WorldCat Identities

Backofen, Rolf

Works: 112 works in 179 publications in 2 languages and 1,578 library holdings
Roles: dgs, Author, Contributor, Other
Publication Timeline
Most widely held works by Rolf Backofen
Computational molecular biology : an introduction by Peter Clote( )

24 editions published between 2000 and 2005 in English and held by 1,065 WorldCat member libraries worldwide

"Primarily aimed at advanced undergraduate and graduate students from bioinformatics, computer science, statistics, mathematics and the biological sciences, this text will also interest researchers from these fields."--Jacket
Expressivity and decidability of first-order languages over feature trees by Rolf Backofen( )

5 editions published between 1994 and 2004 in English and German and held by 29 WorldCat member libraries worldwide

Towards the integration of functions, relations and types in an AI programming language by Rolf Backofen( )

4 editions published between 1991 and 2011 in English and German and held by 20 WorldCat member libraries worldwide

Optimizing algorithms for the comparative analysis of non-coding RNAs by Christina Otto( )

1 edition published in 2015 in English and held by 17 WorldCat member libraries worldwide

Zusammenfassung: Non-coding RNAs (ncRNAs) perform essential functions within the cell, such as the regulation of gene expression or catalytic functionalities. Until today, however, the function of most ncRNA molecules is still unknown. As the structure is key to the function of many ncRNAs, much effort has been devoted to the computational structure prediction of ncRNAs and the subsequent functional characterization. This thesis makes important contributions to this field of research by introducing novel fast methods for revealing the functionalities of ncRNA molecules.The basis of these methods is a novel sparsification technique, the ensemble-based sparsification, which is introduced in the first part of this thesis. Identifying likely structural elements within the structure ensembles of two RNA sequences allows to drastically reduce the search space and leads to a significant shorter runtime. We demonstrate the efficiency of this novel technique for speeding up algorithms for the identification of sequence-structure motifs and simultaneous alignment and folding. However, the applicability of ensemble-based sparsification is not limited to these instances such that this novel technique offers the possibility to speed up other RNA-related tasks in the future as well.In the second part of this thesis, we introduce the novel method ExpaRNA-P for identifying sequence-structure motifs common to two RNAs in entire Boltzmann-distributed structure ensembles. The core algorithm of the existing approach ExpaRNA solves this problem for a priori known input structures. However, such structures are rarely known; moreover, predicting them computationally beforehand is not an option, since single sequence structure prediction is highly unreliable. In our novel approach ExpaRNA-P, we match and fold RNAs simultaneously, analogous to the well-known simultaneous alignment and folding of RNAs. While this implies much higher flexibility compared with ExpaRNA, the novel approach ExpaRNA-P has the same very low complexity (quadratic in time and space), which is enabled by our novel ensemble-based sparsification. Furthermore, we devise a generalized chaining algorithm to compute compatible subsets of ExpaRNA-P's sequence-structure motifs. We utilize the best chain asanchor constraints for the sequence-structure alignment tool LocARNA, resulting in the very fast RNA alignment program ExpLoc-P. ExpLoc-P is benchmarked in several variants and versus state-of-the-art programs. Across a benchmark set of typical ncRNAs, ExpLoc-P has similar accuracy to LocARNA but is on average four times faster, while it achieves a speedup over 30-fold for the longest benchmark sequences (=~400nt).In the third part of this thesis, we present the two novel methods PARSE and SPARSE for simultaneous alignment and folding. PARSE utilizes a lightweight energy model that is derived from a full-featured energy model to score structural contributions. In addition, it integrates Sankoff's original structure prediction flexibility. By utilizing LocARNA's base pair filter, a time complexity of O(n⁴) can be obtained for PARSE. Furthermore, we show how the novel ensemble-based sparsification can be applied to derive the sparsified variant SPARSE with a significantly reduced runtime of O(n²). This means that we introduce the firstmethod with quadratic runtime for simultaneous alignment and folding that does not resort to sequence-based heuristics that could corrupt the alignment quality - as for example the tool RAF does. Furthermore, we demonstrate the effectiveness of our method on benchmarks of real RNA sequences against the state-of-the-art programs LocARNA and RAF. The low computational complexity of SPARSE and RAF is reflected in an overall speedup of around 4 over LocARNA. Whereas RAF's performance drops drastically for instances with low sequence identities, SPARSE benefits from the structure-based optimization and achieves similar alignment quality as LocARNA. Importantly, both tools produce high-quality alignments even for the hard instances with low sequence identity. In addition, we demonstrate the advantage of SPARSE's flexible structure prediction model in comparison with LocARNA. For all sequence identity regions, SPARSE improves LocARNA's structure prediction quality.In the final part of this thesis, we propose a general theory to describe and implement sparsification in dynamic programming (DP) algorithms. So far, sparsification is mostly a collection of loosely related examples and no general, well understood theory has been developed yet. Our approach is formalized as an extension of algebraic dynamic programming (ADP), which makes it applicable to a variety of algorithms and scoring schemes. In particular, this is the first approach that shows how to sparsify algorithms with scoring schemes that go beyond simple minimization or maximization - as for example the enumeration of suboptimal solutions. On the basis of Nussinov's algorithm, we show how to sparsify RNA structure prediction algorithms.In summary, this thesis provides novel approaches to decipher the functionalities of ncRNAs. Particularly, we aim at maintaining high quality output while focusing at the same time on making our novel approaches most efficient regarding the runtime. Moreover, we demonstrate in this work the superior performance of our novel methods compared with state-of-the-art programs on real RNA sequences
Sampling and Approximation in the Context of RNA Secondary Structure Prediction - Algorithms and Studies Based on Stochastic Context-Free Modeling by Anika Schulz( )

