Comparison of classification methods for detecting associations between SNPs and chick mortality

Multi-category classification methods were used to detect SNP-mortality associations in broilers. The objective was to select a subset of whole genome SNPs associated with chick mortality. This was done by categorizing mortality rates and using a filter-wrapper feature selection procedure in each of the classification methods evaluated. Different numbers of categories (2, 3, 4, 5 and 10) and three classification algorithms (naïve Bayes classifiers, Bayesian networks and neural networks) were compared, using early and late chick mortality rates in low and high hygiene environments. Evaluation of SNPs selected by each classification method was done by predicted residual sum of squares and a significance test-related metric. A naïve Bayes classifier, coupled with discretization into two or three categories generated the SNP subset with greatest predictive ability. Further, an alternative categorization scheme, which used only two extreme portions of the empirical distribution of mortality rates, was considered. This scheme selected SNPs with greater predictive ability than those chosen by the methods described previously. Use of extreme samples seems to enhance the ability of feature selection procedures to select influential SNPs in genetic association studies

Overview of Random Forest Methodology and Practical Guidance with Emphasis on Computational Biology and Bioinformatics

The Random Forest (RF) algorithm by Leo Breiman has become a standard data analysis tool in bioinformatics. It has shown excellent performance in settings where the number of variables is much larger than the number of observations, can cope with complex interaction structures as well as highly correlated variables and returns measures of variable importance. This paper synthesizes ten years of RF development with emphasis on applications to bioinformatics and computational biology. Special attention is given to practical aspects such as the selection of parameters, available RF implementations, and important pitfalls and biases of RF and its variable importance measures (VIMs). The paper surveys recent developments of the methodology relevant to bioinformatics as well as some representative examples of RF applications in this context and possible directions for future research

Scalable Feature Selection Applications for Genome-Wide Association Studies of Complex Diseases

Personalized medicine will revolutionize our capabilities to combat disease. Working toward this goal, a fundamental task is the deciphering of geneticvariants that are predictive of complex diseases. Modern studies, in the formof genome-wide association studies (GWAS) have afforded researchers with the opportunity to reveal new genotype-phenotype relationships through the extensive scanning of genetic variants. These studies typically contain over half a million genetic features for thousands of individuals. Examining this with methods other than univariate statistics is a challenging task requiring advanced algorithms that are scalable to the genome-wide level. In the future, next-generation sequencing studies (NGS) will contain an even larger number of common and rare variants. Machine learning-based feature selection algorithms have been shown to have the ability to effectively create predictive models for various genotype-phenotype relationships. This work explores the problem of selecting genetic variant subsets that are the most predictive of complex disease phenotypes through various feature selection methodologies, including filter, wrapper and embedded algorithms. The examined machine learning algorithms were demonstrated to not only be effective at predicting the disease phenotypes, but also doing so efficiently through the use of computational shortcuts. While much of the work was able to be run on high-end desktops, some work was further extended so that it could be implemented on parallel computers helping to assure that they will also scale to the NGS data sets. Further, these studies analyzed the relationships between various feature selection methods and demonstrated the need for careful testing when selecting an algorithm. It was shown that there is no universally optimal algorithm for variant selection in GWAS, but rather methodologies need to be selected based on the desired outcome, such as the number of features to be included in the prediction model. It was also demonstrated that without proper model validation, for example using nested cross-validation, the models can result in overly-optimistic prediction accuracies and decreased generalization ability. It is through the implementation and application of machine learning methods that one can extract predictive genotype–phenotype relationships and biological insights from genetic data sets.Siirretty Doriast

Deciphering the genetic background of quantitative traits using machine learning and bioinformatics frameworks

