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Emergent non-Markovianity and dynamical quantification of the quantum switch

Physical Review A 2025年第3期111卷 032428-032428页

作者： Vishal Anand Ananda G. Maity Subhadip Mitra Samyadeb Bhattacharya Center for Security Theory and Algorithmic Research International Institute of Information Technology Gachibowli Hyderabad 500032 India Center for Computational Natural Sciences and Bioinformatics International Institute of Information Technology Gachibowli Hyderabad 500032 India Center for Quantum Science and Technology International Institute of Information Technology Gachibowli Hyderabad 500032 India

We investigate the dynamical aspects of the quantum switch and find a particular form of quantum memory emerging out of the switch action. We first analyze the loss of information in a general quantum evolution subjected to a quantum switch and propose a measure to quantify the switch-induced memory. We then derive an uncertainty relation between information loss and switch-induced memory. We explicitly consider the example of depolarizing dynamics and show how it is affected by the action of a quantum switch. For a more detailed analysis, we consider both the control qubit and the final measurement on the control qubit as noisy and investigate the said uncertainty relation. Further, while deriving the Lindblad-type dynamics for the reduced operation of the switch action, we identify that the switch-induced memory actually leads to the emergence of non-Markovianity. Interestingly, we demonstrate that the emergent non-Markovianity can be explicitly attributed to the switch operation by comparing it with other standard measures of non-Markovianity. Our investigation thus paves the way forward to understanding the quantum switch as an emerging non-Markovian quantum memory.

关键词： Qubits

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Swiftly Identifying Strongly Unique k-Mers 24

Swiftly Identifying Strongly Unique k-Mers

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24th International Workshop on Algorithms in bioinformatics, WABI 2024

作者： Zentgraf, Jens Rahmann, Sven Algorithmic Bioinformatics Department of Computer Science Saarland University Saarbrücken Germany Center for Bioinformatics Saar Saarland Informatics Campus Saarbrücken Germany Graduate School of Computer Science Saarland Informatics Campus Saarbrücken Germany

ISBN: (纸本)9783959773409

Motivation. Short DNA sequences of length k that appear in a single location (e.g., at a single genomic position, in a single species from a larger set of species, etc.) are called unique k-mers. They are useful for placing sequenced DNA fragments at the correct location without computing alignments and without ambiguity. However, they are not necessarily robust: A single basepair change may turn a unique k-mer into a different one that may in fact be present at one or more different locations, which may give confusing or contradictory information when attempting to place a read by its k-mer content. A more robust concept are strongly unique k-mers, i.e., unique k-mers for which no Hamming-distance-1 neighbor with conflicting information exists in all of the considered sequences. Given a set of k-mers, it is therefore of interest to have an efficient method that can distinguish k-mers with a Hamming-distance-1 neighbor in the collection from those that do not. Results. We present engineered algorithms to identify and mark within a set K of (canonical) k-mers all elements that have a Hamming-distance-1 neighbor in the same set. One algorithm is based on recursively running a 4-way comparison on sub-intervals of the sorted set. The other algorithm is based on bucketing and running a pairwise bit-parallel Hamming distance test on small buckets of the sorted set. Both methods consider canonical k-mers (i.e., taking reverse complements into account) and allow for efficient parallelization. The methods have been implemented and applied in practice to sets consisting of several billions of k-mers. An optimized combined approach running with 16 threads on a 16-core workstation, yields wall-clock running times below 20 seconds on the 2.5 billion distinct 31-mers of the human telomere-to-telomere reference genome. Availability. An implementation can be found at https://***/rahmannlab/strong-kmers. © Jens Zentgraf and Sven Rahmann

关键词： algorithm engineering Hamming distance k-mer parallelization strong uniqueness

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Enumerating common molecular substructures

Enumerating common molecular substructures

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2017 German Conference on bioinformatics, GCB 2017

作者： Engler, Martin S. El-Kebir, Mohammed Mulder, Jelmer Mark, Alan E. Geerke, Daan P. Klau, Gunnar W. Amsterdam Netherlands Princeton University NJ United States VU University Amsterdam Netherlands University of Queensland Australia Algorithmic Bioinformatics Heinrich Heine University Düsseldorf Germany

