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.
17528755
Significance of Gene Ranking for Classification of Microarray Samples
Many methods for classification and gene selection with microarray data have been developed. These methods usually give a ranking of genes. Evaluating the statistical significance of the gene ranking is important for understanding the results and for further biological investigations, but this question has not been well addressed for machine learning methods in existing works. Here, we address this problem by formulating it in the framework of hypothesis testing and propose a solution based on resampling. The proposed r-test methods convert gene ranking results into position p-values to evaluate the significance of genes. The methods are tested on three real microarray data sets and three simulation data sets with support vector machines as the method of classification and gene selection. The obtained position p-values help to determine the number of genes to be selected and enable scientists to analyze selection results by sophisticated multivariate methods under the same statistical inference paradigm as for simple hypothesis testing methods
8572893
Access-Mode Predictions for Low-Power Cache Design
An access-mode prediction technique based on cache hit and miss speculation for cache design-achieves minimal energy consumption. Using this method, cache accesses can be adaptively switched between the way-prediction and the phased accessing modes.
205875547
The hamiltonicity of bipartite graphs involving neighborhood unions
Let G = (X,Y) be a 2-connected balanced bipartite graph with |X| = |Y| = n. In this paper, we prove that if |N(x1) ∪ N(x2)| + |N(y1) ∪ N(y2)| ≥ n+2 for any {x1, x2} ⊆ X and {y1, y2} ⊆ Y, then G is hamiltonian except when G is a special graph on 8 or on 12 vertices.
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.
28741459
Learning Layouts of Biological Datasets Semi-automatically
A key challenge associated with the existing approaches for data integration and workflow creation for bioinformatics is the effort required to integrate a new data source. As new data sources emerge, and data formats and contents of existing data sources evolve, wrapper programs need to be written or modified. This can be extremely time consuming, tedious, and error-prone. 
 
This paper describes our semi-automatic approach for learning the layout of a flat-file bioinformatics dataset. Our approach involves three key steps. The first step is to use a number of heuristics to infer the delimiters used in the program. Specifically, we have developed a metric that uses information on the frequency and starting position of sequences. Based on this metric, we are able to find a superset of delimiters, and then we can seek user input to eliminate the incorrect ones. Our second step involves generating a layout descriptor based on the relative order in which the delimiters occur. Our final step is to generate a parser based on the layout descriptor. Our heuristics for finding the delimiters has been evaluated using three popular flat-file biological datasets.
9344326
Fine-grain priority scheduling on multi-channel memory systems
Configurations of contemporary DRAM memory systems become increasingly complex. A recent study shows that the application performance is highly sensitive to choices of configurations. In this study we show that, by utilizing fine-grain priority access scheduling, we are able to find a workload independent configuration that achieves optimal performance on a multichannel memory system. Our approach can well utilize the available high concurrency and high bandwidth on such memory systems, and effectively reduce the memory stall time of memory-intensive applications. Conducting execution-driven simulation of a 4-way issue, a 2 GHz processor, we show that the average performance improvement for fifteen memory-intensive SPEC2000 programs by using an optimized fine-grain priority scheduling is about 13% and 8% for a 2-channel and a 4-channel Direct Rambus DRAM memory system, respectively, compared with gang scheduling. Compared with burst scheduling, the average performance improvement is 16% and 14% for the 2-channel and 4-channel memory systems, respectively.