In dieser Doktorarbeit habe ich zwei Ansätze verfolgt, mit denen genetische Mechanismen, welche quantitativen Merkmalen zugrunde liegen, aufgezeigt und bestimmt werden können. In diesem Zusammenhang lag mein Fokus auf der Entwicklung effizienter Methoden um Genotyp-Phänotyp Assoziationen zu identifizieren. Durch diese lassen sich im Weiteren regulatorische Mechanismen beschreiben, welche phänotypische Unterschiede zwischen Individuen verursachen. Im ersten Ansatz habe ich Schlüsselmechanismen der Genregulation untersucht, welche die Entwicklung der Bruchfestigkeit von Eierschalen steuern. Das Ziel war es zeitliche Unterschiede der Signalkaskaden, welche die Eierschalen Bruchfestigkeit im Verlauf eines Vogellebens regulieren, zu detektieren. Hierfür habe ich die Bruchfestigkeit zu zwei verschiedenen Zeitpunkten innerhalb eines Produktionszyklus betrachtet und die Genotyp-Phänotyp Assoziationen mithilfe eines Random Forest-Algorithmus bestimmt. Für die Analyse der entsprechenden Gene wurde ein etablierter systembiologischer Ansatz verfolgt, mit dem genregulatorische Pathways und Master-Regulatoren identifiziert werden konnten. Meine Ergebnisse zeigen, dass einige Pathways und Master-Regulatoren (z.B. Slc22a1 und Sox11) gleichzeitig in verschiedenen Legephasen identifiziert wurden, andere (z.B. Scn11a, St8sia2 oder der TGF-beta Pathway) speziell in lediglich einer Phase gefunden wurden. Sie stellen somit altersspezifische Mechanismen dar.Insgesamt liefern meine Ergebnisse (i) signifikante Einblicke in altersspezifische und allgemeine molekulare Mechanismen, welche die Eierschalen-Bruchfestigkeit regulieren und bestimmen; und (ii) neue Zuchtziele, um die Bruchstärke von Eierschalen vor allem in späteren Legephasen zu erhöhen und somit die Eierschalen Qualität zu verbessern. In meinem zweitem Ansatz, habe ich die Methode der Random Forests mit einer Strategie zur Signaldetektierung kombiniert, um robuste Genotyp-Phänotyp-Beziehungen zu identifizieren. Ziel dieses Ansatzes war die Verbesserung der Effizienz der Einzel-SNP basierten Assoziationsanalyse. Genomweite Assoziationsstudien (GWAS) sind ein weit verbreiteter Ansatz zur Identifikation genomischer Varianten und Genen, die verantwortlich sind für Merkmale, welche von Interesse sowohl für den akademischen als auch den wirtschaftlichen Sektor sind. Trotz des langjährigen Einsatzes verschiedener GWAS-Methoden stellt die zuverlässige Identifikation von Genotyp-Phänotyp-Beziehungen noch immer eine Herausforderung für viele quantitative Merkmale dar. Dies wird hauptsächlich durch die große Anzahl genomischer Loci begründet, welche lediglich einen schwachen Effekt auf das zu untersuchende Merkmal haben. Daher lässt sich Hypothese aufstellen, dass genomische Varianten, welche zwar einen geringen, aber dennoch realen Einfluss ausüben, in vielen GWAS-Ansätzen unentdeckt bleiben. Zur Behandlung dieser Unzulänglichkeiten wird in der Arbeit ein zweistufiges Verfahren verwendet. Zunächst werden kubische Splines für Teststatistiken und genomische Regionen angepasst. Die Spline-Maxima, welche höher als die zu erwartenden zufallsbasierten Maximalwerte ausfallen, werden als quantitative Merkmals-Loci (QTL) eingestuft. Anschließend werden die SNPs in diesen QTLs, basierend auf ihrer Assoziationsstärke mit den Phänotypen, durch einen Random Forests-Ansatz priorisiert. Im Rahmen einer Fallstudie haben wir unseren Ansatz auf reale Datensätze angewendet und eine plausible Anzahl, teilweise neuartiger, genomischer Varianten und Genen identifiziert, welche verschiedenen Qualitätsmerkmalen zugrunde liegen.In this thesis, I developed two frameworks that can help highlight the genetic mechanisms underlying quantitative traits. In this regard, my focus was to design efficient methodologies to discover genotype-phenotype associations and then use these identified associations to describe the regulatory mechanism that affects the manifestation of phenotypic differences among the individuals. In the first framework, I investigated key regulatory mechanisms governing the development of eggshell strength. The aim was to highlight the temporal changes in the signaling cascades governing the dynamic eggshell strength during the life of birds. I considered chicken eggshell strength at two different time points during the egg production cycle and studied the genotype-phenotype associations by employing the Random Forest algorithm on genotypic data. For the analysis of corresponding genes, a well established systems biology approach was adopted to delineate gene regulatory pathways and master regulators underlying this important trait. My results indicate that, while some of the master regulators (Slc22a1 and Sox11) and pathways are common at different laying stages of chicken, others (e.g., Scn11a, St8sia2, or the TGF-beta pathway) represent age-specific functions. Overall, my results provide: (i) significant insights into age-specific and common molecular mechanisms underlying the regulation of eggshell strength; and (ii) new breeding targets to improve the eggshell quality during the later stages of the chicken production cycle. In my second framework, I combined the Random Forests and a signal detection strategy to identify robust genotype-phenotype associations. The objective of this framework was to improve on the efficiency of single-SNP based association analysis. Genome wide association studies (GWAS) are a well established methodology to identify genomic variants and genes that are responsible for traits of interest in all branches of the life sciences. Despite the long time this methodology has had to mature the reliable detection of genotype-phenotype associations is still a challenge for many quantitative traits mainly because of the large number of genomic loci with weak individual effects on the trait under investigation. Thus, it can be hypothesized that many genomic variants that have a small, however real, effect~remain unnoticed in many GWAS approaches. Here, we propose a two-step procedure to address this problem. In a first step, cubic splines are fitted to the test statistic values and genomic regions with spline-peaks that are higher than expected by chance are considered as quantitative trait loci (QTL). Then the SNPs in these QTLs are prioritized with respect to the strength of their association with the phenotype using a Random Forests approach. As a case study, we apply our procedure to real data sets and find trustworthy numbers of, partially novel, genomic variants and genes involved in various egg quality traits.2021-10-1