Finding and enumerating common molecular substructures is an important task in cheminformatics, where small molecules are often modeled as molecular graphs. We introduce the problem of enumerating all maximal k-common molecular fragments of a pair of molecular graphs. A k-common fragment is a common connected induced subgraph that consists of a common core and a common k-neighborhood. It is thus a generalization of the NP-hard task to enumerate all maximal common connected induced subgraphs (MCCIS) of two graphs, which corresponds to the k = 0 case. We extend the MCCIS enumeration algorithm by Ina Koch and apply algorithm engineering techniques to solve practical instances fast for the general k > 0 case, which is relevant, for example, for automatically generating force field topologies for molecular dynamics (MD) simulations. We find that our methods achieve good performance on a real-world benchmark of all-against-all comparisons of 255 molecules. Our software is available under the LGPL open source license at https://***/enitram/mogli. © 2017 German Conference on bioinformatics, GCB 2017. All rights reserved.

关键词： Molecular dynamics

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Predicting Comorbidities of Epilepsy Patients Using Big Data from Electronic Health Records Combined With Biomedical Knowledge

Predicting Comorbidities of Epilepsy Patients Using Big Data...

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2017 German Conference on bioinformatics, GCB 2017

作者： Gerlach, Thomas Lu, Chao Fröhlich, Holger UCB Biosciences GmbH Monheim Germany University of Bonn Bonn-Aachen International Center for IT Life Sciene DataAnalytics and Algorithmic Bioinformatics Germany UCB Ltd. Raleigh United States

Epilepsy is a complex brain disorder characterized by repetitive seizure events. Epilepsy patients often suffer from various and severe physical and psychological comorbidities. While general comorbidity prevalence and incidences can be estimated from epidemiological data, such an approach does not take into account that actual patient specific risks can depend on various individual factors, including medication. This motivates to develop a machine learning approach for predicting individual comorbidities. To address these needs we used Big Data from electronic health records (100 Million raw observations), which provide a time resolved view on an individual's disease and medication history. A specific contribution of this work is an integration of these data with information from 14 biomedical sources (DisGeNET, TTD, KEGG, Wiki Pathways, DrugBank, SIDER, Gene Ontology, Human Protein Atlas, ...) to capture putative biological effects of observed diseases and applied medications. In consequence we extracted >165,000 features describing the longitudinal patient journey of >10,000 adult epilepsy patients. We used maximum-relevance-minimum-redundancy feature selection in combination with Random Survival Forests (RSF) for predicting the risk of 9 major comorbidities after first epilepsy diagnosis with high cross-validated C-indices of 76-89% and analyzed the influence of medications on the risk to develop specific comorbidities. Altogether we see our work as a first step towards earlier detection and better prevention of common comorbidities of epilepsy patients. Supplementary material: https://***/file/d/0B4OhgVPeWvGTeUNFQVJLai1HRlk/view?usp=sharing, code: https://***/thomasmooon/GCB2017. © 2017 German Conference on bioinformatics, GCB 2017. All rights reserved.

关键词： Big data

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Reconstructing consensus bayesian network structures with application to learning molecular interaction networks

Reconstructing consensus bayesian network structures with ap...

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2013 German Conference on bioinformatics, GCB 2013

作者： Fröhlich, Holger Klau, Gunnar W. Bonn-Aachen International Center for IT Algorithmic Bioinformatics University of Bonn Dahlmannstr. 2 53113 Bonn Germany Life Sciences Science Park 123 1098 XG Amsterdam Netherlands

ISBN: (纸本)9783939897590

Bayesian Networks are an established computational approach for data driven network inference. However, experimental data is limited in its availability and corrupted by noise. This leads to an unavoidable uncertainty about the correct network structure. Thus sampling or bootstrap based strategies are applied to obtain edge frequencies. In a more general sense edge frequencies can also result from integrating networks learned on different datasets or via different inference algorithms. Subsequently one typically wants to derive a biological interpretation from the results in terms of a consensus network. We here propose a log odds based edge score on the basis of the expected false positive rate and thus avoid the selection of a subjective edge frequency cutoff. Computing a score optimal consensus network in our new model amounts to solving the maximum weight acyclic subdigraph problem. We use a branch-and-cut algorithm based on integer linear programming for this task. Our empirical studies on simulated and real data demonstrate a consistently improved network reconstruction accuracy compared to two threshold based strategies.

