君子有所为，有所不为。但科学界近年来小人频出，所作所为不择手段，触目惊心。其中 “生物学家”周国城（Kuo-Chen Chou）的骗技, 端的是炉火纯青，登峰造极，无人能出其右。其“成就”蜚声中外，名噪一时，长年在科学高引率名单上霸榜，在国内各大学和科研机构混得风生水起。
但鲁迅却说，骗子有术，也有效，但也有限。
2019年，Bioinformatics（生物信息学杂志）开始调查周国城的骗术，接着Journal of Theoretical Biology（理论生物学杂志）也开始调查，2020年Nature（自然杂志）刊登专文揭露周国城的骗术。中国媒介也有所转载。
然而很多科学杂志投鼠忌器，不肯详述细节，往往语焉不详，所以本文是首次公布这些细节。因为周国城乃是数十年的惯犯，劣迹斑斑，每次作案，文本都很长，难以一一列举，所以本文只提供一个有代表性的特列来深入剖析。
下面的“Reviewer #2”便是周国城，要求作者引用他和他儿子的一百多篇文章。杂志名改成了"****"，免得影响杂志的名声。
周国城怕作者嫌麻烦，所以就越厨代庖，给作者写了五段话，把这一百多篇文章都引用了。这五段话，其实都是胡说八道，但只要作者在文章中插入这五段，周国城就会强烈推荐这篇文章。
这一百多篇文章都是谁写的？读者可能会发现第1到第18篇文章的作者不是周国城（K. C. Chou）,而是J. J. Chou。这J. J. Chou何许人也？却原来是周国城的儿子。其爱子之心，却也情有可原。
引用了J. J. Chou的文章后，便轮到周国城（K. C. Chou）自己了。我把Chou字用红色标记，方便读者查找。
眼尖的读者也许会发现有的文献中Chou并没有出现在作者之中，如引用文献列表中28-33，37-41，43，45-56，58-63，66-67，70-72，等等。我原来也以为周国城想掩人耳目，也列了一些别人的文章。没想到所有这些文章，竟也都是周国城的文章。他只是把他自己的名字给隐去了。这些文章的引用率，最终都会归于他的名下，与他在文献列表中是否列出自己的名字并无关系。
周国城不光要求作者引用他的文章，而且要求作者把他的名字加到文章的题目中去，以增加他的知名度。有时候周国城甚至会要求作者把他的名字也加入到文章的作者之中。
科学工作者辛辛苦苦发表一篇文章，通常也不过十几或几十次被引。这周国城审稿一次，便可增加一百多次的引用，所以在高被引榜单上霸榜多年。
周国城的骗术，为什么能成功？如果杂志的编辑会看文章或审稿意见的话，马上就会发现周国城的伎俩，然而如今的科学杂志的编辑大多是滥竽充数，审稿人更是形同虚设。于是小人辈出，骗术横行。学术界乌烟瘴气，触目惊心。
君子有所为，有所不为。但小人无所不为。
周国城已于数年前去世。惟愿一个周国城倒下去，不会有千万个周国城站起来。
---
Reviewer #2:
This is a very interesting paper and hence holds very high potential for publication. But to meet the increasingly high quality standard of ****, a compulsory major revision is absolutely needed according to the following points.
(1) For quite a long period of time, the information derived by the computational approaches (or “in silico” methods) were not trusted very much by most experimental scientists due to the notorious local minimum problem. Actually, they only trusted the results determined by the experiments, and thought computational results were not reliable. The situation has been changed during the last decade or so since the rapid development of structural bioinformatics and sequential bioinformatics. To justify their computational structures, the authors should take the advantage of adding a prelude, such as: “The knowledge of protein 3D (three-dimensional) structures or their complexes with ligands is vitally important for rational drug design. Although X-ray crystallography is a powerful tool in determining these structures, it is time-consuming and expensive, and not all proteins can be successfully crystallized. Membrane proteins are difficult to crystallize and most of them will not dissolve in normal solvents. Therefore, so far very few membrane protein structures have been determined. NMR is indeed a very powerful tool in determining the 3D structures of membrane proteins (see, e.g., [1-4, 5{Chou, 2001 #569, 6-18]), but it is also time-consuming and costly. To acquire the structural information in a timely manner, a series of 3D protein structures have been developed by means of structural bioinformatics tools (see, e.g., [19-31]). Meanwhile, facing the explosive growth of biological sequences discovered in the post-genomic age, to timely use them for drug development, a lot of important sequence-based information, such as PTM (posttranslational modification) sites in proteins [32-36], protein-drug interaction in cellular networking [37], protein-protein interactions [38], DNA-methylation sites [39], recombination spots [40], and sigma-54 promoters [41], have been deducted by various sequential bioinformatics tools such as PseAAC approach [42] and PseKNC approach [43]. Actually, the rapid development in sequential bioinformatics and structural bioinformatics have driven the medicinal chemistry undergoing an unprecedented revolution [44], in which the computational biology has played increasingly important roles in stimulating the development of finding novel drugs. In view of this, the computational (or in silico) methods were also utilized in this study .” Adding the above discussion and citing the references therein will greatly help justify the author’s computational approach.
