Cancer molecular pattern efficient discovery is essential in the molecular diagnostics. The characteristics of the gene/protein expression data are challenging traditional unsupervised classification algorithms. In this work, we describe a subspace consensus kernel clustering algorithm based on the projected gradient nonnegative matrix factorization (PG-NMF). The algorithm is a consensus kernel...

[Internal Report] Saliya Ekanayake School of Informatics and Computing, Indiana University [email protected] ABSTRACT Determination of biologically related clusters of sequences is important bioinformatics analyses. The similarity between sequences is generally assessed based on their alignments with one another. This could be used with a clustering algorithm to determine groups of sequen...

We use a cluster ensemble to determine the number of clusters, k, in a group of data. A consensus similarity matrix is formed from the ensemble using multiple algorithms and several values for k. A random walk is induced on the graph defined by the consensus matrix and the eigenvalues of the associated transition probability matrix are used to determine the number of clusters. For noisy or high...

Multi-view subspace clustering (MVSC) optimally integrates multiple graph structure information to improve performance. Recently, many anchor-based variants are proposed reduce the computational complexity of MVSC. Though achieving considerable acceleration, we observe that most them adopt fixed anchor points separating from subsequential construction, which may adversely affect In addition, po...

In order to provided a novel maximised approach to the generation of accurate, comprehensive, consensus sequences of the expressed human genome, we have developed and produced a system for a novel-representation, broad gene coverage, consensus database of expressed human gene fragments (ESTs). To perform clustering of ESTs, we have developed and employed D2-cluster, an algorithm based on the d2...

Reiterated runs of standard docking protocols usually provide a collection of possible binding modes rather than pinpoint a single solution. Usually, this ensemble is then ranked by means of an energy-based scoring function. However, since many degrees of approximation have to be introduced in the computation of the binding free energy, scoring functions cannot always rank the experimental pose...

Brain tissue segmentation is an important component of the clinical diagnosis brain diseases using multi-modal magnetic resonance imaging (MR). has been developed by many unsupervised methods in literature. The most commonly used are K-Means, Expectation-Maximization, and Fuzzy Clustering. clustering offer considerable benefits compared with aforementioned as they capable handling images that c...