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ECSA
2010
Springer
15 years 7 months ago
Learning from the Cell Life-Cycle: A Self-adaptive Paradigm
In the software domain, self-adaptive systems are able to modify their behavior at run-time to respond to changes in the environment they run, to changes of the users' require...
Antinisca Di Marco, Francesco Gallo, Paola Inverar...
BMCBI
2010
179views more  BMCBI 2010»
15 years 7 months ago
A semi-supervised learning approach to predict synthetic genetic interactions by combining functional and topological properties
Background: Genetic interaction profiles are highly informative and helpful for understanding the functional linkages between genes, and therefore have been extensively exploited ...
Zhuhong You, Zheng Yin, Kyungsook Han, De-Shuang H...
145
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BMCBI
2010
98views more  BMCBI 2010»
15 years 7 months ago
Learning to predict expression efficacy of vectors in recombinant protein production
Background: Recombinant protein production is a useful biotechnology to produce a large quantity of highly soluble proteins. Currently, the most widely used production system is t...
Wen-Ching Chan, Po-Huang Liang, Yan-Ping Shih, Uen...
BMCBI
2010
109views more  BMCBI 2010»
15 years 7 months ago
Application of machine learning methods to histone methylation ChIP-Seq data reveals H4R3me2 globally represses gene expression
Background: In the last decade, biochemical studies have revealed that epigenetic modifications including histone modifications, histone variants and DNA methylation form a comple...
Xiaojiang Xu, Stephen Hoang, Marty W. Mayo, Stefan...
BMCBI
2008
134views more  BMCBI 2008»
15 years 7 months ago
Identification of transcription factor contexts in literature using machine learning approaches
Background: Availability of information about transcription factors (TFs) is crucial for genome biology, as TFs play a central role in the regulation of gene expression. While man...
Hui Yang, Goran Nenadic, John A. Keane