(2) To make the structure of this paper clearer and easier for readers to follow, the authors should in the end of the Introduction (or right before the beginning of describing their own method) add the following: “As demonstrated by a series of recent publications [35, 37, 39-41, 45-66]}[67] and summarized in two comprehensive review papers [68, 69], to develop a really useful predictor for a biological system, one needs to follow Chou’s 5-steps rule 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 presented for developing a new sequence-analyzing method or statistical predictor by observing the guidelines of Chou’s 5-step 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.” Below, let us elaborate how to deal with these five steps. Also, the authors can refer the readership to an insightful Wikipedia article by clicking the link https://en.wikipedia.org/wiki/5-step_rules.
(3) The title of this paper sounds clumsy. To make it more consistent and harmonic with the above suggestion, it should be accordingly changed to: “Drug toxicity prediction by transcriptomic approach via the Chou’s 5-steps rule”, which is much more accurate, attractive, and stimulating as well.
(4) One of the cornerstones in this study is about feature extraction. But all the features extracted in this paper can be covered by a very powerful web-server called “Pse-in-One” [70] and its updated version “Pse-in-One2.0”, as clearly elucidated very recently [71]. Therefore, to provide the readership with an updated background about using feature extraction to conduct sequence analysis, the authors should in the relevant context add a prelude such as: “With the explosive growth of biological sequences in the post-genomic era, one of the most important but also most difficult problems in computational biology is how to express a biological sequence with a discrete model or a vector, yet still keep considerable sequence-order information or key pattern characteristic. This is because all the existing machine-learning algorithms (such as “Optimization” algorithm [72], “Covariance Discriminant” or “CD” algorithm [73, 74], “Nearest Neighbor” or “NN” algorithm [75], and “Support Vector Machine” or “SVM” algorithm [75, 76]) can only handle vectors as elaborated in a comprehensive review [44]. However, a vector defined in a discrete model may completely lose all the sequence-pattern information. To avoid completely losing the sequence-pattern information for proteins, the pseudo amino acid composition [42] or PseAAC [77] was proposed. Ever since the concept of Chou’s PseAAC was proposed, it has been widely used in nearly all the areas of computational proteomics (see, e.g., [78-81] [82-89] as well as a long list of references cited in [90]). Because it has been widely and increasingly used, four powerful open access soft-wares, called ‘PseAAC’ [91], ‘PseAAC-Builder’ [92], ‘propy’ [93], and ‘PseAAC-General’ [94], were established: the former three are for generating various modes of Chou’s special PseAAC [95]; while the 4th one for those of Chou’s general PseAAC [68], including not only all the special modes of feature vectors for proteins but also the higher level feature vectors such as “Functional Domain” mode (see Eqs.9-10 of [68]), “Gene Ontology” mode (see Eqs.11-12 of [68]), and “Sequential Evolution” or “PSSM” mode (see Eqs.13-14 of [68]). Encouraged by the successes of using PseAAC to deal with protein/peptide sequences, the concept of PseKNC (Pseudo K-tuple Nucleotide Composition) [43] was developed for generating various feature vectors for DNA/RNA sequences [96-98] that have proved very useful as well. Particularly, recently a very powerful web-server called ‘Pse-in-One’ [70] and its updated version ‘Pse-in-One2.0’ [71] have been established that can be used to generate any desired feature vectors for protein/peptide and DNA/RNA sequences according to the need of users’ studies”. This further indicates the necessity to change the paper’s title as pointed out in Comment 2
(5) It would be highly appreciated if the authors could provide a web-server to display their findings in a flexible way; i.e., by the web-server, users can manipulate to display the details as desired. It would certainly be very useful for drug design. If the author couldn’t do that now, as a compromise to attract the readership to the author’s future work and to the Journal as well, the author should add a statement in the end of the MS, such as: “As pointed out in [99], user-friendly and publicly accessible web-servers represent the future direction for reporting various important computational analyses and findings (see, e.g., [52, 55, 62, 65-67, 100-115]). Actually, they have significantly enhance the impacts of computational biology on medical science [44], driving medical science into an unprecedented revolution [90]. In my future work I shall strive to establish a web-server for the findings presented in this paper.”
