Max Planck Institute for Informatics

Saarbrucken, Germany

Max Planck Institute for Informatics

Saarbrucken, Germany

The Max Planck Institute for Informatics is a research institute in computer science with a focus on algorithms and their applications in a broad sense. It hosts fundamental research as well a research for various application domains . It is part of the Max-Planck-Gesellschaft, Germany's largest society for fundamental research.The research institutes of the Max Planck Society have a national and international reputation as “Centres of Excellence” for pure research. The institute consists of five departments and two research groups: The Algorithms and Complexity Department is headed by Prof. Dr. Kurt Mehlhorn, The Computer Vision and Multimodal Computing Department is headed by Prof. Dr. Bernt Schiele, The Department Computational Biology and Applied Algorithmics is headed by Prof. Dr. Thomas Lengauer, Ph.D. The Computer Graphics Department is headed by Prof. Dr. Hans-Peter Seidel The Databases and Information Systems Department is headed by Prof. Dr. Gerhard Weikum Research Group Automation of Logic is headed by Prof. Dr. Christoph Weidenbach The Independent Research Group Computational Genomics and Epidemiology is headed by Dr. Alice McHardy.Previously, it included the following departments: The Programming Logics Department was headed by Prof. Dr. Harald Ganzinger Members of the institute have received various awards. Professor Kurt Mehlhorn and Professor Hans-Peter Seidel received the Gottfried Wilhelm Leibniz Prize, Professor Kurt Mehlhorn and Professor Thomas Lengauer received the Konrad-Zuse-Medal, and in 2004 Professor Harald Ganzinger received the Herbrand Award.The institute, along with the Max Planck Institute for Software Systems , the German Research Centre for Artificial Intelligence and the entire Computer Science department of Saarland University, is involved in the Internationales Begegnungs- und Forschungszentrum für Informatik.The International Max Planck Research School for Computer Science is the graduate school of the MPII and the MPI-SWS. It was founded in 2000 and offers a fully funded PhD-Program in cooperation with Saarland University. Dean is Prof. Dr. Gerhard Weikum. Wikipedia.


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Bringmann K.,Max Planck Institute for Informatics
Proceedings of the Annual ACM-SIAM Symposium on Discrete Algorithms | Year: 2017

Given a set Z of n positive integers and a target value t, the SubsetSum problem asks whether any subset of Z sums to t. A textbook pseudopolynomial time algorithm by Bellman from 1957 solves Subset Sum in time O(n t). This has been improved to O(n maxZ) by Pisinger [J. Algorithms'99] and recently to Õ( p n t) by Koiliaris and Xu [SODA'17]. Here we present a simple and elegant randomized algorithm running in time Õ(n+t). This improves upon a classic algorithm and is likely to be near-optimal, since it matches conditional lower bounds from SetCover and k-Clique. We then use our new algorithm and additional tricks to improve the best known polynomial space solution from time Õ (n3t) and space Õ (n2) to time O (n t) and spaceO (n log t), assuming the Extended Riemann Hypothesis. Unconditionally, we obtain time O(n t1+) and space Õ (n t) for any constant > 0. Copyright © by SIAM.


