scispace - formally typeset
Search or ask a question
Author

Hans-Peter Lenhof

Other affiliations: Max Planck Society
Bio: Hans-Peter Lenhof is an academic researcher from Saarland University. The author has contributed to research in topics: Macromolecular docking & Autoantibody. The author has an hindex of 44, co-authored 164 publications receiving 6046 citations. Previous affiliations of Hans-Peter Lenhof include Max Planck Society.


Papers
More filters
Journal ArticleDOI
12 Apr 1999
TL;DR: A master version and a distributed version of the algorithm that is competitive for a relatively large number of processors for dynamic non-bonded interactions in parallel MD simulations of synthetic polymers are devised and compared.
Abstract: We have investigated algorithms that are particularly suited for the parallel MD simulations of synthetic polymers. These algorithms distribute the atoms of the polymer among the processors. Dynamic non-bonded interactions, which are the difficult part of an MD simulation, are realised with the help of a special coarse-grained representation of the chain structure. We have devised and compared a master version and a distributed version of the algorithm. Surprisingly, the master version is competitive for a relatively large number of processors. We also investigated methods to improve load balancing. The resulting simulation package will be made available in the near future.

2 citations

Journal ArticleDOI
TL;DR: In this paper, a novel ILP formulation, called MEthod for Rule Identification with multi-omics DAta (MERIDA), is presented for predicting the drug sensitivity of cancer cells.
Abstract: MOTIVATION A major goal of personalized medicine in oncology is the optimization of treatment strategies given measurements of the genetic and molecular profiles of cancer cells. To further our knowledge on drug sensitivity, machine learning techniques are commonly applied to cancer cell line panels. RESULTS We present a novel integer linear programming (ILP) formulation, called MEthod for Rule Identification with multi-omics DAta (MERIDA), for predicting the drug sensitivity of cancer cells. The method represents a modified version of the LOBICO method by Knijnenburg et al. and yields easily interpretable models amenable to a Boolean logic based interpretation. Since the proposed altered logical rules lead to an enormous acceleration of the running times of MERIDA compared to LOBICO, we can not only consider larger input feature sets integrated from genetic and molecular omics data but also build more comprehensive models that mirror the complexity of cancer initiation and progression. Moreover, we enable the inclusion of a priori knowledge that can either stem from biomarker databases or can also be newly acquired knowledge gathered iteratively by previous runs of MERIDA. Our results show that this approach does not only lead to an improved predictive performance but also identifies a variety of putative sensitivity and resistance biomarkers. We also compare our approach to state-of-the-art machine learning methods and demonstrate the superior performance of our method. Hence, MERIDA has great potential to deepen our understanding of the molecular mechanisms causing drug sensitivity or resistance. AVAILABILITY AND IMPLEMENTATION The corresponding code is available on github (https://github.com/unisb-bioinf/MERIDA.git). SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

2 citations

01 Jan 1997
TL;DR: A parallel algorithm is presented that allows efficient Langevin–dynamics simulations of single macromolecular coils which are the typical structure of synthetic polymers in solution and in bulk.

1 citations

01 Jan 1991
TL;DR: It is shown that the usage of a new construction method for layers reduces the preprocessing time to O(n log n), which provides the first space, query time and pre processing time optimal solution for this class of point retrieval problems.
Abstract: Let P be a set of n points in the Euclidean plane and let C be a convex figure. In 1985, Chazelle and Edelsbrunner presented an algorithm, which preprocesses P such that for any query point q, the points of P in the translate C+q can be retrieved efficiently. Assuming that constant time suffices for deciding the inclusion of a point in C, they provided a space and query time optimal solution. Their algorithm uses O(n) space. A query with output size k can be solved in O(log n+k) time. The preprocessing step of their algorithm, however, has time complexity O(n2). We show that the usage of a new construction method for layers reduces the preprocessing time to O(n log n). We thus provide the first space, query time and preprocessing time optimal solution for this class of point retrieval problems. Besides, we present two new dynamic data structures for these problems. The first dynamic data structure allows on-line insertions and deletions of points in O((log n)2) time. In this dynamic data structure, a query with output size k can be solved in O(log n+k(log n)2) time. The second dynamic data structure, which allows only semi-online updates, has O((log n)2) amortized update time and O(log n+k) query time.

