49 research outputs found
Oqtans: a Galaxy-integrated workflow for quantitative transcriptome analysis from NGS Data : From Seventh International Society for Computational Biology (ISCB) Student Council Symposium 2011 Vienna, Austria. 15 July 2011
First published by BioMed Central:
Schultheiss, Sebastian J.; Jean, Géraldine; Behr, Jonas; Bohnert, Regina; Drewe, Philipp; Görnitz, Nico; Kahles, André; Mudrakarta, Pramod; Sreedharan, Vipin T.; Zeller, Georg; Rätsch, Gunnar: Oqtans: a Galaxy-integrated workflow for quantitative transcriptome analysis from NGS Data - In: BMC Bioinformatics. - ISSN 1471-2105 (online). - 12 (2011), suppl. 11, art. A7. - doi:10.1186/1471-2105-12-S11-A7
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Using transfer learning from prior reference knowledge to improve the clustering of single-cell RNA-Seq data.
Funder: Technische Universität Berlin (TU Berlin); doi: https://doi.org/10.13039/501100006764Funder: - German Federal Ministry for Education and Research through the Berlin Big Data Centre (01IS14013A), the Berlin Center for Machine Learning (01IS18037I) and the TraMeExCo project (01IS18056A). Partial funding by DFG is acknowledged (EXC 2046/1, project-ID: 390685689) - Institute for Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No.2017-0-01779)Funder: GlaxoSmithKline (GlaxoSmithKline plc.); doi: https://doi.org/10.13039/100004330Funder: Pfizer (Pfizer Inc.); doi: https://doi.org/10.13039/100004319In many research areas scientists are interested in clustering objects within small datasets while making use of prior knowledge from large reference datasets. We propose a method to apply the machine learning concept of transfer learning to unsupervised clustering problems and show its effectiveness in the field of single-cell RNA sequencing (scRNA-Seq). The goal of scRNA-Seq experiments is often the definition and cataloguing of cell types from the transcriptional output of individual cells. To improve the clustering of small disease- or tissue-specific datasets, for which the identification of rare cell types is often problematic, we propose a transfer learning method to utilize large and well-annotated reference datasets, such as those produced by the Human Cell Atlas. Our approach modifies the dataset of interest while incorporating key information from the larger reference dataset via Non-negative Matrix Factorization (NMF). The modified dataset is subsequently provided to a clustering algorithm. We empirically evaluate the benefits of our approach on simulated scRNA-Seq data as well as on publicly available datasets. Finally, we present results for the analysis of a recently published small dataset and find improved clustering when transferring knowledge from a large reference dataset. Implementations of the method are available at https://github.com/nicococo/scRNA
Ein-Klassen-Klassifikation in der Gegenwart von Punkt-, Kollektiv- und Kontext-Anomalien
Anomaly detection has a prominent position in the processing pipeline of any real-world data-driven application. Its central goal is to detect and separate valid data points from malicious-anomalous-ones such that the cleaned data set can be processed further. In many applications, anomalies are even the prime objects of interest and need to be exposed early in order to avoid loss, e.g. in credit card fraud detection.
One-class classification is a machine learning concept that is especially suited for the anomaly detection problem. Intrinsically unsupervised, it aims at providing a concise description of a given data set such that data points generated by a different process can be detected accurately. Prominent machine learning models for one-class classification are one-class support vector machines and the closely related support vector data descriptions. The contribution of this thesis is the extension of those methods to cope with different scenarios of anomalies:
- Point Anomalies: Assuming that anomalies are scarce and occur independently of each other, methods for controlling the sparsity of the found solutions in terms of single independent features and groups of features are derived.
- Collective Anomalies: In this scenario anomalies are assumed to appear as groups of measurements instead of single entries. Techniques from structured output learning are (i) extended to cope with large-scale problems, (ii) employed to derive an unsupervised anomaly detector for groups of measurements that exhibit a latent dependency structure.
- Contextual Anomalies: Anomalies appear only in specific contexts and data is supposed to carry two signals that contain behavioral and contextual information. Contributions in this scenario consider latent class dependencies and are threefold: (i) the derivation of a method capable of detecting latent class contextual anomalies, (ii) theoretical insight reveal \kmeans as a special case, and (iii) a method for learning with latent class dependencies when an additional structure is imposed on the latent variables.
The proposed methods are empirically analyzed on a variety of different applications ranging from gene finding to porosity estimation to brain computer interfaces showing promising performance when compared to baseline methods.Anomalieerkennung nimmt im Verarbeitungsablauf jeder realen Daten-getriebenen Anwendung eine wichtige Stellung ein. Ihre zentrale Aufgabe ist es gültige Daten zu erkennen und von Ungültigen, anomalen Daten, zu trennen sodass der so bereinigte Datensatz weiter verarbeitet werden kann. In vielen Anwendungen sind sogar die Anomalien die interessantesten Objekte und sollten so früh wie möglich erkannt werden um möglichen Verlusten vorzubeugen wie zum Beispiel bei der Prävention von Kreditkartenbetrug.
