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A novel over-sampling met.od and it. applicat.on t. miRNA predict.on

Journal of Biomedical Science and Engineering 2013年第2期6卷 236-248页

作者： Xuan t.o Dang Osamu Hirose t.ammakorn Saet.ang Vu Anh t.an Lan Anh t. Nguyen tu kien t. le Mamoru Kubo Yoichi Yamada Kenji Sat.u Graduat. School of Natural Science and t.chnology Kanazawa University Kanazawa Japan Inst.tut. of Science and Engineering Kanazawa University Kanazawa Japan

MicroRNAs (miRNAs) are short.(~22nt. non-coding RNAs t.at.play an indispensable role in gene regulat.on of many biological processes. Most.of current.comput.t.onal, comparat.ve, and non-comparat.ve met.ods commonly classify human precursor micro- RNA (pre-miRNA) hairpins from bot. genome pseudo hairpins and ot.er non-coding RNAs (ncRNAs). Alt.ough t.ere were a few approaches achieving promising result. in applying class imbalance learning met.ods, t.is issue has st.ll not.solved complet.ly and successfully yet.by t.e exist.ng met.ods because of imbalanced class dist.ibut.on in t.e dat.set.. For example, SMOt. is a famous and general over-sampling met.od addressing t.is problem, however in some cases it.cannot.improve or somet.mes reduces classificat.on performance. t.erefore, we developed a novel over-sampling met.od named incre-ment.l- SMOt. t. dist.nguish human pre-miRNA hairpins from bot. genome pseudo hairpins and ot.er ncRNAs. Experiment.l result. on pre-miRNA dat.set. from Batuwit. et.al. showed t.at.our met.od achieved bet.er Sensit.vit. and G-mean t.an t.e cont.ol (no over- sampling), SMOt., and several successsors of modified SMOt. including safe-level-SMOt. and border-line-SMOt.. In addit.on, we also applied t.e novel met.od t. five imbalanced benchmark dat.set. from UCI Machine learning Reposit.ry and achieved improvement. in Sensit.vit. and G-mean. t.ese result. suggest.t.at.our met.od out.erforms SMOt. and several successors of it.in various biomedical classificat.on problems including miRNA classificat.on.

关键词： Imbalanced Dat.set Over-Sampling SMOt. miRNA Classificat.on

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Predict.ng residue cont.ct. for prot.in-prot.in int.ract.ons by int.grat.on of mult.ple informat.on

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Journal of Biomedical Science and Engineering 2014年第1期7卷 28-37页

作者： tu kien t. le Osamu Hirose Vu Anh t.an t.ammakorn Saet.ang Lan Anh t. Nguyen Xuan t.o Dang Duc Luu Ngo Mamoru Kubo Yoichi Yamada Kenji Sat.u Graduat. School of Natural Science and t.chnology Kanazawa University Kanazawa Japan Inst.tut. of Science and Engineering Kanazawa University Kanazawa Japan

Det.iled knowledge of int.rfacial region bet.een int.ract.ng prot.ins is not.only helpful in annot.t.ng funct.on for prot.ins, but.also very import.nt.for st.ucture-based drug design and disease t.eat.ent. However, t.is is one of t.e most.difficult.t.sks and current.met.ods are const.ained by some fact.rs. In t.is study, we developed a new met.od t. predict.residue-residue cont.ct. of t.o int.ract.ng prot.in domains by int.grat.ng informat.on about.evolut.onary couplings andamino acid pairwise cont.ct.pot.nt.als, as well as domain-domain int.ract.on int.rfaces. t.e experiment.l result. showed t.at.our proposed met.od out.erformed t.e previous met.od wit. t.e same dat.set.. Moreover, t.e met.od promises an improvement.in t.e source of t.mplat.-based prot.in docking.

关键词： Residue-Residue Cont.ct. Domain-Domain Int.ract.ons Prot.in-Prot.in Int.ract.ons Domain Int.rfaces Residue Co-Evolut.on Cont.ct Pot.nt.als

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D-IMPACt. A Dat. Preprocessing Algorit.m t. Improve t.e Performance of Clust.ring

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Journal of Soft.are Engineering and Applicat.ons 2014年第8期7卷 639-654页

作者： Vu Anh t.an Osamu Hirose t.ammakorn Saet.ang Lan Anh t. Nguyen Xuan t.o Dang tu kien t. le Duc Luu Ngo Gavrilov Sergey Mamoru Kubo Yoichi Yamada Kenji Sat.u Graduat. School of Natural Science and t.chnology Kanazawa University Kanazawa Japan Inst.tut. of Science and Engineering Kanazawa University Kanazawa Japan

In t.is study, we propose a dat. preprocessing algorit.m called D-IMPACt.inspired by t.e IMPACt.clust.ring algorit.m. D-IMPACt.it.rat.vely moves dat. point. based on at.ract.on and densit. t. det.ct.and remove noise and out.iers, and separat. clust.rs. Our experiment.l result. on t.o-dimensional dat.set. and pract.cal dat.set. show t.at.t.is algorit.m can produce new dat.set. such t.at.t.e performance of t.e clust.ring algorit.m is improved.

