iDRP-PseAAC: Identification of DNA Replication Proteins Using General PseAAC and Position Dependent Features
Abstract DNA replication is one of the specific processes to be considered in all the living organisms, specifically eukaryotes. The prevalence of DNA replication is significant for an evolutionary transition at the beginning of life. DNA replication proteins are those proteins which support the process of replication and are also reported to be important in drug design and discovery. This information depicts that DNA replication proteins have a very important role in human bodies, however, to study their mechanism, their identification is necessary. Thus, it is a very important task but, in any case, an experimental identification is time-consuming, highly-costly and laborious. To cope with this issue, a computational methodology is required for prediction of these proteins, however, no prior method exists. This study comprehends the construction of novel prediction model to serve the proposed purpose. The prediction model is developed based on the artificial neural network by integrating the position relative features and sequence statistical moments in PseAAC for training neural networks. Highest overall accuracy has been achieved through tenfold cross-validation and Jackknife testing that was computed to be 96.22% and 98.56%, respectively. Our astonishing experimental results demonstrated that the proposed predictor surpass the existing models that can be served as a time and cost-effective stratagem for designing novel drugs to strike the contemporary bacterial infection..
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2021 |
|---|---|
| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:27 |
|---|---|
| Enthalten in: |
Letters in peptide science - 27(2021), 2 vom: 08. Feb., Seite 1315-1329 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Amin, Arqam [VerfasserIn] |
|---|
| Links: |
Volltext [lizenzpflichtig] |
|---|
| BKL: | |
|---|---|
| Themen: |
5-Steps rule |
| doi: |
10.1007/s10989-021-10170-7 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
SPR043781012 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | SPR043781012 | ||
| 003 | DE-627 | ||
| 005 | 20230519185430.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 210417s2021 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s10989-021-10170-7 |2 doi | |
| 035 | |a (DE-627)SPR043781012 | ||
| 035 | |a (DE-599)SPRs10989-021-10170-7-e | ||
| 035 | |a (SPR)s10989-021-10170-7-e | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 540 |q ASE |
| 084 | |a 35.76 |2 bkl | ||
| 100 | 1 | |a Amin, Arqam |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a iDRP-PseAAC: Identification of DNA Replication Proteins Using General PseAAC and Position Dependent Features |
| 264 | 1 | |c 2021 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 520 | |a Abstract DNA replication is one of the specific processes to be considered in all the living organisms, specifically eukaryotes. The prevalence of DNA replication is significant for an evolutionary transition at the beginning of life. DNA replication proteins are those proteins which support the process of replication and are also reported to be important in drug design and discovery. This information depicts that DNA replication proteins have a very important role in human bodies, however, to study their mechanism, their identification is necessary. Thus, it is a very important task but, in any case, an experimental identification is time-consuming, highly-costly and laborious. To cope with this issue, a computational methodology is required for prediction of these proteins, however, no prior method exists. This study comprehends the construction of novel prediction model to serve the proposed purpose. The prediction model is developed based on the artificial neural network by integrating the position relative features and sequence statistical moments in PseAAC for training neural networks. Highest overall accuracy has been achieved through tenfold cross-validation and Jackknife testing that was computed to be 96.22% and 98.56%, respectively. Our astonishing experimental results demonstrated that the proposed predictor surpass the existing models that can be served as a time and cost-effective stratagem for designing novel drugs to strike the contemporary bacterial infection. | ||
| 650 | 4 | |a DNA replication |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Replication proteins |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a PseAAC |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a 5-Steps rule |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Prediction |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Awais, Muhammad |e verfasserin |4 aut | |
| 700 | 1 | |a Sahai, Shalini |e verfasserin |4 aut | |
| 700 | 1 | |a Hussain, Waqar |e verfasserin |4 aut | |
| 700 | 1 | |a Rasool, Nouman |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Letters in peptide science |d Dordrecht [u.a.] : Springer Science + Business Media B.V, 1994 |g 27(2021), 2 vom: 08. Feb., Seite 1315-1329 |w (DE-627)SPR015385973 |w (DE-600)2007589-3 |x 1573-496X |7 nnns |
| 773 | 1 | 8 | |g volume:27 |g year:2021 |g number:2 |g day:08 |g month:02 |g pages:1315-1329 |
| 856 | 4 | 0 | |u https://dx.doi.org/10.1007/s10989-021-10170-7 |z lizenzpflichtig |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_SPRINGER | ||
| 912 | |a SSG-OLC-PHA | ||
| 936 | b | k | |a 35.76 |q ASE |
| 951 | |a AR | ||
| 952 | |d 27 |j 2021 |e 2 |b 08 |c 02 |h 1315-1329 | ||