Search results for "ALGORITHM"
showing 10 items of 4887 documents
Span Programs and Quantum Algorithms for st-Connectivity and Claw Detection
2012
We introduce a span program that decides st-connectivity, and generalize the span program to develop quantum algorithms for several graph problems. First, we give an algorithm for st-connectivity that uses O(n d^{1/2}) quantum queries to the n x n adjacency matrix to decide if vertices s and t are connected, under the promise that they either are connected by a path of length at most d, or are disconnected. We also show that if T is a path, a star with two subdivided legs, or a subdivision of a claw, its presence as a subgraph in the input graph G can be detected with O(n) quantum queries to the adjacency matrix. Under the promise that G either contains T as a subgraph or does not contain T…
Evaluation of enantioselective binding of fluoxetine to human serum albumin by ultrafiltration and CE - Experimental design and quality considerations
2012
Several pharmacokinetic processes are affected by enantioselectivity (ES). At the level of distribution, protein binding (PB) is one of the most important. The enantioselective binding of fluoxetine (FLX) to HSA has been evaluated in this work by ultrafiltration of FLX–HSA mixtures and chiral analysis of unbound fractions by EKC-CD. PB, affinity constants (K) and ES were obtained for both enantiomers of FLX. In order to improve the consistency of the estimations, the evaluation of affinity constants of each enantiomer was performed using two designs, one keeping constant the total concentration of protein and varying the total concentration of the enantiomers, and the other in the opposite …
Maximum weight relaxed cliques and Russian Doll Search revisited
2015
Trukhanov et al. [Trukhanov S, Balasubramaniam C, Balasundaram B, Butenko S (2013) Algorithms for detecting optimal hereditary structures in graphs, with application to clique relaxations. Comp. Opt. and Appl., 56(1), 113–130] used the Russian Doll Search (RDS) principle to effectively find maximum hereditary structures in graphs. Prominent examples of such hereditary structures are cliques and some clique relaxations intensely discussed and studied in network analysis. The effectiveness of the tailored RDS by Trukhanov et al. for s-plex and s-defective clique can be attributed to their cleverly designed incremental verification procedures used to distinguish feasible from infeasible struct…
Efficient unsupervised clustering for spatial bird population analysis along the Loire river
2015
International audience; This paper focuses on application and comparison of Non Linear Dimensionality Reduction (NLDR) methods on natural high dimensional bird communities dataset along the Loire River (France). In this context, biologists usually use the well-known PCA in order to explain the upstream-downstream gradient.Unfortunately this method was unsuccessful on this kind of nonlinear dataset.The goal of this paper is to compare recent NLDR methods coupled with different data transformations in order to find out the best approach. Results show that Multiscale Jensen-Shannon Embedding (Ms JSE) outperform all over methods in this context.
SMART: Unique splitting-while-merging framework for gene clustering
2014
© 2014 Fa et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Successful clustering algorithms are highly dependent on parameter settings. The clustering performance degrades significantly unless parameters are properly set, and yet, it is difficult to set these parameters a priori. To address this issue, in this paper, we propose a unique splitting-while-merging clustering framework, named "splitting merging awareness tactics" (SMART), which does not require any a priori knowledge of either the number …
The on-line curvilinear component analysis (onCCA) for real-time data reduction
2015
Real time pattern recognition applications often deal with high dimensional data, which require a data reduction step which is only performed offline. However, this loses the possibility of adaption to a changing environment. This is also true for other applications different from pattern recognition, like data visualization for input inspection. Only linear projections, like the principal component analysis, can work in real time by using iterative algorithms while all known nonlinear techniques cannot be implemented in such a way and actually always work on the whole database at each epoch. Among these nonlinear tools, the Curvilinear Component Analysis (CCA), which is a non-convex techni…
Computation Cluster Validation in the Big Data Era
2017
Data-driven class discovery, i.e., the inference of cluster structure in a dataset, is a fundamental task in Data Analysis, in particular for the Life Sciences. We provide a tutorial on the most common approaches used for that task, focusing on methodologies for the prediction of the number of clusters in a dataset. Although the methods that we present are general in terms of the data for which they can be used, we offer a case study relevant for Microarray Data Analysis.
GenClust: A genetic algorithm for clustering gene expression data
2005
Abstract Background Clustering is a key step in the analysis of gene expression data, and in fact, many classical clustering algorithms are used, or more innovative ones have been designed and validated for the task. Despite the widespread use of artificial intelligence techniques in bioinformatics and, more generally, data analysis, there are very few clustering algorithms based on the genetic paradigm, yet that paradigm has great potential in finding good heuristic solutions to a difficult optimization problem such as clustering. Results GenClust is a new genetic algorithm for clustering gene expression data. It has two key features: (a) a novel coding of the search space that is simple, …
Data Analysis and Bioinformatics
2007
Data analysis methods and techniques are revisited in the case of biological data sets. Particular emphasis is given to clustering and mining issues. Clustering is still a subject of active research in several fields such as statistics, pattern recognition, and machine learning. Data mining adds to clustering the complications of very large data-sets with many attributes of different types. And this is a typical situation in biology. Some cases studies are also described.
Structural clustering of millions of molecular graphs
2014
We propose an algorithm for clustering very large molecular graph databases according to scaffolds (i.e., large structural overlaps) that are common between cluster members. Our approach first partitions the original dataset into several smaller datasets using a greedy clustering approach named APreClus based on dynamic seed clustering. APreClus is an online and instance incremental clustering algorithm delaying the final cluster assignment of an instance until one of the so-called pending clusters the instance belongs to has reached significant size and is converted to a fixed cluster. Once a cluster is fixed, APreClus recalculates the cluster centers, which are used as representatives for…