1 edition published in 2012 in English and held by 17 WorldCat member libraries worldwide

Strukturelles und funktionelles Verständnis von Membranproteinen im Kontext sequenzmotivbasierter Methoden by Steffen Grunert( )

1 edition published in 2017 in German and held by 17 WorldCat member libraries worldwide

Modelling binding preferences of RNA-binding proteins by Daniel Maticzka( )

1 edition published in 2017 in English and held by 17 WorldCat member libraries worldwide

In Silico Prediction of Modular Domain-Peptide Interactions by Kousik Kundu( )

1 edition published in 2015 in English and held by 17 WorldCat member libraries worldwide

Zusammenfassung: Protein-protein interactions (PPIs) are one of the most essential cellular processes in eukaryotes that control many important biological activities, such as signal transduction, differentiation, growth, cell polarity, apoptosis etc. Many PPIs in cellular signaling are mediated by modular protein domains. Peptide recognition modules (PRMs) are an important subclass of modular protein domains that specifically recognize short linear peptides to facilitate their biological functions. Hence, it is important to understand the intriguing mechanisms by which hundreds of modular domains specifically bind to their target peptides in a complex cellular environment. In recent years, an unprecedented progress has been made in high-throughput technologies to describe the binding specificities of a number of modular protein domain families. Therefore, given the high binding specificity of PRMs, in silico prediction of their cognate partners is of great interest. In the first part of this thesis, we describe the main high-throughput technologies (microarray, phage display etc.) that are widely used for defining the binding specificity of PRMs. Currently, several computational methods have been published for the prediction of domain-peptide interactions. Here, we provide a comprehensive review on these methods and their applications. We also describe the major drawbacks (e.g., linearity problem, peptide alignment problem, data-imbalance problem etc.) of these existing tools that are successfully addressed in our study.In the second part of this thesis, we present three methods for predicting domain-peptide interactions mediated by three diverse PRM families (i.e., SH2, SH3, and PDZ domain). In order to circumvent the linearity problem, our methods use efficient kernel functions, which exploit higher-order dependencies between amino acid positions. For the prediction of SH2-peptide interactions, polynomial kernels are used to train the classifiers. In addition, we show how to handle the data-imbalance problem by using an efficient semi-supervised technique. For the prediction of SH3-peptide interactions, graph kernels are used for training the classifiers. Graph kernel feature representation allows us to include the physico-chemical properties of each amino acid in the peptides, which increases the generalization capacity of the classifier. By using this kernel function, we were able to eliminate the need of an initial peptide alignment, since the alignment of proline-rich peptides targeted by SH3 domains is a hard task and an error-prone alignment can severely affect the predictive performance of the classifier. Moreover, we developed a generative approach for refining the confidence negative data. In the case of PDZ-peptide interactions, we cluster hundreds of PDZ domains from different organisms, i.e., human, mouse, fly, and worm, based on their binding specificity, and build a single comprehensive model for a set of multiple PDZ domains. In this way, we show that the domain coverage can be increased by using an accurate clustering technique. For training the classifier, a Gaussian kernel function is used. Similar to SH2-peptide interactions, a semi-supervised technique was applied to generate high-confidence negative data. In the third part of this thesis, we describe the applications and performance evaluations of our methods. We compared our methods with several other existing tools and achieved a much higher performance, which was measured by sensitivity, specificity, precision, AUC PR, and AUC ROC. Our methods were further evaluated on various experimentally verified datasets and as a predictive result, they outperformed the state-of-the-art approaches. To uncover the novel and biologically relevant interactions, we performed a genome-wide prediction. Furthermore, a term-centric enrichment analysis has been performed to unveil the novel functionalities of the predicted interactions. In the last part of this thesis, we introduce a new and efficient web server, which contains three tools (i.e., SH2PepInt, SH3PepInt, and PDZPepInt), for the prediction of modular domain-peptide interactions. Currently, we offer 51 and 69 single domain models for SH2 and SH3 domains, respectively, and 43 multiple domain models, which cover 227 domains, for PDZ domains across several organisms. In summary, this thesis presents machine learning methods for predicting the binding peptides of three diverse PRM families where the training data was derived from various high-throughput experiments. Most importantly, this thesis addresses the major computational challenges in the field of modular domain-peptide interactions. We offer the largest set of models to date for the prediction of modular domain mediated interactions
Computational characterisation of genomic CRISPR-Cas systems in archaea and bacteria by Omer S Alkhnbashi( )

