virtualisierung der Computerhardware über das CoLinux Konzept ermöglicht wurde. Dieses ermöglicht, dass unter Linux auszuführende Aufträge auf Windows Maschinen laufen können. Die Effektivität des Grids wurde durch Leistungsmessungen anhand sequenzieller und paralleler Aufgaben ermittelt. Als Anwendungsbeispiel wurde die Assoziation der Expression bzw. der Sensitivitätsprofile von ABC-Transportern mit den Aktivitätsprofilen von Antikrebswirkstoffen durch Data-Mining des NCI (National Cancer Institute) Datensatzes analysiert. Die dabei generierten Datensätze wurden für liganden-basierte Computermethoden wie Shape-Similarity und Klassifikationsalgorithmen mit dem Ziel verwendet, P-glycoprotein (P-gp) Substrate zu identifizieren und sie von Nichtsubstraten zu trennen. Beim Erstellen vorhersagekräftiger Klassifikationsmodelle konnte das Problem der extrem unausgeglichenen Klassenverteilung durch Verwendung der „Cost-Sensitive Bagging“ Methode gelöst werden. Applicability Domain Studien ergaben, dass unser Modell nicht nur die NCI Substanzen gut vorhersagen kann, sondern auch für wirkstoffähnliche Moleküle verwendet werden kann. Die entwickelten Modelle waren relativ einfach, aber doch präzise genug um für virtuelles Screening einer großen chemischen Bibliothek verwendet werden zu können. Dadurch könnten P-gp Substrate schon frühzeitig erkannt werden, was möglicherweise nützlich sein kann zur Entfernung von Substanzen mit schlechten ADMET-Eigenschaften bereits in einer frühen Phase der Arzneistoffentwicklung. Zusätzlich wurden Shape-Similarity und Self-organizing Map Techniken verwendet um neue Substanzen in einer hauseigenen sowie einer großen kommerziellen Datenbank zu identifizieren, die ähnlich zu selektiven Serotonin-Reuptake-Inhibitoren (SSRI) sind und Apoptose induzieren können. Die erhaltenen Treffer besitzen neue chemische Grundkörper und können als Startpunkte für Leitstruktur-Optimierung in Betracht gezogen werden. Die in dieser Arbeit beschriebenen Studien werden nützlich sein um eine verteilte Computerumgebung zu kreieren die vorhandene Ressourcen in einer Organisation nutzt, und die für verschiedene Anwendungen geeignet ist, wie etwa die effiziente Handhabung der Klassifizierung von unausgeglichenen Datensätzen, oder mehrstufiges virtuelles Screening.In the current drug discovery process, the identification of new target proteins and potential ligands is very tedious, expensive and time-consuming. Thus, use of in silico techniques is of utmost importance and proved to be a valuable strategy in detecting complex structural and bioactivity relationships. Increased demands of computational power for tremendous calculations in scientific fields and timely analysis of generated piles of data require innovative strategies for efficient utilization of distributed computing resources in the form of computational grids. Such grids add a new aspect to the emerging information technology paradigm by providing and coordinating the heterogeneous resources such as various organizations, people, computing, storage and networking facilities as well as data, knowledge, software and workflows. The aim of this study was to develop a university-wide applicable grid infrastructure, UVieCo (University of Vienna Condor pool) which can be used for implementation of standard structure- and ligand-based drug discovery applications using freely available academic software. Firewall and security issues were resolved with a virtual private network setup whereas virtualization of computer hardware was done using the CoLinux concept in a way to run Linux-executable jobs inside Windows machines. The effectiveness of the grid was assessed by performance measurement experiments using sequential and parallel tasks. Subsequently, the association of expression/sensitivity profiles of ABC transporters with activity profiles of anticancer compounds was analyzed by mining the data from NCI (National Cancer Institute). The datasets generated in this analysis were utilized with ligand-based computational methods such as shape similarity and classification algorithms to identify and separate P-gp substrates from non-substrates. While developing predictive classification models, the problem of imbalanced class distribution was proficiently addressed using the cost-sensitive bagging approach. Applicability domain experiment revealed that our model not only predicts NCI compounds well, but it can also be applied to drug-like molecules. The developed models were relatively simple but precise enough to be applicable for virtual screening of large chemical libraries for the early identification of P-gp substrates which can potentially be useful to remove compounds of poor ADMET properties in an early phase of drug discovery. Additionally, shape-similarity and self-organizing maps techniques were used to screen in-house as well as a large vendor database for identification of novel selective serotonin reuptake inhibitor (SSRI) like compounds to induce apoptosis. The retrieved hits possess novel chemical scaffolds and can be considered as a starting point for lead optimization studies. The work described in this thesis will be useful to create distributed computing environment using available resources within an organization and can be applied to various applications such as efficient handling of imbalanced data classification problems or multistep virtual screening approach