Wrapper algorithms and their performance assessment on high-dimensional molecular data

Prediction problems on high-dimensional molecular data, e.g. the classification of microar- ray samples into normal and cancer tissues, are complex and ill-posed since the number of variables usually exceeds the number of observations by orders of magnitude. Recent research in the area has propagated a variety of new statistical models in order to handle these new biological datasets. In practice, however, these models are always applied in combination with preprocessing and variable selection methods as well as model selection which is mostly performed by cross-validation. Varma and Simon (2006) have used the term ‘wrapper-algorithm’ for this integration of preprocessing and model selection into the construction of statistical models. Additionally, they have proposed the method of nested cross-validation (NCV) as a way of estimating their prediction error which has evolved to the gold-standard by now. In the first part, this thesis provides further theoretical and empirical justification for the usage of NCV in the context of wrapper-algorithms. Moreover, a computationally less intensive alternative to NCV is proposed which can be motivated in a decision theoretic framework. The new method can be interpreted as a smoothed variant of NCV and, in contrast to NCV, guarantees intuitive bounds for the estimation of the prediction error. The second part focuses on the ranking of wrapper algorithms. Cross-study-validation is proposed as an alternative concept to the repetition of separated within-study-validations if several similar prediction problems are available. The concept is demonstrated using six different wrapper algorithms for survival prediction on censored data on a selection of eight breast cancer datasets. Additionally, a parametric bootstrap approach for simulating realistic data from such related prediction problems is described and subsequently applied to illustrate the concept of cross-study-validation for the ranking of wrapper algorithms. Eventually, the last part approaches computational aspects of the analyses and simula- tions performed in the thesis. The preprocessing before the analysis as well as the evaluation of the prediction models requires the usage of large computing resources. Parallel comput- ing approaches are illustrated on cluster, cloud and high performance computing resources using the R programming language. Usage of heterogeneous hardware and processing of large datasets are covered as well as the implementation of the R-package survHD for the analysis and evaluation of high-dimensional wrapper algorithms for survival prediction from censored data.Prädiktionsprobleme für hochdimensionale genetische Daten, z.B. die Klassifikation von Proben in normales und Krebsgewebe, sind komplex und unterbestimmt, da die Anzahl der Variablen die Anzahl der Beobachtungen um ein Vielfaches übersteigt. Die Forschung hat auf diesem Gebiet in den letzten Jahren eine Vielzahl an neuen statistischen Meth- oden hervorgebracht. In der Praxis werden diese Algorithmen jedoch stets in Kombination mit Vorbearbeitung und Variablenselektion sowie Modellwahlverfahren angewandt, wobei letztere vorwiegend mit Hilfe von Kreuzvalidierung durchgeführt werden. Varma und Simon (2006) haben den Begriff ’Wrapper-Algorithmus’ für eine derartige Einbet- tung von Vorbearbeitung und Modellwahl in die Konstruktion einer statistischen Methode verwendet. Zudem haben sie die genestete Kreuzvalidierung (NCV) als eine Methode zur Sch ̈atzung ihrer Fehlerrate eingeführt, welche sich mittlerweile zum Goldstandard entwickelt hat. Im ersten Teil dieser Doktorarbeit, wird eine tiefergreifende theoretische Grundlage sowie eine empirische Rechtfertigung für die Anwendung von NCV bei solchen ’Wrapper-Algorithmen’ vorgestellt. Außerdem wird eine alternative, weniger computerintensive Methode vorgeschlagen, welche im Rahmen der Entscheidungstheorie motiviert wird. Diese neue Methode kann als eine gegl ̈attete Variante von NCV interpretiert wer- den und hält im Gegensatz zu NCV intuitive Grenzen bei der Fehlerratenschätzung ein. Der zweite Teil behandelt den Vergleich verschiedener ’Wrapper-Algorithmen’ bzw. das Sch ̈atzen ihrer Reihenfolge gem ̈aß eines bestimmten Gütekriteriums. Als eine Alterna- tive zur wiederholten Durchführung von Kreuzvalidierung auf einzelnen Datensätzen wird das Konzept der studienübergreifenden Validierung vorgeschlagen. Das Konzept wird anhand von sechs verschiedenen ’Wrapper-Algorithmen’ für die Vorhersage von Uberlebenszeiten bei acht Brustkrebsstudien dargestellt. Zusätzlich wird ein Bootstrapverfahren beschrieben, mit dessen Hilfe man mehrere realistische Datens ̈atze aus einer Menge von solchen verwandten Prädiktionsproblemen generieren kann. Der letzte Teil beleuchtet schließlich computationale Verfahren, die bei der Umsetzung der Analysen in dieser Dissertation eine tragende Rolle gespielt haben. Die Vorbearbeitungsschritte sowie die Evaluation der Prädiktionsmodelle erfordert die extensive Nutzung von Computerressourcen. Es werden Ansätze zum parallelen Rechnen auf Cluster-, Cloud- und Hochleistungsrechen- ressourcen unter der Verwendung der Programmiersprache R beschrieben. Die Benutzung von heterogenen Hardwarearchitekturen, die Verarbeitung von großen Datensätzen sowie die Entwicklung des R-Pakets survHD für die Analyse und Evaluierung von ’Wrapper- Algorithmen’ zur Uberlebenszeitenanalyse werden thematisiert

