A machine learning approach to identify hydrogenosomal proteins in trichomonas vaginalis

Eukaryotic Cell

Volumn 11, Issue 2, 2012, Pages 217-228

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

^a TEL AVIV UNIVERSITY   (Israel)

^b HEINRICH HEINE UNIVERSITY   (Germany)

^c NATIONAL EVOLUTIONARY SYNTHESIS CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

PROTOZOAL PROTEIN;

AMINO ACID SEQUENCE; ARTICLE; ARTIFICIAL INTELLIGENCE; CHEMISTRY; GENE; GENETICS; METABOLISM; MOLECULAR GENETICS; OPEN READING FRAME; PHYLOGENY; SEQUENCE ALIGNMENT; TRICHOMONAS VAGINALIS;

AMINO ACID SEQUENCE; ARTIFICIAL INTELLIGENCE; GENES, PROTOZOAN; MOLECULAR SEQUENCE DATA; OPEN READING FRAMES; PHYLOGENY; PROTOZOAN PROTEINS; SEQUENCE ALIGNMENT; TRICHOMONAS VAGINALIS;

PROTOZOA; TRICHOMONAS VAGINALIS;

EID: 84856669080     PISSN: 15359778     EISSN: None     Source Type: Journal    
DOI: 10.1128/EC.05225-11     Document Type: Article

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