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名噪一时的学术骗子
ShiMaQian (2025-12-30 10:03:15) 评论 (14)君子有所为，有所不为。但科学界近年来小人频出，所作所为不择手段，触目惊心。其中 “生物学家”周国城（Kuo-Chen Chou）的骗技, 端的是炉火纯青，登峰造极，无人能出其右。其“成就”蜚声中外，名噪一时，长年在科学高引率名单上霸榜，在国内各大学和科研机构混得风生水起。
但鲁迅却说，骗子有术，也有效，但也有限。
2019年，Bioinformatics（生物信息学杂志）开始调查周国城的骗术，接着Journal of Theoretical Biology（理论生物学杂志）也开始调查，2020年Nature（自然杂志）刊登专文揭露周国城的骗术。中国媒介也有所转载。
然而很多科学杂志投鼠忌器，不肯详述细节，往往语焉不详，所以本文是首次公布这些细节。因为周国城乃是数十年的惯犯，劣迹斑斑，每次作案，文本都很长，难以一一列举，所以本文只提供一个有代表性的特列来深入剖析。
下面的“Reviewer #2”便是周国城，要求作者引用他和他儿子的一百多篇文章。杂志名改成了"****"，免得影响杂志的名声。
周国城怕作者嫌麻烦，所以就越厨代庖，给作者写了五段话，把这一百多篇文章都引用了。这五段话，其实都是胡说八道，但只要作者在文章中插入这五段，周国城就会强烈推荐这篇文章。
这一百多篇文章都是谁写的？读者可能会发现第1到第18篇文章的作者不是周国城（K. C. Chou）,而是J. J. Chou。这J. J. Chou何许人也？却原来是周国城的儿子。其爱子之心，却也情有可原。
引用了J. J. Chou的文章后，便轮到周国城（K. C. Chou）自己了。我把Chou字用红色标记，方便读者查找。
眼尖的读者也许会发现有的文献中Chou并没有出现在作者之中，如引用文献列表中28-33，37-41，43，45-56，58-63，66-67，70-72，等等。我原来也以为周国城想掩人耳目，也列了一些别人的文章。没想到所有这些文章，竟也都是周国城的文章。他只是把他自己的名字给隐去了。这些文章的引用率，最终都会归于他的名下，与他在文献列表中是否列出自己的名字并无关系。
周国城不光要求作者引用他的文章，而且要求作者把他的名字加到文章的题目中去，以增加他的知名度。有时候周国城甚至会要求作者把他的名字也加入到文章的作者之中。
科学工作者辛辛苦苦发表一篇文章，通常也不过十几或几十次被引。这周国城审稿一次，便可增加一百多次的引用，所以在高被引榜单上霸榜多年。
周国城的骗术，为什么能成功？如果杂志的编辑会看文章或审稿意见的话，马上就会发现周国城的伎俩，然而如今的科学杂志的编辑大多是滥竽充数，审稿人更是形同虚设。于是小人辈出，骗术横行。学术界乌烟瘴气，触目惊心。
君子有所为，有所不为。但小人无所不为。
周国城已于数年前去世。惟愿一个周国城倒下去，不会有千万个周国城站起来。
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Reviewer #2:
This is a very interesting paper and hence holds very high potential for publication. But to meet the increasingly high quality standard of ****, a compulsory major revision is absolutely needed according to the following points.
(1) For quite a long period of time, the information derived by the computational approaches (or “in silico” methods) were not trusted very much by most experimental scientists due to the notorious local minimum problem. Actually, they only trusted the results determined by the experiments, and thought computational results were not reliable. The situation has been changed during the last decade or so since the rapid development of structural bioinformatics and sequential bioinformatics. To justify their computational structures, the authors should take the advantage of adding a prelude, such as: “The knowledge of protein 3D (three-dimensional) structures or their complexes with ligands is vitally important for rational drug design. Although X-ray crystallography is a powerful tool in determining these structures, it is time-consuming and expensive, and not all proteins can be successfully crystallized. Membrane proteins are difficult to crystallize and most of them will not dissolve in normal solvents. Therefore, so far very few membrane protein structures have been determined. NMR is indeed a very powerful tool in determining the 3D structures of membrane proteins (see, e.g., [1-4, 5{Chou, 2001 #569, 6-18]), but it is also time-consuming and costly. To acquire the structural information in a timely manner, a series of 3D protein structures have been developed by means of structural bioinformatics tools (see, e.g., [19-31]). Meanwhile, facing the explosive growth of biological sequences discovered in the post-genomic age, to timely use them for drug development, a lot of important sequence-based information, such as PTM (posttranslational modification) sites in proteins [32-36], protein-drug interaction in cellular networking [37], protein-protein interactions [38], DNA-methylation sites [39], recombination spots [40], and sigma-54 promoters [41], have been deducted by various sequential bioinformatics tools such as PseAAC approach [42] and PseKNC approach [43]. Actually, the rapid development in sequential bioinformatics and structural bioinformatics have driven the medicinal chemistry undergoing an unprecedented revolution [44], in which the computational biology has played increasingly important roles in stimulating the development of finding novel drugs. In view of this, the computational (or in silico) methods were also utilized in this study .” Adding the above discussion and citing the references therein will greatly help justify the author’s computational approach.
