5910842
Quantitative characterization of the transcriptional regulatory network in the yeast cell cycle
MOTIVATION
Genome-wide gene expression programs have been monitored and analyzed in the yeast Saccharomyces cerevisiae, but how cells regulate global gene expression programs in response to environmental changes is still far from being understood. We present a systematic approach to quantitatively characterize the transcriptional regulatory network of the yeast cell cycle. For the interpretative purpose, 20 target genes were selected because their expression patterns fluctuated in a periodic manner concurrent with the cell cycle and peaked at different phases. In addition to the most significant five possible regulators of each specific target gene, the expression pattern of each target gene affected by synergy of the regulators during the cell cycle was characterized. Our first step includes modeling the dynamics of gene expression and extracting the transcription rate from a time-course microarray data. The second step embraces finding the regulators that possess a high correlation with the transcription rate of the target gene, and quantifying the regulatory abilities of the identified regulators.


RESULTS
Our network discerns not only the role of the activator or repressor for each specific regulator, but also the regulatory ability of the regulator to the transcription rate of the target gene. The highly coordinated regulatory network has identified a group of significant regulators responsible for the gene expression program through the cell cycle progress. This approach may be useful for computing the regulatory ability of the transcriptional regulatory networks in more diverse conditions and in more complex eukaryotes.


SUPPLEMENTARY INFORMATION
Matlab code and test data are available at http://www.ee.nthu.edu.tw/~bschen/quantitative/regulatory_network.htm
5294188
Method for identifying transcription factor binding sites in yeast
MOTIVATION
Identifying transcription factor binding sites (TFBSs) is helpful for understanding the mechanism of transcriptional regulation. The abundance and the diversity of genomic data provide an excellent opportunity for identifying TFBSs. Developing methods to integrate various types of data has become a major trend in this pursuit.


RESULTS
We develop a TFBS identification method, TFBSfinder, which utilizes several data sources, including DNA sequences, phylogenetic information, microarray data and ChIP-chip data. For a TF, TFBSfinder rigorously selects a set of reliable target genes and a set of non-target genes (as a background set) to find overrepresented and conserved motifs in target genes. A new metric for measuring the degree of conservation at a binding site across species and methods for clustering motifs and for inferring position weight matrices are proposed. For synthetic data and yeast cell cycle TFs, TFBSfinder identifies motifs that are highly similar to known consensuses. Moreover, TFBSfinder outperforms well-known methods.


AVAILABILITY
http://cg1.iis.sinica.edu.tw/~TFBSfinder/.
12496830
A Fast Algorithm for Color Image Segmentation
Based on K-means and a two-layer pyramid structure, a fast algorithm is proposed for color image segmentation. The algorithm employs two strategies. Firstly, a two-layer structure of a color image is established. Then, an improved K-means with integer based lookup table implementation is applied to each layer. The clustering result on the upper layer (lower resolution) is used to guide the clustering in the lower layer (higher resolution). Experiments have shown that the proposed algorithm is significantly faster than the original K-means algorithm while producing comparable segmentation results
42414770
Distributed Knowledge Based System Using Grid Computing for Real Time Air Traffic Synchronization - ATFMGC
6649365
Performing Incremental Bayesian Inference by Dynamic Model Counting
The ability to update the structure of a Bayesian network when new data becomes available is crucial for building adaptive systems. Recent work by Sang, Beame, and Kautz (AAAI 2005) demonstrates that the well-known Davis-Putnam procedure combined with a dynamic decomposition and caching technique is an effective method for exact inference in Bayesian networks with high density and width. In this paper, we define dynamic model counting and extend the dynamic decomposition and caching technique to multiple runs on a series of problems with similar structure. This allows us to perform Bayesian inference incrementally as the structure of the network changes. Experimental results show that our approach yields significant improvements over the previous model counting approaches on multiple challenging Bayesian network instances.
8035776
Semantic Integration in Heterogeneous Databases Using Neural Networks
One important step in integrating heterogeneous databases is matching equivalent attributes: Determining which fields in two databases refer to the same data. The meaning of information may be embodied within a. database model, a conceptual schema, application programs, or data contents. Integration involves extracting semantics, expressing them as metadata, and matching semantically equivalent data elements. We present a procedure using a classifier to categorize attributes according to their field specifications and data values, then train a neural network to recognize similar attributes. In our technique, the knowledge of how to match equivalent data elements is “discovered” from metadata , not “pre-programmed”.
17165775
Clustering of highly homologous sequences to reduce the size of large protein databases
We present a fast and flexible program for clustering large protein databases at different sequence identity levels. It takes less than 2 h for the all-against-all sequence comparison and clustering of the non-redundant protein database of over 560,000 sequences on a high-end PC. The output database, including only the representative sequences, can be used for more efficient and sensitive database searches.