Heuristic Algorithms for the Maximum Colorful Subtree Problem

In metabolomics, small molecules are structurally elucidated using tandem mass spectrometry (MS/MS); this computational task can be formulated as the Maximum Colorful Subtree problem, which is NP-hard. Unfortunately, data from a single metabolite requires us to solve hundreds or thousands of instances of this problem - and in a single Liquid Chromatography MS/MS run, hundreds or thousands of metabolites are measured. Here, we comprehensively evaluate the performance of several heuristic algorithms for the problem. Unfortunately, as is often the case in bioinformatics, the structure of the (chemically) true solution is not known to us; therefore we can only evaluate against the optimal solution of an instance. Evaluating the quality of a heuristic based on scores can be misleading: Even a slightly suboptimal solution can be structurally very different from the optimal solution, but it is the structure of a solution and not its score that is relevant for the downstream analysis. To this end, we propose a different evaluation setup: Given a set of candidate instances of which exactly one is known to be correct, the heuristic in question solves each instance to the best of its ability, producing a score for each instance, which is then used to rank the instances. We then evaluate whether the correct instance is ranked highly by the heuristic. We find that one particular heuristic consistently ranks the correct instance in a top position. We also find that the scores of the best heuristic solutions are very close to the optimal score; in contrast, the structure of the solutions can deviate significantly from the optimal structures. Integrating the heuristic allowed us to speed up computations in practice by a factor of 100-fold

Bayesian methods for small molecule identification

Confident identification of small molecules remains a major challenge in untargeted metabolomics, natural product research and related fields. Liquid chromatography-tandem mass spectrometry is a predominant technique for the high-throughput analysis of small molecules and can detect thousands of different compounds in a biological sample. The automated interpretation of the resulting tandem mass spectra is highly non-trivial and many studies are limited to re-discovering known compounds by searching mass spectra in spectral reference libraries. But these libraries are vastly incomplete and a large portion of measured compounds remains unidentified. This constitutes a major bottleneck in the comprehensive, high-throughput analysis of metabolomics data. In this thesis, we present two computational methods that address different steps in the identification process of small molecules from tandem mass spectra. ZODIAC is a novel method for de novo that is, database-independent molecular formula annotation in complete datasets. It exploits similarities of compounds co-occurring in a sample to find the most likely molecular formula for each individual compound. ZODIAC improves on the currently best-performing method SIRIUS; on one dataset by 16.5 fold. We show that de novo molecular formula annotation is not just a theoretical advantage: We discover multiple novel molecular formulas absent from PubChem, one of the biggest structure databases. Furthermore, we introduce a novel scoring for CSI:FingerID, a state-of-the-art method for searching tandem mass spectra in a structure database. This scoring models dependencies between different molecular properties in a predicted molecular fingerprint via Bayesian networks. This problem has the unusual property, that the marginal probabilities differ for each predicted query fingerprint. Thus, we need to apply Bayesian networks in a novel, non-standard fashion. Modeling dependencies improves on the currently best scoring

