Manuscript Topics

In the last decades, thousands of algorithms or methods related to all the applications and areas of computational biology (modeling, databases, pattern analysis, hypothesis testing, signal classification, machine learning, etc.) have been reported in the scientific literature.

However, most of these methods are just reported on algorithmic or mathematical form, and implementation issues are not usually considered. The performance is solely measured in terms of accuracy, but not in terms of speed, memory needs, or computational load. In addition, no information is provided in order to implement the method on a standalone computer application.

In this new era of big data, algorithm efficiency is becoming a major issue, and standard computational resources can't keep up with the pace of data availability. It does not suffice to have a very accurate method, but it should yield the results almost in a manageable amount of time and match the capabilities of the underlying hardware system.

The purpose of this special issue is to provide a platform for contributions related to the description of algorithms within the realm of computational biology from a new perspective, implementation issues using general purpose programming languages (C, C++, Java, Python, etc.), not just mathematical or statistical centered tools. Contributions will also have to include improvements in terms of computational cost, memory requirements, parameter optimization, parallelization, use of new computer technologies/capabilities, or even accuracy, but always with a clear focus on facilitating the implementation by the interested audience.

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