Identification of plant precursor mirnas using structural robustness and secondary structures features
Conference proceedings article
ผู้เขียน/บรรณาธิการ
กลุ่มสาขาการวิจัยเชิงกลยุทธ์
ไม่พบข้อมูลที่เกี่ยวข้อง
รายละเอียดสำหรับงานพิมพ์
รายชื่อผู้แต่ง: Anuntakarun S., Wattanapornprom W., Lertampaiporn S.
ผู้เผยแพร่: Hindawi
ปีที่เผยแพร่ (ค.ศ.): 2017
Volume number: Part F131935
หน้าแรก: 1
หน้าสุดท้าย: 4
จำนวนหน้า: 4
ISBN: 9781450352970
นอก: 0146-9428
eISSN: 1745-4557
ภาษา: English-Great Britain (EN-GB)
บทคัดย่อ
This work presents an identification tool for plant precursor miRNAs (pre-miRNAs) using structural robustness and derivative features which can improve performance in discriminating the plant pre-miRNAs from pseudo pre-miRNAs. The classification models were trained with plant pre-miRNAs and pseudo hairpins datasets from PlantMiRNAPred web site. The top 20 features were selected from four groups of features including sequence-based features, secondary structure features, base-pair features and a self-containment index score. In particular, the self-containment index score was found to be the highest informative feature among the 20 selected features. Ten-fold cross validation was applied to choose a classifier algorithm with the highest performance being among Support Vector Machine, Random Forest, Decision Tree, Na๏ve Bayes, K-nearest neighbor, Back-propagation Neural Network, Ripper and RBF network based on the ROC area and accuracy. The results demonstrated that the Random Forest model using 20 selected features achieved 97% accuracy and 94% sensitivity in test sets to discriminating real plant pre-miRNAs from others. ฉ 2017 Association for Computing Machinery.
คำสำคัญ
Precursor miRNAs, Self-containment index score
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