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Many previous studies have focused on predicting PPIs by developing computational methods.
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Therefore, developing computational methods, by integrating information on drugs, miRNAs and diseases, to predict potential drug-disease associations for drug repositioning is greatly needed.
Developing computational methods capable of predicting metabolic flux by integrating these data sources with a metabolic network is hence a major challenge of metabolic network modeling.
In fact, developing computational methods capable of predicting metabolic flux by integrating these data sources with a metabolic network is a major challenge of systems biology [ 18].
The rationale for developing computational methods for the prediction of secondary and tertiary structures of biopolymers emanates from the technical and cost limitations imposed by available biophysical methods.
Recently, attention has been paid to developing computational methods that can significantly accelerate the NMR data processing and reduce the errors introduced by manual processing.
Much progress has been made in developing computational methods that predict single locations for proteins.
New high-throughput experimental techniques, complemented by recently developed computational methods, have facilitated the initial reconstructions of large-scale cellular networks.
To address the problem, it is highly demanded by pharmaceutical industries to develop computational methods for predicting the side effects of drugs.
We have developed computational methods (Cell Line Authentication by SNP Profiling, CLASP) for cell line authentication and copy number analysis based on a cost-efficient SNP array, and we provide a reference database of commonly used mouse strains and cell lines.
Results: We assessed the utility of physical protein interactions for determining gene disease associations by examining the performance of seven recently developed computational methods (plus several of their variants).
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