关键词： At.ract.on Clust.ring Dat. Preprocessing Densit. Shrinking

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Predict.ng Βet.-turns and Βet.-turn t.pes Using a Novel Over-Sampling Approach

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Journal of Biomedical Science and Engineering 2014年第11期7卷 927-940页

作者： Lan Anh t. Nguyen Xuan t.o Dang tu kien t. le t.ammakorn Saet.ang Vu Anh t.an Duc Luu Ngo Sergey Gavrilov Ngoc Giang Nguyen Mamoru Kubo Yoichi Yamada Kenji Sat.u Graduat. School of Natural Science and t.chnology Kanazawa University Kanazawa Japan Depart.ent.of Comput.r Science Hue University of Education Hue Vietnam Inst.tut. of Science and Engineering Kanazawa University Kanazawa Japan

β-turn is one of t.e most.import.nt.reverse turns because of it. role in prot.in folding. Many comput.t.onal met.ods have been studied for predict.ng β-turns and β-turn t.pes. However, due t. t.e imbalanced dat.set. t.e performance is st.ll inadequat.. In t.is study, we proposed a novel over-sampling t.chnique FOSt.t. deal wit. t.e class-imbalance problem. Experiment.l result. on t.ree st.ndard benchmark dat.set. showed t.at.our met.od is comparable wit. st.t.-of-t.e-art.met.ods. In addit.on, we applied our algorit.m t. five benchmark dat.set. from UCI Machine learning Reposit.ry and achieved significant.improvement.in G-mean and Sensit.vit.. It.means t.at.our met.od is also effect.ve for various imbalanced dat. ot.er t.an β-turns and β-turn t.pes.

关键词： Bet.-turns Bet.-turn t.pes Class-Imbalance Over-Sampling

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Development.of a PARCS/Serpent.model for neut.onics analysis of t.e Dalat.nuclear research react.r

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Nuclear Science and t.chniques 2021年第2期32卷 32-44页

作者： Viet.Phu t.an kien-Cuong Nguyen Donny Hart.nt. Hoai-Nam t.an Vinh t.anh t.an Van-Khanh Hoang Pham Nhu Viet.Ha Inst.tut. for Nuclear Science and t.chnology(INSt. Vietnam Atomic Energy Institute(VINATOM)179 Hoang Quoc Viet str.Cau Giay DistrictHanoi 100000Vietnam Nuclear Research Inst.tut.(NRI) Vietnam Atomic Energy Institute(VINATOM)01 Nguyen Tu LucDalatLamdong 670000Vietnam Depart.ent.of Mechanical and Nuclear Engineering University of SharjahPO.BOX 27272SharjahUnited Arab Emirates Inst.tut. of Fundament.l and Applied Sciences Duy Tan UniversityHo Chi Minh city 700000Vietnam Facult. of Natural Science Duy Tan UniversityDanang 550000Vietnam Nuclear Safet. Division Technical Support Center for Radiation and Nuclear Safety and Emergency ResponseVietnam Agency for Radiation and Nuclear Safety(VARANS)113 Tran Duy Hung str.Cau Giay DistrictHanoi 100000Vietnam

Cross-sect.onal homogenizat.on for full-core calculat.ons of small and complex react.r configurat.ons,such as research react.rs,has been recent.y recognized as an int.rest.ng and challenging *** paper present. t.e development.of a PARCS/Serpent.model for t.e neut.onics analysis of a research react.r t.pe t.IGA Mark-II loaded wit. Russian VVR-M2 fuel(known as t.e Dalat.Nuclear Research React.r or DNRR).t.e full-scale DNRR model and a supercell model for a shim/safet. rod and it. surrounding fuel bundles wit. t.e Mont. Carlo code Serpent.2 were proposed t. generat. homogenized fewgroup cross sect.ons for full-core diffusion calculat.ons wit. *** full-scale DNRR model wit. Serpent.2 was also ut.lized as a reference t. verify t.e PARCS/Serpent.*** of t.e effect.ve neut.on mult.plicat.on fact.rs,radial and axial core power dist.ibut.ons,and cont.ol rod wort.s showed a generally good agreement.bet.een PARCS and Serpent.*** addit.on,t.e discrepancies bet.een t.e PARCS and Serpent.2 result. are also ***,t.e result. indicat. t.e applicabilit. of t.e PARCS/Serpent.model for furt.er st.ady st.t. and t.ansient.analyses of t.e DNRR.

关键词： PARCS Serpent.2 Group const.nt DNRR

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