Computational Biology-Articles-open-access
Computational biology uses methods from a good range of mathematical and computational fields (e.g., complexity theory, algorithmics, machine learning, robotics, etc.) for biological systems (e.g., molecules, cells, tissues, organs, etc.). This often means watching a biological system during a new way, challenging current assumptions or theories about the relationships between parts of the system, or integrating different sources of data to form a more comprehensive
model than had been attempted before. During this context, it's worth noting that the first goal needn't be to extend human understanding of the system; even small biological systems are often sufficiently complex that scientists cannot fully comprehend or predict their properties. Thus the goal are often the creation of the
model itself; the
model should account for the maximum amount currently available experimental data as possible. Note that this doesn't mean that the
model has been proven, albeit the
model makes one or more correct predictions about new experiments. With the exception of very restricted cases, it's impossible to prove that a
model is correct, only to disprove it then improve it by modifying it to include the new results. This view emphasizes the importance of
machine learning for constructing models. In most current
machine learning applications, statistical and computational methods are wont to construct models from large existing datasets and people models are wont to process new data. Examples include learning to classify spam emails, to enable fingerprint access to your phone, and to acknowledge human speech. Current
computational biology research are often divided into variety of broad areas, mainly supported the sort of experimental data that's analyzed or modeled. Among these are analysis of protein and macromolecule structure and performance ,
gene and protein sequence,
evolutionary genomics and
proteomics, population genomics, regulatory and metabolic networks, biomedical image analysis.
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