Natural Science
Vol.12 No.03(2020), Article ID:98657,17 pages
10.4236/ns.2020.123008

Using Similarity Software to Evaluate Scientific Paper Quality Is a Big Mistake

Kuo-Chen Chou

Gordon Life Science Institute, Boston, Massachusetts 02478, United States of America

Correspondence to: Kuo-Chen Chou,

Copyright © 2020 by author(s) and Scientific Research Publishing Inc.

This work is licensed under the Creative Commons Attribution International License (CC BY 4.0).

http://creativecommons.org/licenses/by/4.0/

Received: February 20, 2020 ; Accepted: March 1, 2020 ; Published: March 4, 2020

ABSTRACT

Using similarity software to examine the quality of scientific papers is a nuisance. The significance of a scientific paper should be decided by the acknowledged experts. The practice of using the computer program to decide scientific papers must be rescinded or voided.

Keywords:

Similarity Check, 5-Steps Rule, PseAAC, PseKNC, Molecular Biology

1. INTRODUCTION

The problem by using the similarity software to evaluate the quality of scientific papers is as follows. 1) For the classic laws, theorems [1], and rules [2], it is not allowed to change even one word. 2) In contrast, by using different words with essentially the same contents or ideas as the classic ones, so as to claim new findings or discovery to steal the credit. Unfortunately, the similarity software is unable to detect this kind of cheatings. 3) In many music tunes composed by Bach Johann Sebastian and Wolfgang Amadeus Mozart (two of the most productive and influential composers for all time), their similarities are extremely high, but their tunes have been highly appreciated until now and even forever.

2. DISCUSSION

To develop a really useful prediction method or predictor for a biological system, one needs to go through the following five steps: 1) select or construct a valid benchmark dataset to train and test the predictor; 2) represent the samples with an effective formulation that can truly reflect their intrinsic correlation with the target to be predicted; 3) introduce or develop a powerful algorithm to conduct the prediction; 4) properly perform cross-validation tests to objectively evaluate the anticipated prediction accuracy; 5) establish a user-friendly web-server for the predictor that is accessible to the public. Papers written in compliance with the guidelines of the 5-steps rules, also named by many as “Chou’s 5-steps rule” [3 - 35], 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.

Also, one of the most challenging problems in computational biology today is how to effectively formulate the sequence of a biological sample (such as protein, peptide, DNA, or RNA) with a discrete model or a vector that can considerably keep its sequence order information or capture its key features. The reasons are as follows. 1) If using the sequential model, i.e., the model in which all the samples are represented by their original sequences, it is hardly able to train a machine that can cover all the possible cases concerned, as elaborated in [2]. 2) All the existing computational algorithms can only handle vector but not sequence samples. However, a vector defined in a discrete model may completely lose the sequence-order information. To cope with such a dilemma for proteomics and genomics systems, the approach of pseudo amino acid composition components [36 , 37] and pseudo K-tuple nucleotide components, called by many as “Chou’s PseAAC” and “PseKNC” [23 , 38 - 202], have been proposed.

3. CONCLUSION

Using similarity software to evaluate scientific paper quality is completely meaningless. The practice of using the computer program to decide scientific papers must be rescinded or voided. The significance of a scientific paper should be decided by the acknowledged experts as well as by whether it is in compliance with the “5-steps rule”, as indicated by some very impressive papers [203 - 210] and a series of very recent papers (see, e.g., [211 - 226]).

CONFLICTS OF INTEREST

The author declares no conflicts of interest regarding the publication of this paper.

