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Traditionally, the computer architect improves the system performance by integrating multiple types of processing cores and memory systems.
His main research interest is to develop systems medicine approaches for understanding rheumatoid arthritis-perturbed networks in rheumatoid arthritis synovial tissues by integrating multiple types of genomic data sets, including mRNA and microRNA expression data.
Therefore, we may predict more interactions for PRDM9 and its partners by integrating multiple types of evidence.
These considerations motivated us to investigate and compare different principled machine learning approaches in order to improve the sensitivity and specificity of detecting ERα/SRC-1 DNA binding sites by integrating multiple types of information.
For example, Franke et al. and Linghu et al. separately constructed a functional linkage network (FLN) by integrating multiple types of data, such as protein-protein interaction, microarray and Gene Ontology annotation data, and utilized the FLN for disease gene prioritization [ 7, 23].
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In addition, we demonstrate that integrating multiple types of DTIs can significantly outperform other predictions either by simply mixing multiple types of interactions without distinction or using only a single interaction type.
Our study shows that integrating multiple types of data is a powerful approach to studying complex biological systems.
Integrating multiple types of omics data may allow for the development of increasingly accurate and robust molecular signatures.
Motivated by this work, we develop a novel data integration framework that integrates multiple types of molecular network data to reconstruct and update GO.
This network was constructed by integrating multiple data types including genetic studies and expression data from human and mouse liver and adipose tissue (Chen et al, 2008; Emilsson et al, 2008).
We integrate multiple types of input features by using appropriate neural network components to process each of them.
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