Identifying the singleplex and multiplex proteins based on transductive learning for protein subcellular localization prediction
Abstract A new method is proposed to identify whether a query protein is singleplex or multiplex for improving the quality of protein subcellular localization prediction. Based on the transductive learning technique, this approach utilizes the information from the both query proteins and known proteins to estimate the subcellular location number of every query protein so that the singleplex and multiplex proteins can be recognized and distinguished. Each query protein is then dealt with by a targeted single-label or multi-label predictor to achieve a high-accuracy prediction result. We assess the performance of the proposed approach by applying it to three groups of protein sequences datasets. Simulation experiments show that the proposed approach can effectively identify the singleplex and multiplex proteins. Through a comparison, the reliably of this method for enhancing the power of predicting protein subcellular localization can also be verified..
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2013 |
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| Erschienen: |
2013 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:35 |
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| Enthalten in: |
Biotechnology letters - 35(2013), 7 vom: 12. Apr., Seite 1107-1113 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Cao, Junzhe [VerfasserIn] |
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| Links: |
Volltext [lizenzpflichtig] |
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| BKL: | |
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| Themen: |
Independent data set test |
| doi: |
10.1007/s10529-013-1186-6 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a Abstract A new method is proposed to identify whether a query protein is singleplex or multiplex for improving the quality of protein subcellular localization prediction. Based on the transductive learning technique, this approach utilizes the information from the both query proteins and known proteins to estimate the subcellular location number of every query protein so that the singleplex and multiplex proteins can be recognized and distinguished. Each query protein is then dealt with by a targeted single-label or multi-label predictor to achieve a high-accuracy prediction result. We assess the performance of the proposed approach by applying it to three groups of protein sequences datasets. Simulation experiments show that the proposed approach can effectively identify the singleplex and multiplex proteins. Through a comparison, the reliably of this method for enhancing the power of predicting protein subcellular localization can also be verified. | ||
| 650 | 4 | |a Independent data set test |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Multiplex protein |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Protein subcellular location prediction |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Singleplex protein |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Transductive learning |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Weighted neighborhood graph |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Liu, Wenqi |e verfasserin |4 aut | |
| 700 | 1 | |a He, Jianjun |e verfasserin |4 aut | |
| 700 | 1 | |a Gu, Hong |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Biotechnology letters |d Dordrecht [u.a.] : Springer Science + Business Media B.V, 1979 |g 35(2013), 7 vom: 12. Apr., Seite 1107-1113 |w (DE-627)SPR010833447 |w (DE-600)2012203-2 |x 1573-6776 |7 nnns |
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