1 citations


Cited by
More filters
Journal ArticleDOI
TL;DR: The survey will help tool designers/developers and experienced end users understand the underlying algorithms and pertinent details of particular tool categories/tools, enabling them to make the best choices for their particular research interests.
Abstract: Functional analysis of large gene lists, derived in most cases from emerging high-throughput genomic, proteomic and bioinformatics scanning approaches, is still a challenging and daunting task. The gene-annotation enrichment analysis is a promising high-throughput strategy that increases the likelihood for investigators to identify biological processes most pertinent to their study. Approximately 68 bioinformatics enrichment tools that are currently available in the community are collected in this survey. Tools are uniquely categorized into three major classes, according to their underlying enrichment algorithms. The comprehensive collections, unique tool classifications and associated questions/issues will provide a more comprehensive and up-to-date view regarding the advantages, pitfalls and recent trends in a simpler tool-class level rather than by a tool-by-tool approach. Thus, the survey will help tool designers/developers and experienced end users understand the underlying algorithms and pertinent details of particular tool categories/tools, enabling them to make the best choices for their particular research interests.

13,102 citations

Journal ArticleDOI
TL;DR: These revisions simplify the McDonald Criteria, preserve their diagnostic sensitivity and specificity, address their applicability across populations, and may allow earlier diagnosis and more uniform and widespread use.
Abstract: New evidence and consensus has led to further revision of the McDonald Criteria for diagnosis of multiple sclerosis. The use of imaging for demonstration of dissemination of central nervous system lesions in space and time has been simplified, and in some circumstances dissemination in space and time can be established by a single scan. These revisions simplify the Criteria, preserve their diagnostic sensitivity and specificity, address their applicability across populations, and may allow earlier diagnosis and more uniform and widespread use.

8,883 citations

Journal ArticleDOI
TL;DR: A new method for multiple sequence alignment that provides a dramatic improvement in accuracy with a modest sacrifice in speed as compared to the most commonly used alternatives but avoids the most serious pitfalls caused by the greedy nature of this algorithm.

6,727 citations

Journal ArticleDOI
TL;DR: A biologist-oriented portal that provides a gene list annotation, enrichment and interactome resource and enables integrated analysis of multi-OMICs datasets, Metascape is an effective and efficient tool for experimental biologists to comprehensively analyze and interpret OMICs-based studies in the big data era.
Abstract: A critical component in the interpretation of systems-level studies is the inference of enriched biological pathways and protein complexes contained within OMICs datasets Successful analysis requires the integration of a broad set of current biological databases and the application of a robust analytical pipeline to produce readily interpretable results Metascape is a web-based portal designed to provide a comprehensive gene list annotation and analysis resource for experimental biologists In terms of design features, Metascape combines functional enrichment, interactome analysis, gene annotation, and membership search to leverage over 40 independent knowledgebases within one integrated portal Additionally, it facilitates comparative analyses of datasets across multiple independent and orthogonal experiments Metascape provides a significantly simplified user experience through a one-click Express Analysis interface to generate interpretable outputs Taken together, Metascape is an effective and efficient tool for experimental biologists to comprehensively analyze and interpret OMICs-based studies in the big data era

6,282 citations

Journal ArticleDOI
TL;DR: A significant update to one of the tools in this domain called Enrichr, a comprehensive resource for curated gene sets and a search engine that accumulates biological knowledge for further biological discoveries is presented.
Abstract: Enrichment analysis is a popular method for analyzing gene sets generated by genome-wide experiments. Here we present a significant update to one of the tools in this domain called Enrichr. Enrichr currently contains a large collection of diverse gene set libraries available for analysis and download. In total, Enrichr currently contains 180 184 annotated gene sets from 102 gene set libraries. New features have been added to Enrichr including the ability to submit fuzzy sets, upload BED files, improved application programming interface and visualization of the results as clustergrams. Overall, Enrichr is a comprehensive resource for curated gene sets and a search engine that accumulates biological knowledge for further biological discoveries. Enrichr is freely available at: http://amp.pharm.mssm.edu/Enrichr.

6,201 citations