(2) To make the structure of this paper clearer and easier for readers to follow, the authors should in the end of the Introduction (or right before the beginning of describing their own method) add the following: “As demonstrated by a series of recent publications [35, 37, 39-41, 45-66]}[67] and summarized in two comprehensive review papers [68, 69], to develop a really useful predictor for a biological system, one needs to follow Chou’s 5-steps rule 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 presented for developing a new sequence-analyzing method or statistical predictor by observing the guidelines of Chou’s 5-step 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.” Below, let us elaborate how to deal with these five steps. Also, the authors can refer the readership to an insightful Wikipedia article by clicking the link https://en.wikipedia.org/wiki/5-step_rules.
(3) The title of this paper sounds clumsy. To make it more consistent and harmonic with the above suggestion, it should be accordingly changed to: “Drug toxicity prediction by transcriptomic approach via the Chou’s 5-steps rule”, which is much more accurate, attractive, and stimulating as well.
(4) One of the cornerstones in this study is about feature extraction. But all the features extracted in this paper can be covered by a very powerful web-server called “Pse-in-One” [70] and its updated version “Pse-in-One2.0”, as clearly elucidated very recently [71]. Therefore, to provide the readership with an updated background about using feature extraction to conduct sequence analysis, the authors should in the relevant context add a prelude such as: “With the explosive growth of biological sequences in the post-genomic era, one of the most important but also most difficult problems in computational biology is how to express a biological sequence with a discrete model or a vector, yet still keep considerable sequence-order information or key pattern characteristic. This is because all the existing machine-learning algorithms (such as “Optimization” algorithm [72], “Covariance Discriminant” or “CD” algorithm [73, 74], “Nearest Neighbor” or “NN” algorithm [75], and “Support Vector Machine” or “SVM” algorithm [75, 76]) can only handle vectors as elaborated in a comprehensive review [44]. However, a vector defined in a discrete model may completely lose all the sequence-pattern information. To avoid completely losing the sequence-pattern information for proteins, the pseudo amino acid composition [42] or PseAAC [77] was proposed. Ever since the concept of Chou’s PseAAC was proposed, it has been widely used in nearly all the areas of computational proteomics (see, e.g., [78-81] [82-89] as well as a long list of references cited in [90]). Because it has been widely and increasingly used, four powerful open access soft-wares, called ‘PseAAC’ [91], ‘PseAAC-Builder’ [92], ‘propy’ [93], and ‘PseAAC-General’ [94], were established: the former three are for generating various modes of Chou’s special PseAAC [95]; while the 4th one for those of Chou’s general PseAAC [68], including not only all the special modes of feature vectors for proteins but also the higher level feature vectors such as “Functional Domain” mode (see Eqs.9-10 of [68]), “Gene Ontology” mode (see Eqs.11-12 of [68]), and “Sequential Evolution” or “PSSM” mode (see Eqs.13-14 of [68]). Encouraged by the successes of using PseAAC to deal with protein/peptide sequences, the concept of PseKNC (Pseudo K-tuple Nucleotide Composition) [43] was developed for generating various feature vectors for DNA/RNA sequences [96-98] that have proved very useful as well. Particularly, recently a very powerful web-server called ‘Pse-in-One’ [70] and its updated version ‘Pse-in-One2.0’ [71] have been established that can be used to generate any desired feature vectors for protein/peptide and DNA/RNA sequences according to the need of users’ studies”. This further indicates the necessity to change the paper’s title as pointed out in Comment 2
(5) It would be highly appreciated if the authors could provide a web-server to display their findings in a flexible way; i.e., by the web-server, users can manipulate to display the details as desired. It would certainly be very useful for drug design. If the author couldn’t do that now, as a compromise to attract the readership to the author’s future work and to the Journal as well, the author should add a statement in the end of the MS, such as: “As pointed out in [99], user-friendly and publicly accessible web-servers represent the future direction for reporting various important computational analyses and findings (see, e.g., [52, 55, 62, 65-67, 100-115]). Actually, they have significantly enhance the impacts of computational biology on medical science [44], driving medical science into an unprecedented revolution [90]. In my future work I shall strive to establish a web-server for the findings presented in this paper.”
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