News Article | May 23, 2017
Site: www.eurekalert.org

(Vienna, May 22, 2017) Two drugs taken together can sometimes lead to outcomes that largely deviate from the effect of the separated compounds - a fact well known from warnings on patient information leaflets. However, while doctors strongly advice against unsupervised mixing of drugs, the synergy of two combined pharmaceuticals assessed in an experimental setting can reveal completely new therapeutic options. Nevertheless, finding a novel combination of drugs for a given disease within the more than 30,000 drug products approved by the regulatory agencies was hitherto a big challenge for scientists. To facilitate systematic screening for synergistic interactions of drugs, CeMM PI Stefan Kubicek and his colleagues established a collection of 308 compounds (CeMM Library of Unique Drugs, CLOUD) that effectively represent the diversity of structures and molecular targets of all FDA-approved chemical entities. Moreover, the scientists proved the potential of the CLOUD with CeMM´s highly automated chemical screening platform by identifying a novel synergistic effect of two drugs (flutamide and phenprocoumon (PPC)) on prostate cancer cells. The results of Kubicek´s team with Marco Licciardello as first author were published in Nature Chemical Biology (DOI:10.1038/nchembio.2382) For the establishment of the CLOUD, a clever series of condensation steps was necessary: the CeMM scientists first determined and extracted 2171 unique active pharmaceutical ingredients from the database, discarding all products with identical compounds. Next, they removed large macromolecules like antibodies as well as salt fragments, and discarded all molecules that exert their biological effects through mechanisms other than protein-ligand interactions, are not used to treat diseases or are found only in topical products. With the remaining 954 systemically active small molecules (STEAM collection), the work had just begun: in order to create a comprehensive collection of compounds that fits on a standard 384-well screening plate, the researchers appended biological activities to all drugs with known molecular targets and grouped them into 176 classes of similar structure and activity. With a sophisticated clustering algorithm, 239 representative drugs were selected from those classes. Combined with 34 drugs with unknown target and 35 active forms of prodrugs (that otherwise need to be metabolized to become active), 308 compounds were selected in total for the CLOUD - the world´s first library representing all FDA-approved chemical entities including the active form of prodrugs. To put the combinatorial screen with the CLOUD to the test, Kubicek's group investigated the effect of pairwise combinations of CLOUD compounds on the viability on KBM7 leukemia cells, a cell line well suited for drug experiments. Using a dose chosen for each compound individually based on the clinically relevant maximum plasma concentration, the scientists found a strong synergistic interaction between flutamide, a drug approved for the treatment of prostate cancer, and phenprocoumon (PPC), an anti-thrombosis compound. In combination, flutamide and PPC efficiently killed the cancer cells. After identifying the androgen receptor (AR) as molecular target of the synergistic interaction, the scientists tried the drug combination on prostate cancer cells known to be hard to treat - and hit the bulls eye. "The combination induced massive cell death in prostate cancer cells. We then went back to the entire approved drug list, and indeed, we could show that all drugs from the clusters that flutamide and phenprocoumon represent synergize. Thereby we validated the reductionist concept underlying the CLOUD library," Stefan Kubicek explains. With their experiments, Kubicek´s team in collaboration with scientists from the Medical University of Vienna, the Uppsala University, Enamine Kiev and the Max Planck Institute for Informatics in Saarbrücken proved that the CLOUD is the ideal set of compounds to develop screening assays and discover new applications for approved active ingredients. At CeMM, a number of key discoveries on new applications for approved drugs have already been made with the CLOUD. Furthermore, as shown in the current issue of Nature Chemical Biology, the CLOUD is ideal for finding new drug combinations. "In view of these successes, I would predict that this set of compounds will become world standard for all screening campaigns", Stefan Kubicek emphasizes. Attached pictures: 1) Schematic representation of the filtering and clustering procedure leading to the 308 CLOUD drugs (© Nature Chemical Biology / Stefan Kubicek), 2) Immunofluorescence analysis of prostate cancer cells treated with 15mM flutamide, 35 μM PPC or the combination for 24 h. Scale Bar 20 μM (© Nature Chemical Biology / Stefan Kubicek) 3) Senior author Stefan Kubicek (© CeMM/Sazel) The study "A combinatorial screen of the CLOUD uncovers a synergy targeting the androgen receptor" was published online in advance in Nature Chemical Biology on May 22, 2017. DOI:10.1038/nchembio.2382 The study was funded by a Marie Curie Career Integration Grant, the Austrian Federal Ministry of Science, Research and Economy, the National Foundation for Research, Technology, and Development and the Austrian Science Fund (FWF). Stefan Kubicek studied organic chemistry in Vienna and Zürich. He received his Ph.D. in Thomas Jenuwein's group at the Institute for Molecular Pathology (IMP) in Vienna followed by postdoctoral work with Stuart Schreiber at the Broad Institute of Harvard and MIT in the U.S. He joined CeMM in 2010. He is the Head of the Chemical Screening at CeMM and the Platform Austria for Chemical Biology (PLACEBO) and the Christian Doppler Laboratory for Chemical Epigenetics and Anti-Infectives. The mission of CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences is to achieve maximum scientific innovation in molecular medicine to improve healthcare. At CeMM, an international and creative team of scientists and medical doctors pursues free-minded basic life science research in a large and vibrant hospital environment of outstanding medical tradition and practice. CeMM's research is based on post-genomic technologies and focuses on societally important diseases, such as immune disorders and infections, cancer and metabolic disorders. CeMM operates in a unique mode of super-cooperation, connecting biology with medicine, experiments with computation, discovery with translation, and science with society and the arts. The goal of CeMM is to pioneer the science that nurtures the precise, personalized, predictive and preventive medicine of the future. CeMM trains a modern blend of biomedical scientists and is located at the campus of the General Hospital and the Medical University of Vienna. http://www. For further information please contact Mag. Wolfgang Däuble Media Relations Manager CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences Lazarettgasse 14, AKH BT 25.3 1090 Vienna, Austria Phone +43-1/40160-70 057 Fax +43-1/40160-970 000 wdaeuble@cemm.oeaw.ac.at http://www.


