Predicting the secondary structure of proteins using machine learning algorithms

No abstract is available for this paper.

• Publication year

  2012

• Venue

  International Journal of Data Mining and Bioinformatics

• Publication date

  2012-11-01

• Fields of study

  Biology, Medicine, Computer Science

• Identifiers
  DOI 10.1504/IJDMB.2012.050265PMID 23356008
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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  1999influential reference
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  1999cited by this paper
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  1999influential reference
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  1998cited by this paper
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  1995cited by this paper
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  1995cited by this paper
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