A statistical approach for 5' splice site prediction using short sequence motifs and without encoding sequence data

BMC Bioinformatics

Volumn 15, Issue 1, 2014, Pages

  Meher, Prabina Kumar ^a   Sahu, Tanmaya Kumar ^a   Rao, Atmakuri Ramakrishna ^a   Wahi, Sant Dass ^a  

^a ICAR INDIAN AGRICULTURAL STATISTICS RESEARCH INSTITUTE   (India)

Author keywords

Di nucleotide associations; SAE; Short reads; Threshold value

Indexed keywords

ARTIFICIAL INTELLIGENCE; ENCODING (SYMBOLS); LEARNING ALGORITHMS; LEARNING SYSTEMS; MARKOV PROCESSES; MAXIMUM ENTROPY METHODS; NUCLEOTIDES;

COMPLEMENTARY METHODS; MACHINE LEARNING TECHNIQUES; MAXIMUM ENTROPY MODELS; NEXT-GENERATION SEQUENCING; SHORT READS; SPLICE SITE PREDICTION; SUM OF ABSOLUTE ERRORS; THRESHOLD-VALUE;

FORECASTING;

RNA SPLICING;

ALGORITHM; ARTIFICIAL INTELLIGENCE; BIOLOGY; GENETICS; HIGH THROUGHPUT SEQUENCING; HUMAN; MOLECULAR GENETICS; NUCLEOTIDE MOTIF; NUCLEOTIDE SEQUENCE; PROCEDURES; RECEIVER OPERATING CHARACTERISTIC; RNA SPLICING; SEQUENCE ANALYSIS; STATISTICAL MODEL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BASE SEQUENCE; COMPUTATIONAL BIOLOGY; HIGH-THROUGHPUT NUCLEOTIDE SEQUENCING; HUMANS; MODELS, STATISTICAL; MOLECULAR SEQUENCE DATA; NUCLEOTIDE MOTIFS; RNA SPLICE SITES; RNA SPLICING; ROC CURVE; SEQUENCE ANALYSIS, RNA;

EID: 84940543452     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/s12859-014-0362-6     Document Type: Article

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