8353152
An Efficient Algorithm for Mining Coherent Patterns from Heterogeneous Microarrays
DNA microarray techniques present a novel way for geneticists to monitor interactions among tens of thousands of genes simultaneously, and have become standard lab routines in gene discovery, disease diagnosis, and drug design. There has been extensive research on coherent subspace clustering of gene expressions measured under consistent experimental settings. This implies that all experiments are run using the same batch of microarray chips with similar characteristics of noise. Algorithms developed under this assumption may not be applicable for analyzing data collected from heterogeneous settings, where the set of genes being monitored may be different and expression levels may be not directly comparable even for the same gene. In this paper, we propose a model, F-cluster, for mining subspace coherent patterns from heterogeneous gene expression data, which is shown effective for revealing truthful patterns and reducing spurious ones. We also develop an efficient and scalable hybrid approach that combines gene-pair based and sample-pair based pruning to generate F-clusters from multiple gene expression matrices simultaneously. The experimental results demonstrate that our model can discover significant clusters that may not be identified by previous models.
2396831
Mining coherent patterns from heterogeneous microarray data
Microarray technology is a powerful tool for geneticists to monitor interactions among tens of thousands of genes simultaneously. There has been extensive research on coherent subspace clustering of gene expressions measured under consistent experimental settings. However, these methods assume that all experiments are run using the same batch of microarray chips with similar characteristics of noise. Algorithms developed under this assumption may not be applicable for analyzing data collected from heterogeneous settings, where the set of genes being monitored may be different and expression levels may be not directly comparable even for the same gene. In this paper, we propose a model, F-cluster, for mining subspace coherent patterns from heterogeneous gene expression data. We compare our model with previously proposed models. We analyze the search space of the problem and give a naïve solution for it.
17965840
Novel Approaches to Biomolecular Sequence Indexing
In many biomolecular database applications involving string/sequence data, it is common to have similarity search in the form of near neighbor queries or nearest neighbor queries. The similarity between strings/sequences are typically measured in terms of the least costly set of allowed edit operations that transform one string/sequence to another. In this survey, we briefly describe some of the recent developments in biomolecular sequence indexing methods that allow efficient similarity search. Our focus here is on global similarity measures that compare sequences in full; such measures are important for comparing protein sequences and smaller biomolecules. Examples include character and block edit distances and their weighted variants. Two major approaches are summarized here: distance based indexing and embeddings of general sequence similarity measures to Hamming distance, for which efficient indexing methods are available.
12946822
Partial parallel factorization in soft-decision Reed-Solomon decoding
Reed-Solomon (RS) codes have very broad applications in communications and data storage systems. The recently developed Koetter-Vardy (KV) soft-decision decoding algorithm of RS codes can achieve substantial coding gain, while has a complexity polynomial with respect to the codeword length. One of the major steps of the KV algorithm is the factorization step. A prediction-based architecture is developed in prior efforts to reduce the latency and silicon area of this step. However, the speedup can be achieved by this approach is limited by the serial nature of the factorization algorithm. The computations involved in multiple iteration levels can not be carried out simultaneously. In this work, a novel partial parallel architecture is proposed for the factorization step. The partial 2-parallel architecture can achieve a speedup of 19% over prior works.
9003534
A Language Modeling Text Mining Approach to the Annotation of Protein Community
This paper discusses an ontology based language modeling text mining approach to the annotation of protein community. Communities appear to play an important role in the functional properties of complex networks. Being able to annotate the identified the community structure in a biological network can help us to understand better the structure and dynamics of biological systems. Traditional method such as gene ontology (GO) provides information about the functionality of gene products, but they are not enough to annotate community as for only limited number of proteins in the database, limited protein properties available for annotation and the inability to annotate a group of gene products as a whole. Thus, we present an ontology based mixture language model approach to annotate protein community. Compared to traditional method, we have the following three advantages. First, biomedical literature mining brings much richer information than existed gene databases. Second, the mixture language model can help "purify" the document by eliminating some background noise. Third, using domain ontology, we extract biological concept and concept pairs from abstracts. Biological concept is more meaningful than word or multi-word phrases. Moreover, using concept pairs can deliver much more information and serve as evidence of annotation results. We test our approach on four communities SAGA-SRB, CCR-NOT, RFC and ARP2/3, detected from dataset of interactions for Saccharomyces cerevisae from the general repository for interaction datasets (GRID). Annotation results provide a very coherent indication of functionality of each community
13183312
Wavelet based automatic thresholding for image segmentation
In this paper, a new systematic method to segment possible target areas based on wavelet transforms is presented. We develop an analytic model for the segmentation of targets, which uses a novel multiresolution analysis in concert with a Bayesian classifier to identify the possible target areas. A method is developed which adaptively chooses thresholds to segment targets from background, by using a multiscale analysis of the image probability density function (PDF). We present examples which demonstrate the efficiency of the technique on a variety of targets.
18526893
Scaling and parallelizing a scientific feature mining application using a cluster middleware
Summary form only given. As scientific simulations are generating large amounts of data, analyzing this data to gain insights into scientific phenomenon is increasingly becoming a challenge. We present a case study on the use of a cluster middleware for rapidly creating a scalable and parallel implementation of a scientific data analysis application. Using FREERIDE (framework for rapid implementation of data mining engines), we parallelize as well as scale to disk-resident datasets a feature extraction algorithm. We have developed a parallel algorithm for this problem which matches the communication and computation structure supported by the FREERIDE system. The main observations from our experimental results are as follows: 1) the overhead of using the middleware is quite small in most cases, 2) there is an overhead associated with breaking the datasets into more partitions or chunks, and 3) if the dataset is partitioned into the same number of chunks, the execution time stays proportional to the size of the dataset and inversely proportional to the number of nodes, i.e. the overhead of communication or reading disk-resident datasets is very small.
14773046
Predicting methylation status of CpG islands in the human brain
MOTIVATION
Over 50% of human genes contain CpG islands in their 5'-regions. Methylation patterns of CpG islands are involved in tissue-specific gene expression and regulation. Mis-epigenetic silencing associated with aberrant CpG island methylation is one mechanism leading to the loss of tumor suppressor functions in cancer cells. Large-scale experimental detection of DNA methylation is still both labor-intensive and time-consuming. Therefore, it is necessary to develop in silico approaches for predicting methylation status of CpG islands.


RESULTS
Based on a recent genome-scale dataset of DNA methylation in human brain tissues, we developed a classifier called MethCGI for predicting methylation status of CpG islands using a support vector machine (SVM). Nucleotide sequence contents as well as transcription factor binding sites (TFBSs) are used as features for the classification. The method achieves specificity of 84.65% and sensitivity of 84.32% on the brain data, and can also correctly predict about two-third of the data from other tissues reported in the MethDB database.


AVAILABILITY
An online predictor based on MethCGI is available at http://166.111.201.7/MethCGI.html


CONTACT
mzhang@cshl.edu


SUPPLEMENTARY INFORMATION
Supplementary data available at Bioinformatics online and http://166.111.201.7/help.html.