Network-Based Biomarker Discovery : Development of Prognostic Biomarkers for Personalized Medicine by Integrating Data and Prior Knowledge

Advances in genome science and technology offer a deeper understanding of biology while at the same time improving the practice of medicine. The expression profiling of some diseases, such as cancer, allows for identifying marker genes, which could be able to diagnose a disease or predict future disease outcomes. Marker genes (biomarkers) are selected by scoring how well their expression levels can discriminate between different classes of disease or between groups of patients with different clinical outcome (e.g. therapy response, survival time, etc.). A current challenge is to identify new markers that are directly related to the underlying disease mechanism

Improving Feature Selection Techniques for Machine Learning

As a commonly used technique in data preprocessing for machine learning, feature selection identifies important features and removes irrelevant, redundant or noise features to reduce the dimensionality of feature space. It improves efficiency, accuracy and comprehensibility of the models built by learning algorithms. Feature selection techniques have been widely employed in a variety of applications, such as genomic analysis, information retrieval, and text categorization. Researchers have introduced many feature selection algorithms with different selection criteria. However, it has been discovered that no single criterion is best for all applications. We proposed a hybrid feature selection framework called based on genetic algorithms (GAs) that employs a target learning algorithm to evaluate features, a wrapper method. We call it hybrid genetic feature selection (HGFS) framework. The advantages of this approach include the ability to accommodate multiple feature selection criteria and find small subsets of features that perform well for the target algorithm. The experiments on genomic data demonstrate that ours is a robust and effective approach that can find subsets of features with higher classification accuracy and/or smaller size compared to each individual feature selection algorithm. A common characteristic of text categorization tasks is multi-label classification with a great number of features, which makes wrapper methods time-consuming and impractical. We proposed a simple filter (non-wrapper) approach called Relation Strength and Frequency Variance (RSFV) measure. The basic idea is that informative features are those that are highly correlated with the class and distribute most differently among all classes. The approach is compared with two well-known feature selection methods in the experiments on two standard text corpora. The experiments show that RSFV generate equal or better performance than the others in many cases