关键词： Reverse engineering

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The longest run subsequence problem 20

The longest run subsequence problem

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20th International Workshop on Algorithms in bioinformatics, WABI 2020

作者： Schrinner, Sven Goel, Manish Wulfert, Michael Spohr, Philipp Schneeberger, Korbinian Klau, Gunnar W. Algorithmic Bioinformatics Heinrich Heine University Düsseldorf Germany Max Planck Institute for Plant Breeding Research Cologne Germany Heinrich Heine University Düsseldorf Germany Faculty of Biology LMU Munich Planegg-Martinsried Germany

ISBN: (纸本)9783959771610

Genome assembly is one of the most important problems in computational genomics. Here, we suggest addressing the scaffolding phase, in which contigs need to be linked and ordered to obtain larger pseudo-chromosomes, by means of a second incomplete assembly of a related species. The idea is to use alignments of binned regions in one contig to find the most homologous contig in the other assembly. We show that ordering the contigs of the other assembly can be expressed by a new string problem, the longest run subsequence problem (LRS). We show that LRS is NP-hard and present reduction rules and two algorithmic approaches that, together, are able to solve large instances of LRS to provable optimality. In particular, they can solve realistic instances resulting from partial Arabidopsis thaliana assemblies in short computation time. Our source code and all data used in the experiments are freely available. © Sven Schrinner, Manish Goel, Michael Wulfert, Philipp Spohr, Korbinian Schneeberger, and Gunnar W. Klau

关键词： Scaffolds

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Fast and accurate structural RNA alignment by progressive lagrangian optimization

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1st International Symposium on Computational Life Sciences, CompLife 2005

作者： Bauer, Markus Klau, Gunnar W. Reinert, Knut Algorithmic Bioinformatics Group Institute of Computer Science Free University of Berlin Germany International Max Planck Research School on Computational Biology and Scientific Computing Germany Mathematics in Life Sciences Group Institute of Mathematics Free University of Berlin Germany

ISBN: (纸本)3540291040

During the last few years new functionalities of RNA have been discovered, renewing the need for computational tools for their analysis. To this respect, multiple sequence alignment is an essential step in finding structurally conserved regions in related RNA sequences. In contrast to proteins, many classes of functionally related RNA molecules show a rather weak sequence conservation but instead a fairly well conserved secondary structure. Hence, any method that relates RNA sequences in form of multiple alignments should take structural features into account, which has been verified in recent studies. Progress has been made in developing new structural alignment algorithms, however, current methods are computationally costly or do not have the desired accuracy to make them an everyday tool. In this paper we present a fast, practical, and accurate method for computing multiple, structural RNA alignments. The method is based on combining a new pairwise structural alignment method with the popular program T-Coffee. Our pairwise method is based on an integer linear programming (ILP) formulation resulting from a graph-theoretic reformulation of the structural alignment problem. We find provably optimal or near-optimal solutions of the ILP with a Lagrangian approach. Tests on a recently published benchmark set show that our Lagrangian approach outper-forms current programs in quality and in the length of the sequences it can align. © Springer-Verlag Berlin Heidelberg 2005.

关键词： RNA

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Algorithms for the automated absolute quantification of diagnostic markers in complex proteomics samples

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1st International Symposium on Computational Life Sciences, CompLife 2005

作者： Gröpl, Clemens Lange, Eva Reinert, Knut Kohlbacher, Oliver Sturm, Marc Huber, Christian G. Mayr, Bettina M. Klein, Christoph L. Free University Berlin Algorithmic Bioinformatics D-14195 Berlin Germany Eberhard Karls University Tübingen Simulation of Biological Systems D-72076 Tübingen Germany Saarland University Instrumental Analysis and Bioanalysis D-66123 Saarbrücken Germany Italy

ISBN: (纸本)3540291040

HPLC-ESI-MS is rapidly becoming an established standard method for shotgun proteomics. Currently, its major drawbacks are two-fold: quantification is mostly limited to relative quantification and the large amount of data produced by every individual experiment can make manual analysis quite difficult. Here we present a new, combined experimental and algorithmic approach to absolutely quantify proteins from samples with unprecedented precision. We apply the method to the analysis of myoglobin in human blood serum, which is an important diagnostic marker for myocardial infarction. Our approach was able to determine the absolute amount of myoglobin in a serum sample through a series of standard addition experiments with a relative error of 2.5%. Compared to a manual analysis of the same dataset we could improve the precision and conduct it in a fraction of the time needed for the manual analysis. We anticipate that our automatic quantitation method will facilitate further absolute or relative quantitation of even more complex peptide samples. The algorithm was developed using our publically available software framework OpenMS (***). © Springer-Verlag Berlin Heidelberg 2005.