News Article | May 25, 2017
Site: www.sciencedaily.com

Two drugs taken together can sometimes lead to outcomes that largely deviate from the effect of the separated compounds -- a fact well known from warnings on patient information leaflets. However, while doctors strongly advice against unsupervised mixing of drugs, the synergy of two combined pharmaceuticals assessed in an experimental setting can reveal completely new therapeutic options. Nevertheless, finding a novel combination of drugs for a given disease within the more than 30,000 drug products approved by the regulatory agencies was hitherto a big challenge for scientists. To facilitate systematic screening for synergistic interactions of drugs, CeMM PI Stefan Kubicek and his colleagues established a collection of 308 compounds (CeMM Library of Unique Drugs, CLOUD) that effectively represent the diversity of structures and molecular targets of all FDA-approved chemical entities. Moreover, the scientists proved the potential of the CLOUD with CeMM´s highly automated chemical screening platform by identifying a novel synergistic effect of two drugs (flutamide and phenprocoumon (PPC)) on prostate cancer cells. The results of Kubicek´s team with Marco Licciardello as first author were published in Nature Chemical Biology. For the establishment of the CLOUD, a clever series of condensation steps was necessary: the CeMM scientists first determined and extracted 2171 unique active pharmaceutical ingredients from the database, discarding all products with identical compounds. Next, they removed large macromolecules like antibodies as well as salt fragments, and discarded all molecules that exert their biological effects through mechanisms other than protein-ligand interactions, are not used to treat diseases or are found only in topical products. With the remaining 954 systemically active small molecules (STEAM collection), the work had just begun: in order to create a comprehensive collection of compounds that fits on a standard 384-well screening plate, the researchers appended biological activities to all drugs with known molecular targets and grouped them into 176 classes of similar structure and activity. With a sophisticated clustering algorithm, 239 representative drugs were selected from those classes. Combined with 34 drugs with unknown target and 35 active forms of prodrugs (that otherwise need to be metabolized to become active), 308 compounds were selected in total for the CLOUD -- the world´s first library representing all FDA-approved chemical entities including the active form of prodrugs. To put the combinatorial screen with the CLOUD to the test, Kubicek's group investigated the effect of pairwise combinations of CLOUD compounds on the viability on KBM7 leukemia cells, a cell line well suited for drug experiments. Using a dose chosen for each compound individually based on the clinically relevant maximum plasma concentration, the scientists found a strong synergistic interaction between flutamide, a drug approved for the treatment of prostate cancer, and phenprocoumon (PPC), an anti-thrombosis compound. In combination, flutamide and PPC efficiently killed the cancer cells. After identifying the androgen receptor (AR) as molecular target of the synergistic interaction, the scientists tried the drug combination on prostate cancer cells known to be hard to treat -- and hit the bulls eye. "The combination induced massive cell death in prostate cancer cells. We then went back to the entire approved drug list, and indeed, we could show that all drugs from the clusters that flutamide and phenprocoumon represent synergize. Thereby we validated the reductionist concept underlying the CLOUD library," Stefan Kubicek explains. With their experiments, Kubicek´s team in collaboration with scientists from the Medical University of Vienna, the Uppsala University, Enamine Kiev and the Max Planck Institute for Informatics in Saarbrücken proved that the CLOUD is the ideal set of compounds to develop screening assays and discover new applications for approved active ingredients. At CeMM, a number of key discoveries on new applications for approved drugs have already been made with the CLOUD. Furthermore, as shown in the current issue of Nature Chemical Biology, the CLOUD is ideal for finding new drug combinations. "In view of these successes, I would predict that this set of compounds will become world standard for all screening campaigns," Stefan Kubicek emphasizes.