Computational methods for small molecule identification

Identification of small molecules remains a central question in analytical chemistry, in particular for natural product research, metabolomics, environmental research, and biomarker discovery. Mass spectrometry is the predominant technique for high-throughput analysis of small molecules. But it reveals only information about the mass of molecules and, by using tandem mass spectrometry, about the mass of molecular fragments. Automated interpretation of mass spectra is often limited to searching in spectral libraries, such that we can only dereplicate molecules for which we have already recorded reference mass spectra. In this thesis we present methods for answering two central questions: What is the molecular formula of the measured ion and what is its molecular structure? SIRIUS is a combinatorial optimization method for annotating a spectrum and identifying the ion's molecular formula by computing hypothetical fragmentation trees. We present a new scoring for computing fragmentation trees, transforming the combinatorial optimization into a maximum a posteriori estimator. This allows us to learn parameters and hyperparameters of the scoring directly from data. We demonstrate that the statistical model, which was fitted on a small dataset, generalises well across many different datasets and mass spectrometry instruments. In addition to tandem mass spectra, isotope pattern can be used for identifying the molecular formula of the precursor ion. We present a novel scoring for comparing isotope patterns based on maximum likelihood. We describe how to integrate the isotope pattern analysis into the fragmentation tree optimisation problem to analyse data were fragment peaks and isotope peaks occur within the same spectrum. We demonstrate that the new scorings significantly improves on the task of molecular formula assignment. We evaluate SIRIUS on several datasets and show that it outperforms all other methods for molecular formula annotation by a large margin. We also present CSI:FingerID, a method for predicting a molecular fingerprint from a tandem mass spectrum using kernel support vector machines. The predicted fingerprint can be searched in a structure database to identify the molecular structure. CSI:FingerID is based on FingerID, that uses probability product kernels on mass spectra for this task. We describe several novel kernels for comparing fragmentation trees instead of spectra. These kernels are combined using multiple kernel learning. We present a new scoring based on posterior probabilities and extend the method to use additional molecular fingerprints. We demonstrate on several datasets that CSI:FingerID identifies more molecules than its predecessor FingerID and outperforms all other methods for this task. We analyse how each of the methodological improvements of CSI:FingerID contributes to its identification performance and make suggestions for future improvements of the method. Both methods, SIRIUS and CSI:FingerID, are available as commandline tool and as user interface. The molecular fingerprint prediction is implemented as web service and receives over one million requests per month.Die Identifizierung kleiner Moleküle ist eine zentrale Fragestellung der analytischen Chemie, insbesondere in der Naturwirkstoffforschung, der Metabolomik, der Ökologie und Umweltforschung sowie in der Entwicklung neuer Diagnoseverfahren mittels Biomarker. Massenspektrometrie ist die vorherrschende Technik für Hochdurchsatzanalysen kleiner Moleküle. Aber sie liefert nur Informationen über die Masse der gemessenen Moleküle und, mittels Tandem-Massenspektrometrie, über die Massen der gemessenen Fragmente. Die automatisierte Auswertung von Massenspektren beschränkt sich oft auf die Suche in Spektrendatenbanken, so dass nur Moleküle derepliziert werden können, die bereits in einer solchen Datenbank gemessen wurden. In dieser Dissertation präsentieren wir zwei Methoden zur Beantwortung zweier zentraler Fragen: Was ist die Molekülformel eines gemessenen Ions? Und was ist seine Molekülstruktur? SIRIUS ist eine Methode der kombinatorischen Optimierung für die Annotation von Massenspektren und der Identifikation der Molekülformel. Dazu berechnet sie hypothetische Fragmentierungsbäume. Wir stellen ein neues Scoring Modell für die Berechnung von Fragmentierungsbäumen vor, welches die kombinatorische Optimierung als einen Maximum-a-posteriori-Schätzer auffasst. Dieses Modell ermöglicht es uns, Parameter und Hyperparameter des Scorings direkt aus den Daten abzuschätzen. Wir zeigen, dass dieses statistische Modell, dessen (Hyper)Parameter auf einem kleinen Datensatz geschätzt wurden, allgemeingültig für viele Datensätze und sogar für verschiedene Massenspektrometriegeräte ist. Neben Tandem-Massenspektren lassen sich auch Isotopenmuster zur Molekülformelidentifizierung des Ions verwenden. Wir stellen ein neuartiges Scoring für den Vergleich von Isotopenmustern vor, welches auf Maximum Likelihood basiert. Wir beschreiben, wie die Isotopenmusteranalyse in das Optimierungsproblem für Fragmentierungsbäume integriert werden kann, so dass sich auch Daten analysieren lassen, in denen Fragmente und Isotopenmuster im selben Massenspektrum gemessen werden. Wir zeigen, dass das neue Scoring die korrekte Zuweisung der Molekülformeln signifikant verbessert. Wir evaluieren SIRIUS auf einer Vielzahl von Datensätzen und zeigen, dass die Methode deutlich besser funktioniert als alle anderen Methoden für die Identifikation von Molekülformeln. Wir stellen außerdem CSI:FingerID vor, eine Methode, die Kernel Support Vector Maschinen zur Vorhersage von molekularen Fingerabdrücken aus Tandem-Massenspektren nutzt. Vorhergesagte molekulare Fingerabdrücke können in Strukturdatenbanken gesucht werden, um die genaue Molekülstruktur aufzuklären. CSI:FingerID basiert auf FingerID, welches Wahrscheinlichkeitsprodukt-Kernels für diese Aufgabe benutzt. Wir beschreiben etliche neue Kernels, zum Vergleich von Fragmentierungsbäumen anstelle von Massenspektren. Diese Kernels werden mittels Multiple Kernel Learning zu einem Kernel kombiniert. Wir stellen ein neues Scoring vor, welches auf A-posteriori-Wahrscheinlichkeiten basiert. Außerdem erweitern wir die Methode, so dass sie zusätzliche molekulare Fingerabdrücke verwendet. Wir zeigen auf verschiedenen Testdatensätzen, dass CSI:FingerID mehr Molekülstrukturen identifizieren kann als der Vorgänger FingerID, und damit auch alle anderen Methoden für diese Anwendung übertrifft. Wir werten aus, wie die verschiedenen methodischen Erweiterung zur Identifikationsrate von CSI:FingerID beitragen und machen Vorschläge für künftige Verbesserungen der Methode. Beide Methoden, SIRIUS und CSI:FingerID, sind als Kommandozeilenprogramm und als Benutzeroberfläche verfügbar. Die Vorhersage molekularer Fingerabdrücke ist als Webservice implementiert, der über eine Millionen Anfragen pro Monat erhält