关键词： Proteins

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Broadcasting quantum coherence via cloning

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Physical Review A 2017年第5期96卷 052319-052319页

作者： Udit Kamal Sharma Indranil Chakrabarty Manish Kumar Shukla Center for Security Theory and Algorithmic Research International Institute of Information Technology Gachibowli Hyderabad India Center for Computational Natural Sciences and Bioinformatics International Institute of Information Technology Gachibowli Hyderabad India

Quantum coherence has recently emerged as a key candidate for use as a resource in various quantum information processing tasks. Therefore, it is of utmost importance to explore the possibility of creating a greater number of coherent states from an existing coherent pair. In other words, we start with an initial incoherent pair and induce coherence via quantum cloning. More specifically, we start with a genuinely incoherent state that remains incoherent with the change of basis and make it a coherent state at the end. This process is known as broadcasting of coherence via cloning, which can either be optimal or nonoptimal. Interestingly, in this work, we are able to give a method by which we can introduce coherence in the genuinely incoherent state. We use the computational basis representation of the most general two-qubit mixed state, shared between Alice and Bob, as the input state for the universal symmetric optimal Buzek-Hillery cloner. First of all we show that it is impossible to ensure optimal broadcast of coherence. Second, in the case of nonoptimal broadcasting, we show that the coherence introduced in the output states of the cloner will always be lesser than the initial coherence of the input state. Finally, we take the examples of statistical mixture of the most coherent state and most incoherent state, and then the Bell-diagonal states, to obtain their respective ranges of nonoptimal broadcasting in terms of their input state parameters.

关键词： Quantum communication Quantum information processing

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Multiple-choice knapsack for assigning partial atomic charges in drug-like molecules 18

Multiple-choice knapsack for assigning partial atomic charge...

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18th International Workshop on Algorithms in bioinformatics, WABI 2018

作者： Engler, Martin S. Caron, Bertrand Veen, Lourens Geerke, Daan P. Mark, Alan E. Klau, Gunnar W. Life Sciences and Health Group Centrum Wiskunde and Informatica Amsterdam Netherlands School of Chemistry and Molecular Biosciences University of Queensland St Lucia Australia Netherlands EScience Center Amsterdam Netherlands AIMMS Division of Molecular and Computational Toxicology Vrije Universiteit Amsterdam Netherlands Algorithmic Bioinformatics Heinrich Heine University Düsseldorf Germany

ISBN: (纸本)9783959770828

A key factor in computational drug design is the consistency and reliability with which intermolecular interactions between a wide variety of molecules can be described. Here we present a procedure to efficiently, reliably and automatically assign partial atomic charges to atoms based on known distributions. We formally introduce the molecular charge assignment problem, where the task is to select a charge from a set of candidate charges for every atom of a given query molecule. Charges are accompanied by a score that depends on their observed frequency in similar neighbourhoods (chemical environments) in a database of previously parameterised molecules. The aim is to assign the charges such that the total charge equals a known target charge within a margin of error while maximizing the sum of the charge scores. We show that the problem is a variant of the well-studied multiple-choice knapsack problem and thus weakly NP-complete. We propose solutions based on Integer Linear Programming and a pseudo-polynomial time Dynamic Programming algorithm. We show that the results obtained for novel molecules not included in the database are comparable to the ones obtained performing explicit charge calculations while decreasing the time to determine partial charges for a molecule by several orders of magnitude, that is, from hours or even days to below a second. Our software is openly available at https://***/enitram/charge-assign. © Martin S. Engler, Bertrand Caron, Lourens Veen, Daan P. Geerke, Alan E. Mark, and Gunnar W. Klau.

关键词： Molecules

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