News Article | May 24, 2017
Site: www.chromatographytechniques.com

Two drugs taken together can sometimes lead to outcomes that largely deviate from the effect of the separated compounds - a fact well known from warnings on patient information leaflets. However, while doctors strongly advice against unsupervised mixing of drugs, the synergy of two combined pharmaceuticals assessed in an experimental setting can reveal completely new therapeutic options. Nevertheless, finding a novel combination of drugs for a given disease within the more than 30,000 drug products approved by the regulatory agencies was hitherto a big challenge for scientists. To facilitate systematic screening for synergistic interactions of drugs, CeMM PI Stefan Kubicek and his colleagues established a collection of 308 compounds (CeMM Library of Unique Drugs, CLOUD) that effectively represent the diversity of structures and molecular targets of all FDA-approved chemical entities. Moreover, the scientists proved the potential of the CLOUD with CeMM´s highly automated chemical screening platform by identifying a novel synergistic effect of two drugs (flutamide and phenprocoumon (PPC)) on prostate cancer cells. The results of Kubicek´s team with Marco Licciardello as first author were published in Nature Chemical Biology. For the establishment of the CLOUD, a clever series of condensation steps was necessary: the CeMM scientists first determined and extracted 2171 unique active pharmaceutical ingredients from the database, discarding all products with identical compounds. Next, they removed large macromolecules like antibodies as well as salt fragments, and discarded all molecules that exert their biological effects through mechanisms other than protein-ligand interactions, are not used to treat diseases or are found only in topical products. With the remaining 954 systemically active small molecules (STEAM collection), the work had just begun: in order to create a comprehensive collection of compounds that fits on a standard 384-well screening plate, the researchers appended biological activities to all drugs with known molecular targets and grouped them into 176 classes of similar structure and activity. With a sophisticated clustering algorithm, 239 representative drugs were selected from those classes. Combined with 34 drugs with unknown target and 35 active forms of prodrugs (that otherwise need to be metabolized to become active), 308 compounds were selected in total for the CLOUD - the world´s first library representing all FDA-approved chemical entities including the active form of prodrugs. To put the combinatorial screen with the CLOUD to the test, Kubicek's group investigated the effect of pairwise combinations of CLOUD compounds on the viability on KBM7 leukemia cells, a cell line well suited for drug experiments. Using a dose chosen for each compound individually based on the clinically relevant maximum plasma concentration, the scientists found a strong synergistic interaction between flutamide, a drug approved for the treatment of prostate cancer, and phenprocoumon (PPC), an anti-thrombosis compound. In combination, flutamide and PPC efficiently killed the cancer cells. After identifying the androgen receptor (AR) as molecular target of the synergistic interaction, the scientists tried the drug combination on prostate cancer cells known to be hard to treat - and hit the bulls eye. "The combination induced massive cell death in prostate cancer cells. We then went back to the entire approved drug list, and indeed, we could show that all drugs from the clusters that flutamide and phenprocoumon represent synergize. Thereby we validated the reductionist concept underlying the CLOUD library," Stefan Kubicek explains. With their experiments, Kubicek´s team in collaboration with scientists from the Medical University of Vienna, the Uppsala University, Enamine Kiev and the Max Planck Institute for Informatics in Saarbrücken proved that the CLOUD is the ideal set of compounds to develop screening assays and discover new applications for approved active ingredients. At CeMM, a number of key discoveries on new applications for approved drugs have already been made with the CLOUD. Furthermore, as shown in the current issue of Nature Chemical Biology, the CLOUD is ideal for finding new drug combinations. "In view of these successes, I would predict that this set of compounds will become world standard for all screening campaigns", Stefan Kubicek emphasizes.


