740 research outputs found
Fast branching algorithm for Cluster Vertex Deletion
In the family of clustering problems, we are given a set of objects (vertices
of the graph), together with some observed pairwise similarities (edges). The
goal is to identify clusters of similar objects by slightly modifying the graph
to obtain a cluster graph (disjoint union of cliques). Hueffner et al. [Theory
Comput. Syst. 2010] initiated the parameterized study of Cluster Vertex
Deletion, where the allowed modification is vertex deletion, and presented an
elegant O(2^k * k^9 + n * m)-time fixed-parameter algorithm, parameterized by
the solution size. In our work, we pick up this line of research and present an
O(1.9102^k * (n + m))-time branching algorithm
Normal, Abby Normal, Prefix Normal
A prefix normal word is a binary word with the property that no substring has
more 1s than the prefix of the same length. This class of words is important in
the context of binary jumbled pattern matching. In this paper we present
results about the number of prefix normal words of length , showing
that for some and
. We introduce efficient
algorithms for testing the prefix normal property and a "mechanical algorithm"
for computing prefix normal forms. We also include games which can be played
with prefix normal words. In these games Alice wishes to stay normal but Bob
wants to drive her "abnormal" -- we discuss which parameter settings allow
Alice to succeed.Comment: Accepted at FUN '1
Theoretical Sensitivity Analysis for Quantitative Operational Risk Management
We study the asymptotic behavior of the difference between the values at risk
VaR(L) and VaR(L+S) for heavy tailed random variables L and S for application
in sensitivity analysis of quantitative operational risk management within the
framework of the advanced measurement approach of Basel II (and III). Here L
describes the loss amount of the present risk profile and S describes the loss
amount caused by an additional loss factor. We obtain different types of
results according to the relative magnitudes of the thicknesses of the tails of
L and S. In particular, if the tail of S is sufficiently thinner than the tail
of L, then the difference between prior and posterior risk amounts VaR(L+S) -
VaR(L) is asymptotically equivalent to the expectation (expected loss) of S.Comment: 21 pages, 1 figure, 4 tables, forthcoming in International Journal of
Theoretical and Applied Finance (IJTAF
Going weighted: Parameterized algorithms for cluster editing
AbstractThe goal of the Cluster Editing problem is to make the fewest changes to the edge set of an input graph such that the resulting graph is a disjoint union of cliques. This problem is NP-complete but recently, several parameterized algorithms have been proposed. In this paper, we present a number of surprisingly simple search tree algorithms for Weighted Cluster Editing assuming that edge insertion and deletion costs are positive integers. We show that the smallest search tree has size O(1.82k) for edit cost k, resulting in the currently fastest parameterized algorithm, both for this problem and its unweighted counterpart. We have implemented and compared our algorithms, and achieved promising results.11This is an extended version of two articles published in: Proc. of the 6th Asia Pacific Bioinformatics Conference, APBC 2008, in: Series on Advances in Bioinformatics and Computational Biology, vol. 5, Imperial College Press, pp. 211–220; and in: Proc. of the 2nd Conference on Combinatorial Optimization and Applications, COCOA 2008, in: LNCS, vol. 5038, Springer, pp. 289–302
MAD HATTER Correctly Annotates 98% of Small Molecule Tandem Mass Spectra Searching in PubChem
Metabolites provide a direct functional signature of cellular state. Untargeted metabolomics usually relies on mass spectrometry, a technology capable of detecting thousands of compounds in a biological sample. Metabolite annotation is executed using tandem mass spectrometry. Spectral library search is far from comprehensive, and numerous compounds remain unannotated. So-called in silico methods allow us to overcome the restrictions of spectral libraries, by searching in much larger molecular structure databases. Yet, after more than a decade of method development, in silico methods still do not reach the correct annotation rates that users would wish for. Here, we present a novel computational method called Mad Hatter for this task. Mad Hatter combines CSI:FingerID results with information from the searched structure database via a metascore. Compound information includes the melting point, and the number of words in the compound description starting with the letter ‘u’. We then show that Mad Hatter reaches a stunning 97.6% correct annotations when searching PubChem, one of the largest and most comprehensive molecular structure databases. Unfortunately, Mad Hatter is not a real method. Rather, we developed Mad Hatter solely for the purpose of demonstrating common issues in computational method development and evaluation. We explain what evaluation glitches were necessary for Mad Hatter to reach this annotation level, what is wrong with similar metascores in general, and why metascores may screw up not only method evaluations but also the analysis of biological experiments. This paper may serve as an example of problems in the development and evaluation of machine learning models for metabolite annotation
