“Draft:The 5-step rules for proteome or genome analysis”的意思、由来-开放百科全书

The 5-Step Rules for proteome or genome analysis was originally proposed by Kuo-Chen Chou in 2011

that has been widely used for proteome and genome analyses as well as predicting posttranslational modification (PTM) sites in protein, RNA, and DNA sequences.

According to this rule, to develop a practically more useful statistical prediction method or predictor for genome or proteome analysis, one should observe the following five guidelines. (1) Construct or select a valid benchmark dataset to train and test the predictor. (2) Formulate the biological sequence samples with an effective mathematical expression that can truly reflect their intrinsic correlation with the target to be predicted. (3) Introduce or develop a powerful algorithm (or engine) to operate the prediction. (4) Properly perform cross-validation tests to objectively evaluate the anticipated accuracy of the predictor. (5) Establish a user-friendly web-server for the predictor that is accessible to the public. Ever since then, the 5-steps rule has been used by many scientists in developing various predictors for proteome or genome analyses, particularly by those who are formulating biological sequences with PseAAC or PseKNC to develop various predictors for proteome or genome analyses.

Papers presented for developing a new sequence-analyzing method or statistical predictor by observing the guidelines of Chou’s 5-strp rules have the following notable merits: (1) crystal clear in logic development, (2) completely transparent in operation, (3) easily to repeat the reported results by other investigators, (4) with high potential in stimulating other sequence-analyzing methods, and (5) very convenient to be used by the majority of experimental scientists.

Moreover, the Chou’s 5-step rule has been further extended to materials science for developing powerful method of detecting perovskite materials with higher Curie temperature as well.^[6]

References

1. ^="pmid21168420">{{cite journal |vauthors=Chou KC | title = Some remarks on protein attribute prediction and pseudo amino acid composition (50th Anniversary Year Review).| journal = J. Theor. Biol. | volume = 273 | pages = 236-47 |date=Feb 2011 | pmid = 21168420| doi = 10.1016/j.jtbi.2010.12.024}}
2. ^="pmid30768975">{{cite journal |vauthors=Hussain W, Khan YD, Rasool N, Khan SA, Chou KC | title = SPrenylC-PseAAC: A sequence-based model developed via Chou's 5-steps rule and general PseAAC for identifying S-prenylation sites in proteins.| journal = J. Theor. Biol. | volume = 468 | pages = 1-11 |date=Feb 2019 | pmid = 30768975| doi = 10.1016/j.jtbi.2019.02.007}}
3. ^{{cite journal |vauthors=Hussain W, Khan YD, Rasool N, Khan SA, Chou KC | title = SPalmitoylC-PseAAC: A sequence-based model developed via Chou's 5-steps rule and general PseAAC for identifying S-palmitoylation sites in proteins.| journal = Anal. Biochem.| volume = 568 | pages = 14-23 |date=Mar 2019 | pmid = 30593778| doi = 10.1016/j.ab.2018.12.019}}
4. ^{{cite journal |vauthors=((Le NQK)), ((Yapp EKY)), Ho QT, Nagasundaram N, Ou YY, Yeh HY | title = iEnhancer-5Step: Identifying enhancers using hidden information of DNA sequences via Chou's 5-step rule and word embedding.| journal = Anal. Biochem.| volume = 57 | pages = 53-61 |date=Feb 2019 | pmid = 30822398| doi = 10.1016/j.ab.2019.02.017}}
5. ^{{cite journal |vauthors=Ning Q, Ma Z, Zhao X | title = dForml(KNN)-PseAAC: Detecting Formylation sites from protein sequences using K-nearest neighbor algorithm via Chou's 5-step rule and Pseudo components.| journal = J. Theor. Biol. |date=Mar 2019 | pmid = 30880183| doi = 10.1016/j.jtbi.2019.03.011}}
6. ^Zhan, X., Chen, M., Lu, W. (2018). Accelerated search for perovskite materials with higher Curie temperature based on the machine learning methods. Computational Materials Science 151, 41-48.http://dx.doi.org/10.1016/j.commatsci.2018.04.031