News Article | May 23, 2017
Site: phys.org

Two drugs taken together can sometimes lead to outcomes that largely deviate from the effect of the separated compounds - a fact well known from warnings on patient information leaflets. However, while doctors strongly advice against unsupervised mixing of drugs, the synergy of two combined pharmaceuticals assessed in an experimental setting can reveal completely new therapeutic options. Nevertheless, finding a novel combination of drugs for a given disease within the more than 30,000 drug products approved by the regulatory agencies was hitherto a big challenge for scientists. To facilitate systematic screening for synergistic interactions of drugs, CeMM PI Stefan Kubicek and his colleagues established a collection of 308 compounds (CeMM Library of Unique Drugs, CLOUD) that effectively represent the diversity of structures and molecular targets of all FDA-approved chemical entities. Moreover, the scientists proved the potential of the CLOUD with CeMM´s highly automated chemical screening platform by identifying a novel synergistic effect of two drugs (flutamide and phenprocoumon (PPC)) on prostate cancer cells. The results of Kubicek´s team with Marco Licciardello as first author were published in Nature Chemical Biology. For the establishment of the CLOUD, a clever series of condensation steps was necessary: the CeMM scientists first determined and extracted 2171 unique active pharmaceutical ingredients from the database, discarding all products with identical compounds. Next, they removed large macromolecules like antibodies as well as salt fragments, and discarded all molecules that exert their biological effects through mechanisms other than protein-ligand interactions, are not used to treat diseases or are found only in topical products. With the remaining 954 systemically active small molecules (STEAM collection), the work had just begun: in order to create a comprehensive collection of compounds that fits on a standard 384-well screening plate, the researchers appended biological activities to all drugs with known molecular targets and grouped them into 176 classes of similar structure and activity. With a sophisticated clustering algorithm, 239 representative drugs were selected from those classes. Combined with 34 drugs with unknown target and 35 active forms of prodrugs (that otherwise need to be metabolized to become active), 308 compounds were selected in total for the CLOUD - the world´s first library representing all FDA-approved chemical entities including the active form of prodrugs. To put the combinatorial screen with the CLOUD to the test, Kubicek's group investigated the effect of pairwise combinations of CLOUD compounds on the viability on KBM7 leukemia cells, a cell line well suited for drug experiments. Using a dose chosen for each compound individually based on the clinically relevant maximum plasma concentration, the scientists found a strong synergistic interaction between flutamide, a drug approved for the treatment of prostate cancer, and phenprocoumon (PPC), an anti-thrombosis compound. In combination, flutamide and PPC efficiently killed the cancer cells. After identifying the androgen receptor (AR) as molecular target of the synergistic interaction, the scientists tried the drug combination on prostate cancer cells known to be hard to treat - and hit the bulls eye. "The combination induced massive cell death in prostate cancer cells. We then went back to the entire approved drug list, and indeed, we could show that all drugs from the clusters that flutamide and phenprocoumon represent synergize. Thereby we validated the reductionist concept underlying the CLOUD library," Stefan Kubicek explains. With their experiments, Kubicek´s team in collaboration with scientists from the Medical University of Vienna, the Uppsala University, Enamine Kiev and the Max Planck Institute for Informatics in Saarbrücken proved that the CLOUD is the ideal set of compounds to develop screening assays and discover new applications for approved active ingredients. At CeMM, a number of key discoveries on new applications for approved drugs have already been made with the CLOUD. Furthermore, as shown in the current issue of Nature Chemical Biology, the CLOUD is ideal for finding new drug combinations. "In view of these successes, I would predict that this set of compounds will become world standard for all screening campaigns", Stefan Kubicek emphasizes. More information: Marco P Licciardello et al, A combinatorial screen of the CLOUD uncovers a synergy targeting the androgen receptor, Nature Chemical Biology (2017). DOI: 10.1038/nchembio.2382