Metareasoning for Heuristic Search Using Uncertainty

Heuristic search methods are widely used in many real-world autonomous systems. Yet, people always want to solve search problems that are larger than time allows. To address these challenging problems, even suboptimally, a planning agent should be smart enough to intelligently allocate its computational resources, to think carefully about where in the state space it should spend time searching. For finding optimal solutions, we must examine every node that is not provably too expensive. In contrast, to find suboptimal solutions when under time pressure, we need to be very selective about which nodes to examine. In this dissertation, we will demonstrate that estimates of uncertainty, represented as belief distributions, can be used to drive search effectively. This type of algorithmic approach is known as metareasoning, which refers to reasoning about which reasoning to do. We will provide examples of improved algorithms for real-time search, bounded-cost search, and situated planning

Metareasoning for Heuristic Search Using Uncertainty

Heuristic search methods are widely used in many real-world autonomous systems. Yet, people always want to solve search problems that are larger than time allows. To address these challenging problems, even suboptimally, a planning agent should be smart enough to intelligently allocate its computational resources, to think carefully about where in the state space it should spend time searching. For finding optimal solutions, we must examine every node that is not provably too expensive. In contrast, to find suboptimal solutions when under time pressure, we need to be very selective about which nodes to examine. In this dissertation, we will demonstrate that estimates of uncertainty, represented as belief distributions, can be used to drive search effectively. This type of algorithmic approach is known as metareasoning, which refers to reasoning about which reasoning to do. We will provide examples of improved algorithms for real-time search, bounded-cost search, and situated planning

Rare Feature Selection in High Dimensions

It is common in modern prediction problems for many predictor variables to be counts of rarely occurring events. This leads to design matrices in which many columns are highly sparse. The challenge posed by such "rare features" has received little attention despite its prevalence in diverse areas, ranging from natural language processing (e.g., rare words) to biology (e.g., rare species). We show, both theoretically and empirically, that not explicitly accounting for the rareness of features can greatly reduce the effectiveness of an analysis. We next propose a framework for aggregating rare features into denser features in a flexible manner that creates better predictors of the response. Our strategy leverages side information in the form of a tree that encodes feature similarity. We apply our method to data from TripAdvisor, in which we predict the numerical rating of a hotel based on the text of the associated review. Our method achieves high accuracy by making effective use of rare words; by contrast, the lasso is unable to identify highly predictive words if they are too rare. A companion R package, called rare, implements our new estimator, using the alternating direction method of multipliers.Comment: 42 pages, 10 figure

