A machine learning approach to identify hydrogenosomal proteins in Trichomonas vaginalis.

Burstein, David; Gould, Sven B; Zimorski, Verena; Kloesges, Thorsten; Kiosse, Fuat; Major, Peter; Martin, William F; Pupko, Tal; Dagan, Tal

Burstein, David; Gould, Sven B; Zimorski, Verena; Kloesges, Thorsten; Kiosse, Fuat; Major, Peter; Martin, William F; Pupko, Tal; Dagan, Tal.

Affiliation

Burstein D; Department of Cell Research and Immunology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel.

Eukaryot Cell ; 11(2): 217-28, 2012 Feb.

Article in En | MEDLINE | ID: mdl-22140228

Subject(s)

Artificial Intelligence; Protozoan Proteins/chemistry; Trichomonas vaginalis/metabolism; Amino Acid Sequence; Genes, Protozoan; Molecular Sequence Data; Open Reading Frames; Phylogeny; Protozoan Proteins/genetics; Protozoan Proteins/metabolism; Sequence Alignment

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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Trichomonas vaginalis / Artificial Intelligence / Protozoan Proteins Type of study: Prognostic_studies Language: En Journal: Eukaryot Cell Journal subject: BIOLOGIA MOLECULAR Year: 2012 Type: Article Affiliation country: Israel

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