|Table of Contents|

The Prediction of MiRNA Based on Pairwise Constrains Dimensionality Reduction(PDF)

《南京师大学报(自然科学版)》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2010年04期
Page:
166-171
Research Field:
计算机科学
Publishing date:

Info

Title:
The Prediction of MiRNA Based on Pairwise Constrains Dimensionality Reduction
Author(s):
Wei ShuangYang Ming
School of Computer Science and Technology,Nanjing Normal University,Nanjing 210046,China
Keywords:
m iRNA pairw ise constra ins d im ensiona lity reduction pred ic tion
PACS:
TP181
DOI:
-
Abstract:
M icroRNAs are a class of non-coding RNA s o f sing le-stranded, endogenous, wh ich p lay an im po rtant ro le in gene regu lation. The pred iction o fM icroRNAs w ill help a lot to study and understand their b io log ica l function. At present, very littlew ork has been done for the prediction o fm icroRNA s by using pa irw ise constra ins. The re fo re, a fter adding local struc ture preserv ing strategy, a d im ensiona lity reduction a lgo rithm based on pa irw ise constrains is introduced to improve the pred ic tion o f m icroRNAs e ffectively. Experim ental results on m icroRNA and UC I da ta sets va lidate the perform ance o f the proposed algor ithm.

References:

[ 1] F ire S Xu, M ontgom eryM, Kastas S, e t a .l Potent and specific gene tic interference by double-stranded RNA in Caeno rhabd-i tis e legans[ J]. Nature, 1998, 391( 6669): 806-811.
[ 2] Lee Y. M icroRNA m atura tion: stepw ise processing and subce llu la r lo ca lization[ J]. EM BO J, 2002, 21( 17): 4 663-4 670.
[ 3] Barte l D P. M icroRNA s: g enom ics, b iogenesis, m echan ism, and function[ J] . C el,l 2004, 116( 2): 281-297.
[ 4] Kur iha ra Y, W atanabeY. A rabidopsis m icro-RNA b iogenesis through D icer- like 1 prote in functions[ J]. ProcN atlAcad Sc,i 2004, 101( 3): 12 753-12 758.
[ 5] Zhang B. M icroRNA s and the ir regulatory roles in an im als and plants[ J]. J Ce ll Physio,l 2007, 210( 2) : 279-289.
[ 6] Tanzer A, Stadler P F. M o lecu la r evo lution of am icroRNA cluster[ J]. JM o l B io,l 2004, 339( 2) : 327-335.
[ 7] M o lnar A, Schw ach F, Studho lm e D J, et a.l M iRNAs con tro l gene expression in the sing le-ce ll a lga Ch lam ydom onas re inhardtii[ J]. Na ture, 2007, 447( 7148) : 1 126-1 129.
[ 8] Kren B T, W ong P Y, Sa rverA, et a.l m icroRNAs identified in high ly pur ified liver-der ived m itochondriam ay play a ro le in apoptosis[ J]. RNA B io ,l 2009, 6( 1): 65-72.
[ 9] G rosshansH, Slack F J. M icro-RNAs: sm a ll is plentiful[ J]. J Cell B io ,l 2002, 156( 1): 17-21.
[ 10] Lee C T, R isom T, StraussW M. Evo lutionary conse rvation o fm ic roRNA regulato ry c ircu its: an ex am ination o f m icroRNA gene com plex ity and conserved m icroRNA- target interactions through m etazoan phy logeny [ J]. DNA Ce ll B io,l 2007, 26 ( 4): 209-218.
[ 11] Vapnik V N. The Nature of Statistica l Learning Theory[M ] . 2nd ed. Now Yo rk: Spr inger-V erlag, 1999.
[ 12] B re im an L. Random forests[ J]. M ach Learn, 2001, 45( 1): 5-32.
[ 13] Xue C. C lass ification o f rea l and pseudo m icroRNA precursors using local structure- sequence features and support vector m achines[ J] . BMC B io inform atics, 2005, 6: 310.
[ 14] Ng K L S, M ishra S K. De novo SVM c lassifica tion o f precursorm icroRNAs from genom ic pseudo hairp ins using g loba l and intr insic fo ld ing m easures[ J]. B io in fo rm atics, 2007, 23( 11): 1 321-1 330.
[ 15] Pasa ila D, M oho rianu I, C iortuz L. Us ing base pa iring probabilities forM iRNA recogn ition[ C ] / / Proceed ing o f the 10 th In ternational Sym posium on Sym bo lic and Nume ric A lgo lithm fo r Sc ientific Computing. T im isoara: IEEE Con ference Publishing, 2008: 519-525.
[ 16] W ang X ium e,i Gao X inbo, Yuan Yuan, et a.l Sem -i superv ised Gauss ian process latent var iable m ode l based on pairw ise constra ints[ J] . Neurocom puting, 2010, 73: 2 186-2 195.
[ 17] Bar-h ille lA, H ertz T, Shenta l N, et a.l Learn ing a m aha lanob is m etr ic from equivalence constra in ts[ J]. Journa l ofM ach ine Learn ing Research, 2005, 6: 937-965.
[ 18] TangW e,i Zhong Sh.i Pairw ise constra ints-gu ided d im ensina lity reduc tion[ C] / / SDM W orkshop on Feature Se lection for DataM in ing. B ethesda, 2006.
[ 19] C ai Deng, H eX iao fe,i H an Jiawe .i Sem -i superv ised d iscrim inant analysis[ C] / / Proceed ings o f the 11th IEEE Inte rnational Conference on Com puter V is ion. R io de Janeiro, 2007.
[ 20] H o tellingH. Analysis of a comp lex of statistica l var iables into pr inc ipal components[ J] . Journal of Educationa lPsycho logy, 1933, 24( 1933) : 417-441.

Memo

Memo:
-
Last Update: 2013-04-08