42728551
Random distributed multiresolution representations with significance querying
33099797
Compression of cDNA and inkjet microarray images
Microarray image technology is a powerful tool for monitoring the expression of thousands of genes simultaneously. Each microarray experiment produces immense amounts of image data, and efficient storage and transmission requires compression that utilizes the microarray image's structure and unique analysis goals. We have developed a progressive compression scheme for microarray images which can be either lossy or lossless. Our scheme has a coded data structure that allows fast decoding and reprocessing of image subsets, and includes summary statistics and image segmentation information. Since visual fidelity is not the end goal for microarray images, we introduce a new measure of distortion for lossy compression: the sensitivity of microarray information extraction to compression loss. We find that a lossy compression ratio of 8:1 for cDNA microarrays minimally affects downstream processing. The average lossless compression ratio is 1.83:1 for cDNA images and 2.43:1 for inkjet images, comparable to state-of-the-art lossless schemas, yet with added flexibility and information.
37744397
Stay Current and Relevant in Data Mining Research
In a recent editorial of the Bioinformatics journal, Dr. Pavel Pevzner, a pioneering researcher in the field of bioinformatics, made the following statement [1]: For many years algorithms were taught exclusively to computer scientists, with relatively few students from other disciplines attending algorithms courses. A biology student in an algorithms class would be a surprising and unlikely (though not entirely unwelcome) guest in the 1990s. Things change; some biology students now take some sort of Algorithms 101. At the same time, curious computer science students often take Genetics 101.
8256320
Fast computation with neural oscillators
This paper studies new spike-based models for winner-take-all computation and coincidence detection. In both cases, fast convergence is achieved independent of initial conditions, and network complexity is linear in the number of inputs. Fully distributed versions can be modelled based on groups of interneurons connected through electrical synapses.
16657065
Collaborative Web caching based on proxy affinities
With the exponential growth of hosts and traffic workloads on the Internet, collaborative web caching has been recognized as an efficient solution to alleviate web page server bottlenecks and reduce traffic. However, cache discovery, i.e., locating where a page is cached, is a challenging problem, especially in the fast growing World Wide Web environment, where the number of participating proxies can be very large. In this paper, we propose a new scheme which employs proxy affinities to maintain a dynamic distributed collaborative caching infrastructure. Web pages are partitioned into clusters according to proxy reference patterns. All proxies which frequently access some page(s) in the same web page cluster form an “information group”. When web pages belonging to a web page cluster are deleted from or added into a proxy's cache, only proxies in the associated information group are notified. This scheme can be shown to greatly reduce the number of messages and other overhead on individual proxies while maintaining a high cache hit rate. Finally, we employ trace driven simulation to evaluate our web caching scheme using three web access trace logs to verify that our caching structure can provide significant benefits on real workloads.
7654975
Using low-discrepancy sequences and the Crofton formula to compute surface areas of geometric models
The surface area of a geometric model, like its volume, is an important integral property that needs to be evaluated frequently and accurately in practice. In this paper, we present a new quasi-Monte Carlo method using low-discrepancy sequences for computing the surface area of a 3D object. We show that the new method is more efficient than a Monte Carlo method using pseudo-random numbers. This method is based on the Cauchy ‐ Crofton formula from integral geometry, and it computes the surface area of a 3D body B by counting the number of intersection points between the boundary surface of B and a set of straight lines in E 3 . Low discrepancy sequences are used to generate the set of lines in E 3 to reduce the estimation errors that would be caused by using statistically uniformly distributed lines. We study and compare two different methods for generating 3D random lines, and demonstrate their validity theoretically and experimentally. Experiments suggest that the new quasi-Monte Carlo method is also more efficient than the conventional approach based on surface tessellation. q 2002 Elsevier Science Ltd. All rights reserved.
8630623
Gene ontology friendly biclustering of expression profiles
The soundness of clustering in the analysis of gene expression profiles and gene function prediction is based on the hypothesis that genes with similar expression profiles may imply strong correlations with their functions in the biological activities. Gene ontology (GO) has become a well accepted standard in organizing gene function categories. Different gene function categories in GO can have very sophisticated relationships, such as 'part of' and 'overlapping'. Until now, no clustering algorithm can generate gene clusters within which the relationships can naturally reflect those of gene function categories in the GO hierarchy. The failure in resembling the relationships may reduce the confidence of clustering in gene function prediction. In this paper, we present a new clustering technique, smart hierarchical tendency preserving clustering (SMTP-clustering), based on a bicluster model, tendency preserving cluster (TP-Cluster). By directly incorporating gene ontology information into the clustering process, the SMTP-clustering algorithm yields a TP-cluster tree within which any subtree can be well mapped to a part of the GO hierarchy. Our experiments on yeast cell cycle data demonstrate that this method is efficient and effective in generating the biological relevant TP-clusters.