News Article | May 24, 2017
Site: www.chromatographytechniques.com

Two drugs taken together can sometimes lead to outcomes that largely deviate from the effect of the separated compounds - a fact well known from warnings on patient information leaflets. However, while doctors strongly advice against unsupervised mixing of drugs, the synergy of two combined pharmaceuticals assessed in an experimental setting can reveal completely new therapeutic options. Nevertheless, finding a novel combination of drugs for a given disease within the more than 30,000 drug products approved by the regulatory agencies was hitherto a big challenge for scientists. To facilitate systematic screening for synergistic interactions of drugs, CeMM PI Stefan Kubicek and his colleagues established a collection of 308 compounds (CeMM Library of Unique Drugs, CLOUD) that effectively represent the diversity of structures and molecular targets of all FDA-approved chemical entities. Moreover, the scientists proved the potential of the CLOUD with CeMM´s highly automated chemical screening platform by identifying a novel synergistic effect of two drugs (flutamide and phenprocoumon (PPC)) on prostate cancer cells. The results of Kubicek´s team with Marco Licciardello as first author were published in Nature Chemical Biology. For the establishment of the CLOUD, a clever series of condensation steps was necessary: the CeMM scientists first determined and extracted 2171 unique active pharmaceutical ingredients from the database, discarding all products with identical compounds. Next, they removed large macromolecules like antibodies as well as salt fragments, and discarded all molecules that exert their biological effects through mechanisms other than protein-ligand interactions, are not used to treat diseases or are found only in topical products. With the remaining 954 systemically active small molecules (STEAM collection), the work had just begun: in order to create a comprehensive collection of compounds that fits on a standard 384-well screening plate, the researchers appended biological activities to all drugs with known molecular targets and grouped them into 176 classes of similar structure and activity. With a sophisticated clustering algorithm, 239 representative drugs were selected from those classes. Combined with 34 drugs with unknown target and 35 active forms of prodrugs (that otherwise need to be metabolized to become active), 308 compounds were selected in total for the CLOUD - the world´s first library representing all FDA-approved chemical entities including the active form of prodrugs. To put the combinatorial screen with the CLOUD to the test, Kubicek's group investigated the effect of pairwise combinations of CLOUD compounds on the viability on KBM7 leukemia cells, a cell line well suited for drug experiments. Using a dose chosen for each compound individually based on the clinically relevant maximum plasma concentration, the scientists found a strong synergistic interaction between flutamide, a drug approved for the treatment of prostate cancer, and phenprocoumon (PPC), an anti-thrombosis compound. In combination, flutamide and PPC efficiently killed the cancer cells. After identifying the androgen receptor (AR) as molecular target of the synergistic interaction, the scientists tried the drug combination on prostate cancer cells known to be hard to treat - and hit the bulls eye. "The combination induced massive cell death in prostate cancer cells. We then went back to the entire approved drug list, and indeed, we could show that all drugs from the clusters that flutamide and phenprocoumon represent synergize. Thereby we validated the reductionist concept underlying the CLOUD library," Stefan Kubicek explains. With their experiments, Kubicek´s team in collaboration with scientists from the Medical University of Vienna, the Uppsala University, Enamine Kiev and the Max Planck Institute for Informatics in Saarbrücken proved that the CLOUD is the ideal set of compounds to develop screening assays and discover new applications for approved active ingredients. At CeMM, a number of key discoveries on new applications for approved drugs have already been made with the CLOUD. Furthermore, as shown in the current issue of Nature Chemical Biology, the CLOUD is ideal for finding new drug combinations. "In view of these successes, I would predict that this set of compounds will become world standard for all screening campaigns", Stefan Kubicek emphasizes.


Kratsch S.,University Utrecht | Wahlstrom M.,Max Planck Institute for Informatics
Proceedings - Annual IEEE Symposium on Foundations of Computer Science, FOCS | Year: 2012

The existence of a polynomial kernel for Odd Cycle Transversal was a notorious open problem in parameterized complexity. Recently, this was settled by the present authors (Kratsch and Wahlströom, SODA 2012), with a randomized polynomial kernel for the problem, using matroid theory to encode flow questions over a set of terminals in size polynomial in the number of terminals (rather than the total graph size, which may be superpolynomially larger). In the current work we further establish the usefulness of matroid theory to kernelization by showing applications of a result on representative sets due to Lovász (Combinatorial Surveys 1977) and Marx (TCS 2009). We show how representative sets can be used to give a polynomial kernel for the elusive Almost 2-sat problem (where the task is to remove at most k clauses to make a 2-CNF formula satisfiable), solving a major open problem in kernelization. We further apply the representative sets tool to the problem of finding irrelevant vertices in graph cut problems, that is, vertices which can be made undeletable without affecting the status of the problem. This gives the first significant progress towards a polynomial kernel for the Multiway Cut problem, in particular, we get a polynomial kernel for Multiway Cut instances with a bounded number of terminals. Both these kernelization results have significant spin-off effects, producing the first polynomial kernels for a range of related problems. More generally, the irrelevant vertex results have implications for covering min-cuts in graphs. In particular, given a directed graph and a set of terminals, we can find a set of size polynomial in the number of terminals (a cut-covering set) which contains a minimum vertex cut for every choice of sources and sinks from the terminal set. Similarly, given an undirected graph and a set of terminals, we can find a set of vertices, of size polynomial in the number of terminals, which contains a minimum multiway cut for every partition of the terminals into a bounded number of sets. Both results are polynomial time. We expect this to have further applications, in particular, we get direct, reduction rule-based kernelizations for all problems above, in contrast to the indirect compression-based kernel previously given for Odd Cycle Transversal. All our results are randomized, with failure probabilities which can be made exponentially small in the size of the input, due to needing a representation of a matroid to apply the representative sets tool. © 2012 IEEE.