1 edition published in 2017 in English and held by 16 WorldCat member libraries worldwide

Computational analyses of post-transcriptional regulatory mechanisms = Computergestützte Analysen von post-transkriptionellen regulatorischen Mechanismen by Sita Johanna Saunders( )

1 edition published in 2014 in English and held by 16 WorldCat member libraries worldwide

Predicting small RNA targets in prokaryotes - a challenge beyond the barriers of thermodynamic models by Patrick R Wright( )

1 edition published in 2016 in English and held by 16 WorldCat member libraries worldwide

Computational analysis and prediction of RNA-RNA interactions = Computergestützte Analyse und Vorhersage von RNA-RNA-Interaktionen by Andreas S Richter( )

1 edition published in 2012 in English and held by 16 WorldCat member libraries worldwide

Development and application of ligand-based cheminformatics tools for drug discovery from natural products = Entwicklung und Anwendung von ligandenbasierten Cheminformatik-Programmen für die Identifizierung von Arzneimitteln aus Naturstoffen by Kiran Telukunta( )

1 edition published in 2018 in English and held by 16 WorldCat member libraries worldwide

Abstract: In the drug-discovery identification of small molecules that selectively bind to a biological target from virtually infinite chemical space is a time-consuming crucial step among many other critical steps in drug development. Identified molecules need to possess adequate residence times of drug-target complexes to modulate the function of the target protein and must affect the desirable phenotype. Furthermore, examination of the pharmacological activity of compounds in in vivo studies is required which are characterized by pharmacokinetic and pharmacodynamic properties. Finally, the efficacy of the drug has to be validated in human.<br><br>The huge number of natural products being approved drugs indicates the importance of natural compounds for drug discovery. Genome-mining tools can be applied to identify a substantial number of novel natural products and ligand- or structure-based virtual screening methods will further increase the pace of therapeutic compound discovery. <br><br>The present doctoral thesis focuses on developing cheminformatic tools which aid basic research for lead identification in drug development. The following applications were codeveloped within the scope of this work:<br><br>Tools evaluating existing literature by applying text-mining in natural language processing are becoming an essential part of identifying compound-protein, drug-drug, and protein-protein interactions along with their associations to diseases in literature. PubMedPortable is a framework developed for accessing large-scale biomolecule associated data and bridges the gap between natural language processing components and relation extraction methods by providing a local queryable and searchable instance of the literature.<br>NANPDB annotates thousands of compounds from the Northern African region; StreptomeDB is an updated database of molecules produced by actinobacteria. Both developed libraries contribute significantly to the biologically relevant natural chemical space. Furthermore, provided web services allow for the retrieval of information about a therapeutic application, physicochemical properties, and synthesis routes.<br>Structural elucidation of biosynthetic substances is a hurdle. The developed web tool SeMPI provides a pipeline to identify encoding gene clusters from genomic data and predicts the basic structure of related natural products. <br>DVS offers an algorithm that serves to narrow down the chemical space that has to be screened to identify putative drugs. FragPred provides a solution in another direction by predicting the activity of compounds based on the knowledge of contained active substructures.<br><br>The cheminformatic tools presented in the thesis are useful for creating hypotheses for the discovery of novel drugs to certain diseases. A case study, diabetes mellitus, illustrates these tools and their operation. Starting with finding literature on diabetes mellitus and the identification of existing drugs for treatment, proposes alternative compounds from the presented natural databases. Finally, for the alternative compounds, putative targets are predicted. The manifested drug-discovery cheminformatic tools demonstrate the importance of in silico methods in modern drug discovery
A complete and recursive feature theory by Rolf Backofen( Book )

4 editions published between 1992 and 2011 in English and German and held by 14 WorldCat member libraries worldwide

A first-order axiomatization of the theory of finite trees by Rolf Backofen( Book )

5 editions published between 1995 and 2011 in English and German and held by 12 WorldCat member libraries worldwide

Linking typed feature formalisms and terminological knowledge representation languages in natural language front ends by Rolf Backofen( Book )

4 editions published between 1990 and 2011 in German and English and held by 11 WorldCat member libraries worldwide

Regular path expressions in feature logic by Rolf Backofen( Book )

2 editions published between 1993 and 1995 in German and English and held by 10 WorldCat member libraries worldwide

Schlussbericht der Freiburger Initiative für Systembiologie FRISYS ; Schlussbericht ; (1.10.2006-31.12.2011)( )

2 editions published in 2012 in German and held by 9 WorldCat member libraries worldwide

How to win a game with features by Rolf Backofen( Book )

2 editions published between 1994 and 1996 in German and English and held by 8 WorldCat member libraries worldwide

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Computational molecular biology : an introduction
Alternative Names
Rolf Backofen deutscher Bioinformatiker

Rolf Backofen Duits auteur

Rolf Backofen German bioinformatician and author

Rolf Backofen tysk professor

English (51)

German (13)