Combining-Structure-Based-Features-and-Conserved-D.ppt
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1、Combining Structure-Based Features and Conserved Data for MicroRNA Target Prediction by the Neural Network MethodShih-Yi Chao and Jung-Hsien Chiang*Department of Computer Science and Information Engineering National Cheng Kung University,Taiwan AgendanIntroductionnMaterialsnMethodsnExperimental Resu
2、ltsnConclusionsIntroductionnWhat are microRNAs?short non-coding of about 1925 nucleotides RNAs single-stranded RNAs generated from endogenous transcripts that can form local hairpin structuresnWhat do the microRNAs do?regulating gene expression in animals and plants.taking part in post-transcription
3、al regulation either by arresting the translation of messenger RNAs(mRNAs)or by the cleavage of mRNAs.how they recognize and regulate target genes remains less well understood IntroductionnThe motivationit is difficult to identify the functions of short microRAN target sequences by only performing p
4、rimary sequences alignment which means microRNAs contain only short complementary sequences,interrupted by gaps and mismatches.it may cause high false-positive rateMicroRNA target motifs are conserved more in structure than in primary sequences to consider the structure-based featuresmicroRNAs are o
5、ften highly conserved across a wide range of species to consider the conserved data across speciesMaterialsnExtraction of 3UTR sequences in Human Data are originally downloaded from NCBI web site at.We extract 3UTRs sequences,gene ids,gene official symbol names,gene alias names,accession numbers,and
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