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KDD
2009
ACM
230views Data Mining» more  KDD 2009»
15 years 11 months ago
Grouped graphical Granger modeling methods for temporal causal modeling
We develop and evaluate an approach to causal modeling based on time series data, collectively referred to as“grouped graphical Granger modeling methods.” Graphical Granger mo...
Aurelie C. Lozano, Naoki Abe, Yan Liu, Saharon Ros...
BMCBI
2006
153views more  BMCBI 2006»
15 years 6 months ago
Intensity-based hierarchical Bayes method improves testing for differentially expressed genes in microarray experiments
Background: The small sample sizes often used for microarray experiments result in poor estimates of variance if each gene is considered independently. Yet accurately estimating v...
Maureen A. Sartor, Craig R. Tomlinson, Scott C. We...
BMCBI
2005
121views more  BMCBI 2005»
15 years 6 months ago
Automated methods of predicting the function of biological sequences using GO and BLAST
Background: With the exponential increase in genomic sequence data there is a need to develop automated approaches to deducing the biological functions of novel sequences with hig...
Craig E. Jones, Ute Baumann, Alfred L. Brown
FSS
2010
147views more  FSS 2010»
15 years 5 months ago
A divide and conquer method for learning large Fuzzy Cognitive Maps
Fuzzy Cognitive Maps (FCMs) are a convenient tool for modeling and simulating dynamic systems. FCMs were applied in a large number of diverse areas and have already gained momentu...
Wojciech Stach, Lukasz A. Kurgan, Witold Pedrycz
BMCBI
2010
147views more  BMCBI 2010»
15 years 6 months ago
baySeq: Empirical Bayesian methods for identifying differential expression in sequence count data
Background: High throughput sequencing has become an important technology for studying expression levels in many types of genomic, and particularly transcriptomic, data. One key w...
Thomas J. Hardcastle, Krystyna A. Kelly