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At that time, many different computational methods were developed by different scientists in the world, creating a problem of incongruence in taxonomic database systems.

Many different computational approaches were tried, and many linguistic resources have been considered as an aid for the solution of this problem.

Biological computational modellers are becoming increasingly interested in building large, eclectic models, including components on many different computational substrates, both biological and non-biological.

Currently, there is a wide range of endpoints available from predictive quantitative structure activity relationship models driven by many different computational software programs and data sources, and this is only expected to grow.

The experimental cost and the risk associated with poor selection of lead series can be greatly reduced by the use of many different computational and cheminformatic techniques to sort and prioritize compounds.

Besides different data sources, many different computational methods have also been employed for identifying disease genes.

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Atlas2 runs on many different computing platforms, from the standard desktop computer to highly scaled implementation with multimode computational clusters.

Hashing methods have been intensively studied and widely used in many different fields, including computer graphics, computational geometry, telecommunication, computer vision, especially for mobile visual search.

There are many different statistical and computational methods for determining the quality of attributes.

Thus, many researchers have attempted different computational approaches to accomplish such tasks including virtual screening based on PubChem bioactivity data [8] using the maximum unbiased validation datasets, predicting adverse drug reactions using PubChem bioassay data [9] and many others [10 13].

Many different molecular biology and computational techniques have been developed that attempt to circumvent the problems associated with short read length, while keeping the cost of genome sequencing projects low.

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