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  195. 195. Srivastava, A., Kumar, R. and Kumar, M. (2018) BlaPred: Predicting and Classifying Beta-Lactamase Using a 3-Tier Prediction System via Chou’s General PseAAC. Journal of Theoretical Biology, 457, 29-36. https://doi.org/10.1016/j.jtbi.2018.08.030

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  198. 198. Behbahani, M., Nosrati, M., Moradi, M. and Mohabatkar, H. (2019) Using Chou’s General Pseudo Amino Acid Composition to Classify Laccases from Bacterial and Fungal Sources via Chou’s Five-Step Rule. Applied Biochemistry and Biotechnology. https://doi.org/10.1007/s12010-019-03141-8

  199. 199. Chen, G., Cao, M., Yu, J., Guo, X. and Shi, S. (2019) Prediction and Functional Analysis of Prokaryote Lysine Acetylation Site by Incorporating Six Types of Features into Chou’s General PseAAC. Journal of Theoretical Biology, 461, 92-101. https://doi.org/10.1016/j.jtbi.2018.10.047

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  202. 202. Xiao, X., Cheng, X., Chen, G., Mao, Q. and Chou, K.C. (2019) pLoc_bal-mVirus: Predict Subcellular Localization of Multi-Label Virus Proteins by Chou’s General PseAAC and IHTS Treatment to Balance Training Dataset. Medicinal Chemistry, 15, 496-509. https://doi.org/10.2174/1573406415666181217114710

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  211. 211. Liu, B. (2018) BioSeq-Analysis: A Platform for DNA, RNA, and Protein Sequence Analysis Based on Machine Learning Approaches. Briefings in Bioinformatics, 20, 1280-1294.

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  213. 213. Chou, K.C. (2019) The Cradle of Gordon Life Science Institute and Its Development and Driving Force. Int J Biol Genetics, 1, 1-28.

  214. 214. Chou, K.C. (2019) Showcase to Illustrate How the Web-Server iDNA6mA-PseKNC Is Working. Journal of Pathology Research Reviews & Reports, 1, 1-15.

  215. 215. Chou, K.C. (2019) The pLoc_bal-mPlant Is a Powerful Artificial Intelligence Tool for Predicting the Subcellular Localization of Plant Proteins Purely Based on Their Sequence Information. International Journal of Nutrition Sciences, 4, 1037.

  216. 216. Chou, K.C., Cheng, X. and Xiao, X. (2019) pLoc_bal-mEuk: Predict Subcellular Localization of Eukaryotic Proteins by General PseAAC and Quasi-Balancing Training Dataset. Medicinal Chemistry, 15, 472-485. https://doi.org/10.2174/1573406415666181218102517

  217. 217. Chou, K.C. (2019) Showcase to Illustrate How the Web-Server iNitro-Tyr Is Working. Glo J of Com Sci and Infor Tec., 2, 1-16.

  218. 218. Chou, K.C. (2019) The pLoc_bal-mAnimal Is a Powerful Artificial Intelligence Tool for Predicting the Subcellular Localization of Animal Proteins Based on Their Sequence Information Alone. Scientific Journal of Biometrics & Biostatistics, 2, 1-13.

  219. 219. Chou, K.C. (2020) Showcase to Illustrate How the Webserver pLoc_bal-mEuk Is Working. Biomedical Journal of Scientific & Technical Research.

  220. 220. Chou, K.C. (2020) The pLoc_bal-mGneg Predictor Is a Powerful Web-Server for Identifying the Subcellular Localization of Gram-Negative Bacterial Proteins Based on their Sequences Information Alone. ijSci, 9, 27-34. https://doi.org/10.18483/ijSci.2248

  221. 221. Chou, K.C. (2020) How the Artificial Intelligence Tool iRNA-2methyl Is Working for RNA 2’-Omethylation Sites. Journal of Medical Care Research and Review, 3, 348-366.

  222. 222. Chou, K.-C. (2020) Showcase to Illustrate How the Web-Server iKcr-PseEns Is Working. Journal of Medical Care Research and Review, 3, 331-347.

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  224. 224. Chou, K.C. (2019) How the Artificial Intelligence Tool iSNO-PseAAC Is Working in Predicting the Cysteine S-Nitrosylation Sites in Proteins. Journal of Stem Cell Research and Medicine, 4, 1-9.

  225. 225. Chou, K.C. (2020) Showcase to Illustrate How the Web-Server iRNA-Methyl Is Working. Journal of Molecular Genetics, 3, 1-7.

  226. 226. Chou, K.C. (2020) How the Artificial Intelligence Tool iRNA-PseU Is Working in Predicting the RNA Pseudouridine Sites. Biomedical Journal of Scientific & Technical Research.