Machine learning applications in microbial ecology, human microbiome studies, and environmental monitoring

Advances in nucleic acid sequencing technology have enabled expansion of our ability to profile microbial diversity. These large datasets of taxonomic and functional diversity are key to better understanding microbial ecology. Machine learning has proven to be a useful approach for analyzing microbial community data and making predictions about outcomes including human and environmental health. Machine learning applied to microbial community profiles has been used to predict disease states in human health, environmental quality and presence of contamination in the environment, and as trace evidence in forensics. Machine learning has appeal as a powerful tool that can provide deep insights into microbial communities and identify patterns in microbial community data. However, often machine learning models can be used as black boxes to predict a specific outcome, with little understanding of how the models arrived at predictions. Complex machine learning algorithms often may value higher accuracy and performance at the sacrifice of interpretability. In order to leverage machine learning into more translational research related to the microbiome and strengthen our ability to extract meaningful biological information, it is important for models to be interpretable. Here we review current trends in machine learning applications in microbial ecology as well as some of the important challenges and opportunities for more broad application of machine learning to understanding microbial communities

Integration and visualisation of clinical-omics datasets for medical knowledge discovery

In recent decades, the rise of various omics fields has flooded life sciences with unprecedented amounts of high-throughput data, which have transformed the way biomedical research is conducted. This trend will only intensify in the coming decades, as the cost of data acquisition will continue to decrease. Therefore, there is a pressing need to find novel ways to turn this ocean of raw data into waves of information and finally distil those into drops of translational medical knowledge. This is particularly challenging because of the incredible richness of these datasets, the humbling complexity of biological systems and the growing abundance of clinical metadata, which makes the integration of disparate data sources even more difficult. Data integration has proven to be a promising avenue for knowledge discovery in biomedical research. Multi-omics studies allow us to examine a biological problem through different lenses using more than one analytical platform. These studies not only present tremendous opportunities for the deep and systematic understanding of health and disease, but they also pose new statistical and computational challenges. The work presented in this thesis aims to alleviate this problem with a novel pipeline for omics data integration. Modern omics datasets are extremely feature rich and in multi-omics studies this complexity is compounded by a second or even third dataset. However, many of these features might be completely irrelevant to the studied biological problem or redundant in the context of others. Therefore, in this thesis, clinical metadata driven feature selection is proposed as a viable option for narrowing down the focus of analyses in biomedical research. Our visual cortex has been fine-tuned through millions of years to become an outstanding pattern recognition machine. To leverage this incredible resource of the human brain, we need to develop advanced visualisation software that enables researchers to explore these vast biological datasets through illuminating charts and interactivity. Accordingly, a substantial portion of this PhD was dedicated to implementing truly novel visualisation methods for multi-omics studies.Open Acces

Applications and extensions of Random Forests in genetic and environmental studies

Transcriptional regulation refers to the molecular systems that control the concentration of mRNA species within the cell. Variation in these controlling systems is not only responsible for many diseases, but also contributes to the vast phenotypic diversity in the biological world. There are powerful experimental approaches to probe these regulatory systems, and the focus of my doctoral research has been to develop and apply effective computational methods that exploit these rich data sets more completely. First, I present a method for mapping genetic regulators of gene expression (expression quantitative trait loci, or eQTL) using Random Forests. This approach allows for flexible modeling and feature selection, and results in eQTL that are more biologically supportable than those mapped with competing methods. Next, I present a method that finds interactions between genes that in turn regulate the expression of other genes. This is accomplished by finding recurring decision motifs in the forest structure that represent dependencies between genetic loci. Third, I present a method to use distributional differences in eQTL data to establish the regulatory roles of genes relative to other disease-associated genes. Using this method, we found that genes that are master regulators of other disease genes are more likely to be consistently associated with the disease in genetic association studies. Finally, I present a novel application of Random Forests to determine the mode of regulation of toxin-perturbed genes, using time-resolved gene expression. The results demonstrate a novel approach to supervised weighted clustering of gene expression data