Bock C.,Austrian Academy of Sciences | Bock C.,Medical University of Vienna | Lengauer T.,Max Planck Institute for Informatics
Nature Reviews Cancer | Year: 2012

Drug resistance is a common cause of treatment failure for HIV infection and cancer. The high mutation rate of HIV leads to genetic heterogeneity among viral populations and provides the seed from which drug-resistant clones emerge in response to therapy. Similarly, most cancers are characterized by extensive genetic, epigenetic, transcriptional and cellular diversity, and drug-resistant cancer cells outgrow their non-resistant peers in a process of somatic evolution. Patient-specific combination of antiviral drugs has emerged as a powerful approach for treating drug-resistant HIV infection, using genotype-based predictions to identify the best matched combination therapy among several hundred possible combinations of HIV drugs. In this Opinion article, we argue that HIV therapy provides a 'blueprint' for designing and validating patient-specific combination therapies in cancer. © 2012 Macmillan Publishers Limited. All rights reserved.


Bringmann K.,Max Planck Institute for Informatics
Proceedings - Annual IEEE Symposium on Foundations of Computer Science, FOCS | Year: 2014

The Fréchet distance is a well-studied and very popular measure of similarity of two curves. Many variants and extensions have been studied since Alt and Godau introduced this measure to computational geometry in 1991. Their original algorithm to compute the Fréchet distance of two polygonal curves with n vertices has a runtime of O(n2 log n). More than 20 years later, the state of the art algorithms for most variants still take time more than O(n2/log n), but no matching lower bounds are known, not even under reasonable complexity theoretic assumptions. To obtain a conditional lower bound, in this paper we assume the Strong Exponential Time Hypothesis or, more precisely, that there is no O∗((2-δ)N) algorithm for CNF-SAT for any delta > 0. Under this assumption we show that the Fréchet distance cannot be computed in strongly subquadratic time, i.e., in time O(n2-δ) for any delta > 0. This means that finding faster algorithms for the Fréchet distance is as hard as finding faster CNF-SAT algorithms, and the existence of a strongly subquadratic algorithm can be considered unlikely. Our result holds for both the continuous and the discrete Fréchet distance. We extend the main result in various directions. Based on the same assumption we (1) show non-existence of a strongly subquadratic 1.001-approximation, (2) present tight lower bounds in case the numbers of vertices of the two curves are imbalanced, and (3) examine realistic input assumptions (c-packed curves). © 2014 IEEE.


Lawyer G.,Max Planck Institute for Informatics
Scientific Reports | Year: 2015

Centrality measures such as the degree, k-shell, or eigenvalue centrality can identify a network's most influential nodes, but are rarely usefully accurate in quantifying the spreading power of the vast majority of nodes which are not highly influential. The spreading power of all network nodes is better explained by considering, from a continuous-time epidemiological perspective, the distribution of the force of infection each node generates. The resulting metric, the expected force, accurately quantifies node spreading power under all primary epidemiological models across a wide range of archetypical human contact networks. When node power is low, influence is a function of neighbor degree. As power increases, a node's own degree becomes more important. The strength of this relationship is modulated by network structure, being more pronounced in narrow, dense networks typical of social networking and weakening in broader, looser association networks such as the Internet. The expected force can be computed independently for individual nodes, making it applicable for networks whose adjacency matrix is dynamic, not well specified, or overwhelmingly large.

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