On discovery of extremely low-dimensional clusters using semi-supervised projected clustering

Recent studies suggest that projected clusters with extremely low dimensionality exist in many real datasets. A number of projected clustering algorithms have been proposed in the past several years, but few can identify clusters with dimensionality lower than 10% of the total number of dimensions, which are commonly found in some real datasets such as gene expression profiles. In this paper we propose a new algorithm that can accurately identify projected clusters with relevant dimensions as few as 5% of the total number of dimensions. It makes use of a robust objective function that combines object clustering and dimension selection into a single optimization problem. The algorithm can also utilize domain knowledge in the form of labeled objects and labeled dimensions to improve its clustering accuracy. We believe this is the first semi-supervised projected clustering algorithm. Both theoretical analysis and experimental results show that by using a small amount of input knowledge, possibly covering only a portion of the underlying classes, the new algorithm can be further improved to accurately detect clusters with only 1% of the dimensions being relevant. The algorithm is also useful in getting a target set of clusters when there are multiple possible groupings of the objects. © 2005 IEEE.published_or_final_versio

Persistent current of one-dimensional perfect rings under the canonical ensemble

We have analyzed the harmonic contents of the persistent current at high temperatures under the canonical ensemble. Results show that the behavior of each harmonic is different from that under the grand-canonical ensemble. The persistent current of the multiring system is also presented.published_or_final_versio

HARP: A practical projected clustering algorithm

In high-dimensional data, clusters can exist in subspaces that hide themselves from traditional clustering methods. A number of algorithms have been proposed to Identify such projected clusters, but most of them rely on some user parameters to guide the clustering process. The clustering accuracy can be seriously degraded If incorrect values are used. Unfortunately, in real situations, it is rarely possible for users to supply the parameter values accurately, which causes practical difficulties in applying these algorithms to real data. In this paper, we analyze the major challenges of projected clustering and suggest why these algorithms need to depend heavily on user parameters. Based on the analysis, we propose a new algorithm that exploits the clustering status to adjust the internal thresholds dynamically without the assistance of user parameters. According to the results of extensive experiments on real and synthetic data, the new method has excellent accuracy and usability. It outperformed the other algorithms even when correct parameter values were artificially supplied to them. The encouraging results suggest that projected clustering can be a practical tool for various kinds of real applications.published_or_final_versio

Dynamic conductance of mesoscopic waveguides

We report a theoretical investigation of dynamic conductance G(), for general ac frequency , of two-dimensional mesoscopic waveguides whose transport is characterized by antiresonances. We calculate G() by numerically evaluating nonequilibrium Green's functions. By tuning the ac frequency we observe photon-assisted resonant transport as well as a gradual smearing out of the antiresonances. The antiresonance causes the dynamic response to vary between capacitive-like behavior to that of the inductive-like behavior. © 2001 American Institute of Physics.published_or_final_versio

Identifying projected clusters from gene expression profiles

In microarray gene expression data, clusters may hide in subspaces. Traditional clustering algorithms that make use of similarity measurements in the full input space may fail to detect the clusters. In recent years a number of algorithms have been proposed to identify this kind of projected clusters, but many of them rely on some critical parameters whose proper values are hard for users to determine. In this paper a new algorithm that dynamically adjusts its internal thresholds is proposed. It has a low dependency on user parameters while allowing users to input some domain knowledge should they be available. Experimental results show that the algorithm is capable of identifying some interesting projected clusters from real microarray data.published_or_final_versio

Deconvolution of positron annihilation coincidence Doppler broadening spectra using an iterative projected Newton method with non-negativity constraints

A generalized least-square method with Tikonov-Miller regularization and non-negativity constarints was developed for deconvoluting two-dimensional coincidence Doppler broadening spectroscopy (CDBS) spectra. A projected Newton algorithm was developed to solve the generalized least-square problem. The algorithm was used to deconvolute experimental CDBS data from aluminum was tested on Monte Carlo generated spectra. The retrieval of the positron-electron momentum distributions in the low momentum region was also demonstrated.published_or_final_versio

A genetic locus for adolescent idiopathic scoliosis linked to chromosome 19p13.3

Adolescent idiopathic scoliosis (ALS) is one of the most common orthopedic disorders, affecting up to 4% of schoolchildren worldwide. We studied seven unrelated multiplex families of southern Chinese descent with AIS, consisting of 25 affected members. A genomewide scan with >400 fluorescent microsatellite markers was performed. Multipoint linkage analysis by GENEHUNTER revealed significant linkage of the abnormal phenotype to the distal short arm of chromosome 19, with both a maximum multipoint LOD score and a nonparametric LOD score of 4.93. Two-point linkage analysis by MLINK gave a LOD score of 3.63 (recombination fraction θ [m=f] = 0.00) at D19S216. Further high-density mapping and informative recombinations defined the AIS critical region in the vicinity of D19S216, flanked by D19S894 and D19S1034, spanning 5.2 cM on the sex-averaged genetic map on chromosome 19p13.3.published_or_final_versio

Copy number gain of granulin-epithelin precursor (GEP) at chromosome 17q21 associates with overexpression in human liver cancer

Background: Granulin-epithelin precursor (GEP), a secretory growth factor, demonstrated overexpression in various human cancers, however, mechanism remain elusive. Primary liver cancer, hepatocellular carcinoma (HCC), ranks the second in cancer-related death globally. GEP controlled growth, invasion, metastasis and chemo-resistance in liver cancer. Noted that GEP gene locates at 17q21 and the region has been frequently reported to be amplified in subset of HCC. The study aims to investigate if copy number gain would associate with GEP overexpression. Methods: Quantitative Microsatellite Analysis (QuMA) was used to quantify the GEP DNA copy number, and fluorescent in situ hybridization (FISH) was performed to consolidate the amplification status. GEP gene copy number, mRNA expression level and clinico-pathological features were analyzed. Results: GEP DNA copy number determined by QuMA corroborated well with the FISH data, and the gene copy number correlated with the expression levels (n = 60, r = 0.331, P = 0.010). Gain of GEP copy number was observed in 20% (12/60) HCC and associated with hepatitis B virus infection status (P = 0.015). In HCC with increased GEP copy number, tight association between GEP DNA and mRNA levels were observed (n = 12, r = 0.664, P = 0.019). Conclusions: Gain of the GEP gene copy number was observed in 20% HCC and the frequency comparable to literatures reported on the chromosome region 17q. Increased gene copy number contributed to GEP overexpression in subset of HCC. © Yung et al; licensee BioMed Central.published_or_final_versio

Robot-assisted radical prostatectomy in Hong Kong: a review of 235 cases

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