Transactions Chasing Scalability and Instruction Locality on Multicores

For several decades, online transaction processing (OLTP) has been one of the main server applications that drives innovations in the data management ecosystem, and in turn the database and computer architecture communities. Recent hardware trends oblige software to overcome two major challenges against systems scalability on modern multicore processors: (1) exploiting the abundant thread-level parallelism across cores and (2) taking advantage of the implicit parallelism within a core. The traditional design of the OLTP systems, however, faces inherent scalability problems due to its tightly coupled components. In addition, OLTP cannot exploit the full capability of the micro-architectural resources of modern processors because of the conventional scheduling decisions that ignore the cache locality for transactions. As a result, today’s commonly used server hardware remains largely underutilized leading to a huge waste of hardware resources and energy. .... In this thesis, we first identify the unbounded critical sections of traditional OLTP systems as the main enemy of thread-level parallelism. We design an alternative shared-everything system based on physiological partitioning (PLP) to eliminate the unbounded critical sections while providing an infrastructure for low-cost dynamic repartitioning and without introducing high-cost distributed transactions. Then, we demonstrate that L1 instruction cache stalls are the dominant factor leading to underutilization in the commodity servers. However, we also observe that independently of their high-level functionality, transactions running in parallel on a multicore system share significant amount of common instructions. By adaptively spreading the execution of a transaction over multiple cores through thread migration or multiplexing transactions on one core, we enable both an ample L1 instruction cache capacity for a transaction and reuse of common instructions across concurrent transactions. .... As the hardware demands more from the software to exploit the complexity and parallelism it offers in the multicore era, this work would change the way we traditionally schedule transactions. Instead of viewing a transaction as a single big task, we split it into smaller parts that can exploit data and instruction locality through careful dynamic scheduling decisions. The methods this thesis presents are not only specific to OLTP systems, but they can also benefit other types of applications that have concurrent requests executing a series of actions from a predefined set and face similar scalability problems on emerging hardware

INFORMATION VISUALIZATION DESIGN FOR MULTIDIMENSIONAL DATA: INTEGRATING THE RANK-BY-FEATURE FRAMEWORK WITH HIERARCHICAL CLUSTERING

Interactive exploration of multidimensional data sets is challenging because: (1) it is difficult to comprehend patterns in more than three dimensions, and (2) current systems are often a patchwork of graphical and statistical methods leaving many researchers uncertain about how to explore their data in an orderly manner. This dissertation offers a set of principles and a novel rank-by-feature framework that could enable users to better understand multidimensional and multivariate data by systematically studying distributions in one (1D) or two dimensions (2D), and then discovering relationships, clusters, gaps, outliers, and other features. Users of this rank-by-feature framework can view graphical presentations (histograms, boxplots, and scatterplots), and then choose a feature detection criterion to rank 1D or 2D axis-parallel projections. By combining information visualization techniques (overview, coordination, and dynamic query) with summaries and statistical methods, users can systematically examine the most important 1D and 2D axis-parallel projections. This research provides a number of valuable contributions: Graphics, Ranking, and Interaction for Discovery (GRID) principles- a set of principles for exploratory analysis of multidimensional data, which are summarized as: (1) study 1D, study 2D, then find features (2) ranking guides insight, statistics confirm. GRID principles help users organize their discovery process in an orderly manner so as to produce more thorough analyses and extract deeper insights in any multidimensional data application. Rank-by-feature framework - a user interface framework based on the GRID principles. Interactive information visualization techniques are combined with statistical methods and data mining algorithms to enable users to orderly examine multidimensional data sets using 1D and 2D projections. The design and implementation of the Hierarchical Clustering Explorer (HCE), an information visualization tool available at www.cs.umd.edu/hcil/hce. HCE implements the rank-by-feature framework and supports interactive exploration of hierarchical clustering results to reveal one of the important features - clusters. Validation through case studies and user surveys: Case studies with motivated experts in three research fields and a user survey via emails to a wide range of HCE users demonstrated the efficacy of HCE and the rank-by-feature framework. These studies also revealed potential improvement opportunities in terms of design and implementation