一言堂
此一言堂非彼一言堂也。此一言堂，乃是萬言堂中之一分子。無此一堂之言，便無百家之爭。故君子“寧鳴而死，不默而生”。（歡迎轉載，但請注明出處）
君子有所為，有所不為。但科學界近年來小人頻出，所作所為不擇手段，觸目驚心。其中 “生物學家”周國城（Kuo-Chen Chou）的騙技, 端的是爐火純青，登峰造極，無人能出其右。其“成就”蜚聲中外，名噪一時，長年在科學高引率名單上霸榜，在國內各大學和科研機構混得風生水起。
但魯迅卻說，騙子有術，也有效，但也有限。
2019年，Bioinformatics（生物信息學雜誌）開始調查周國城的騙術，接著Journal of Theoretical Biology（理論生物學雜誌）也開始調查，2020年Nature（自然雜誌）刊登專文揭露周國城的騙術。中國媒介也有所轉載。
然而很多科學雜誌投鼠忌器，不肯詳述細節，往往語焉不詳，所以本文是首次公布這些細節。因為周國城乃是數十年的慣犯，劣跡斑斑，每次作案，文本都很長，難以一一列舉，所以本文隻提供一個有代表性的特列來深入剖析。
下麵的“Reviewer #2”便是周國城，要求作者引用他和他兒子的一百多篇文章。雜誌名改成了"****"，免得影響雜誌的名聲。
周國城怕作者嫌麻煩，所以就越廚代庖，給作者寫了五段話，把這一百多篇文章都引用了。這五段話，其實都是胡說八道，但隻要作者在文章中插入這五段，周國城就會強烈推薦這篇文章。
這一百多篇文章都是誰寫的？讀者可能會發現第1到第18篇文章的作者不是周國城（K. C. Chou）,而是J. J. Chou。這J. J. Chou何許人也？卻原來是周國城的兒子。其愛子之心，卻也情有可原。
引用了J. J. Chou的文章後，便輪到周國城（K. C. Chou）自己了。我把Chou字用紅色標記，方便讀者查找。
眼尖的讀者也許會發現有的文獻中Chou並沒有出現在作者之中，如引用文獻列表中28-33，37-41，43，45-56，58-63，66-67，70-72，等等。我原來也以為周國城想掩人耳目，也列了一些別人的文章。沒想到所有這些文章，竟也都是周國城的文章。他隻是把他自己的名字給隱去了。這些文章的引用率，最終都會歸於他的名下，與他在文獻列表中是否列出自己的名字並無關係。
周國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題目中去，以增加他的知名度。有時候周國城甚至會要求作者把他的名字也加入到文章的作者之中。
科學工作者辛辛苦苦發表一篇文章，通常也不過十幾或幾十次被引。這周國城審稿一次，便可增加一百多次的引用，所以在高被引榜單上霸榜多年。
周國城的騙術，為什麽能成功？如果雜誌的編輯會看文章或審稿意見的話，馬上就會發現周國城的伎倆，然而如今的科學雜誌的編輯大多是濫竽充數，審稿人更是形同虛設。於是小人輩出，騙術橫行。學術界烏煙瘴氣，觸目驚心。
君子有所為，有所不為。但小人無所不為。
周國城已於數年前去世。惟願一個周國城倒下去，不會有千萬個周國城站起來。
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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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方舟子曾經是個有抱負的才子，可惜心胸略嫌狹窄，在個人恩怨上浪費太多時間精力。但話說回來，又有多少人能跳出個人恩怨呢？
——-
方舟子在教條的模版上胡說八道，謬論百出，從這個角度來講，他是個打著打假旗號胡扯爛八道的學術渣子。
騙子們為一己之私，敗壞了廣大華人科學家的名聲，最終得不償失，害了他自己，也害了他的親友。中國曆史上的悲劇，多是因為有人以小人手段，營造虛假繁榮所致。
騙引用率不是騙子嗎？網站（https://polecopub.hypotheses.org/tag/fraud）給了周國城一個頭銜：大騙子中的新冠軍（new champion of great fraudsters)
騙引用率不是騙子嗎？莫非竊書不能算偷？