Targeting of Rad51-dependent homologous recombination: implications for the radiation sensitivity of human lung cancer cell lines
The aim of the present work was to study the role of Rad51-dependent homologous recombination in the radiation response of non-small-cell lung cancer (NSCLC) cell lines. A dose- and time-dependent increase in the formation of Rad51 and γ-H2AX foci with a maximum at about 4 and 1 h after irradiation, followed by a decrease, has been found. The relative fraction of cells with persisting Rad51 foci was 20–30% in radioresistant and 60–80% in radiosensitive cell lines. In comparison, a higher fraction of residual Dsb was evident in cell lines with nonfunctional p53. Transfection with As-Rad51 significantly downregulates radiation-induced formation of Rad51 foci and increases apoptosis, but did not influence the rejoining of DNA double-strand breaks. Interestingly, wortmannin, a well-known inhibitor of nonhomologous end-joining, also inhibits Rad51 foci formation. In general, there was no correlation between the clonogenic survival at 2 Gy and the percentage of initial Rad51 or γ-H2AX foci after ionising radiation (IR). The most reliable predictive factor for radiosensitivity of NSCLC cell lines was the relative fraction of Rad51 foci remaining at 24 h after IR. Although most of the Rad51 foci are co-localised with γ-H2AX foci, no correlation of the relative fraction of persisting γ-H2AX foci and SF2 is evident
The development of a knowledge base for basic active structures: an example case of dopamine agonists
<p>Abstract</p> <p>Background</p> <p>Chemical compounds affecting a bioactivity can usually be classified into several groups, each of which shares a characteristic substructure. We call these substructures "basic active structures" or BASs. The extraction of BASs is challenging when the database of compounds contains a variety of skeletons. Data mining technology, associated with the work of chemists, has enabled the systematic elaboration of BASs.</p> <p>Results</p> <p>This paper presents a BAS knowledge base, BASiC, which currently covers 46 activities and is available on the Internet. We use the dopamine agonists D1, D2, and Dauto as examples and illustrate the process of BAS extraction. The resulting BASs were reasonably interpreted after proposing a few template structures.</p> <p>Conclusions</p> <p>The knowledge base is useful for drug design. Proposed BASs and their supporting structures in the knowledge base will facilitate the development of new template structures for other activities, and will be useful in the design of new lead compounds via reasonable interpretations of active structures.</p
On Symbolic Ultrametrics, Cotree Representations, and Cograph Edge Decompositions and Partitions
Symbolic ultrametrics define edge-colored complete graphs K_n and yield a
simple tree representation of K_n. We discuss, under which conditions this idea
can be generalized to find a symbolic ultrametric that, in addition,
distinguishes between edges and non-edges of arbitrary graphs G=(V,E) and thus,
yielding a simple tree representation of G. We prove that such a symbolic
ultrametric can only be defined for G if and only if G is a so-called cograph.
A cograph is uniquely determined by a so-called cotree. As not all graphs are
cographs, we ask, furthermore, what is the minimum number of cotrees needed to
represent the topology of G. The latter problem is equivalent to find an
optimal cograph edge k-decomposition {E_1,...,E_k} of E so that each subgraph
(V,E_i) of G is a cograph. An upper bound for the integer k is derived and it
is shown that determining whether a graph has a cograph 2-decomposition, resp.,
2-partition is NP-complete
Grote ondernemingen over vijf specialistische rechtspraakvoorzieningen: ervaringen en waardering
Contains fulltext :
142855pub.pdf (publisher's version ) (Open Access)Themamiddag LOVCK 1 november 201
Cluster Editing: Kernelization based on Edge Cuts
Kernelization algorithms for the {\sc cluster editing} problem have been a
popular topic in the recent research in parameterized computation. Thus far
most kernelization algorithms for this problem are based on the concept of {\it
critical cliques}. In this paper, we present new observations and new
techniques for the study of kernelization algorithms for the {\sc cluster
editing} problem. Our techniques are based on the study of the relationship
between {\sc cluster editing} and graph edge-cuts. As an application, we
present an -time algorithm that constructs a kernel for the
{\it weighted} version of the {\sc cluster editing} problem. Our result meets
the best kernel size for the unweighted version for the {\sc cluster editing}
problem, and significantly improves the previous best kernel of quadratic size
for the weighted version of the problem
- …