Einklassen-Klassifikation ist ein Konzept des Maschinellen Lernens, welches besonders geeignet ist um Anomalien zu detektieren. Es handelt sich um intrinsisch unüberwachte Lernverfahren welche darauf abzielen eine genaue Beschreibung eines gegebenen Datensatzes zu liefern, so dass Datenpunkte, die von einem anderen Prozess erzeugt wurden, akkurat erkannt werden können. Die wichtigsten Vertreter dieser Zunft sind die Einklassen-SVM sowie die mir ihr eng verwandte SVDD.
Diese Arbeit leisten einen Beitrag um diese Methoden so zu erweitern, dass sie mit den folgenden, allgemeinen Anomalieszenarien umgehen können:
- Punktanomalien: Wir nehmen an, dass Anomalien selten sind und unabhängig voneinander auftreten. Wir entwickeln Methoden, welche die Spährlichkeit, die Anzahl der Nullstellen in der Lösung, kontrollieren, basierend dabei auf einzelnen Merkmalen oder Gruppen von Merkmalen.
- Kollektivanomalien: In diesem Szenario wird angenommen, dass Anomalien in Gruppen von Messungen auftreten anstatt als isolierte Einzelmessung. Wir werden Techniken vom Strukturlernen (i) erweitern, um mit grossen Datenmeengen umgehen zu können, und (ii) anwenden um einen unüberwachten Anomalieerkenner für Gruppen von Messungen zu entwickeln, wenn diese Messungen eine latente Abhängigkeitsstruktur besitzen.
- Kontextanomalien: Anomalien erscheinen nur in gewissen Kontext und es wird angenommen, dass Daten aus Verhaltens- und Kontextinformationen bestehen. Beiträge in diesen Szenario beschränken sich auf latente Klassenstruktur und sind dreigeteilt: (i) eine Methode zur Erkennung von Anomalien mit latenter Klassenstruktur wird vorgestellt, (ii) theoretische Einsichten, welche zeigen das \kmeans ein Spezialfall ist, werden vorgestellt, und (iii) eine Methode die mit latenter Klassenstruktur umgehen kann, wenn diese wiederum eine eigene Abhängigkeitsstruktur besitzt, wird entwickelt.
Die vorgestellten Methoden werden empirisch analysiert. Die Anwendungen reichen dabei von Generkennung über Hirn-Computer-Schnittstellen zu Porositätserkennung. Dabei zeigen die vorgestellten Methoden im Vergleich zu Standardmethoden vielversprechende Resultate.BMBF, 01IB10003B, ALICE - Autonomous Learning in Complex EnvironmentsBMBF, 01IB15001B, ALICEII - Autonomous Learning in Complex Environments IIDFG, MU 987/6-1, Lernen mit sequentiellen Date
Toward Supervised Anomaly Detection
Anomaly detection is being regarded as an unsupervised learning task as anomalies stem from adversarial or unlikely events with unknown distributions. However, the predictive performance of purely unsupervised anomaly detection often fails to match the required detection rates in many tasks and there exists a need for labeled data to guide the model generation. Our first contribution shows that classical semi-supervised approaches, originating from a supervised classifier, are inappropriate and hardly detect new and unknown anomalies. We argue that semi-supervised anomaly detection needs to ground on the unsupervised learning paradigm and devise a novel algorithm that meets this requirement. Although being intrinsically non-convex, we further show that the optimization problem has a convex equivalent under relatively mild assumptions. Additionally, we propose an active learning strategy to automatically filter candidates for labeling. In an empirical study on network intrusion detection data, we observe that the proposed learning methodology requires much less labeled data than the state-of-the-art, while achieving higher detection accuracies
ML2Motif-Reliable extraction of discriminative sequence motifs from learning machines.
High prediction accuracies are not the only objective to consider when solving problems using machine learning. Instead, particular scientific applications require some explanation of the learned prediction function. For computational biology, positional oligomer importance matrices (POIMs) have been successfully applied to explain the decision of support vector machines (SVMs) using weighted-degree (WD) kernels. To extract relevant biological motifs from POIMs, the motifPOIM method has been devised and showed promising results on real-world data. Our contribution in this paper is twofold: as an extension to POIMs, we propose gPOIM, a general measure of feature importance for arbitrary learning machines and feature sets (including, but not limited to, SVMs and CNNs) and devise a sampling strategy for efficient computation. As a second contribution, we derive a convex formulation of motifPOIMs that leads to more reliable motif extraction from gPOIMs. Empirical evaluations confirm the usefulness of our approach on artificially generated data as well as on real-world datasets