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 NS  Vol.12 No.6 , June 2020
pLoc_Deep-mEuk: Predict Subcellular Localization of Eukaryotic Proteins by Deep Learning
Abstract: Recently, the life of worldwide human beings has been endangering by the spreading of pneu- monia-causing virus, such as Coronavirus, COVID-19, and H1N1. To develop effective drugs against Coronavirus, knowledge of protein subcellular localization is prerequisite. In 2019, a predictor called “pLoc_bal-mEuk” was developed for identifying the subcellular localization of eukaryotic proteins. Its predicted results are significantly better than its counterparts, particularly for those proteins that may simultaneously occur or move between two or more subcellular location sites. However, more efforts are definitely needed to further improve its power since pLoc_bal-mEuk was still not trained by a “deep learning”, a very powerful technique developed recently. The present study was devoted to incorporating the “deep- learning” technique and developed a new predictor called “pLoc_Deep-mEuk”. The global absolute true rate achieved by the new predictor is over 81% and its local accuracy is over 90%. Both are overwhelmingly superior to its counterparts. Moreover, a user-friendly web- server for the new predictor has been well established at http://www.jci-bioinfo.cn/pLoc_Deep-mEuk/, by which the majority of experimental scientists can easily get their desired data.

Knowledge of the subcellular localization of proteins is crucially important for fulfilling the following two important goals: 1) revealing the intricate pathways that regulate biological processes at the cellular level [1,2]. 2) selecting the right targets [3] for developing new drugs.

With the avalanche of protein sequences in the post-genomic age, we are challenged to develop computational tools for effectively identifying their subcellular localization purely based on the sequence information.

In 2019, a very powerful predictor, called “pLoc_bal-mEuk” [4], was developed for predicting the subcellular localization of eukaryotic proteins based on their sequence information alone. It has the following remarkable advantages. 1) Most existing protein subcellular location prediction methods were developed based on the single-label system in which it was assumed that each constituent protein had one, and only one, subcellular location (see, e.g., [5-7] and a long list of references cited in a review papers [8]). With more experimental data uncovered, however, the localization of proteins in a cell is actually a multi-label system, where some proteins may simultaneously occur in two or more different location sites. This kind of multiplex proteins often bears some exceptional functions worthy of our special notice [2]. And the pLoc_bal-mEuk predictor [4] can cover this kind of important information missed by most other methods since it was established based on the multi-label benchmark dataset and theory. 2) Although there are a few methods (see, e.g., [9,10]) that can be used to deal with multi-label subcellular localization for eukaryotic proteins, the prediction quality achieved by pLoc_bal-mEuk [4] is overwhelmingly higher, particularly in the absolute true rate. 3) Although the pLoc_bal-mEuk predictor [4] has the aforementioned merits, it has not been trained at a deeper level yet [11-14].

The present study was initiated in an attempt to address this problem. As done in pLoc_bal-mEuk [4] as well as many other recent publications in developing new prediction methods (see, e.g., [12-57]), the guidelines of the 5-step rule [58] are followed. They are about the detailed procedures for 1) benchmark dataset, 2) sample formulation, 3) operation engine or algorithm, 4) cross-validation, and 5) web-server. But here our attentions are focused on the procedures that significantly differ from those in developing the predictor pLoc_bal-mEuk [4].

2. MATERIALS AND METHODS

2.1. Benchmark Dataset

The benchmark dataset used in this study is exactly the same as that in pLoc_bal-mEuk [4]; i.e.,

S = S 1 S 2 S u S 21 S 22 (1)

where S 1 only contains the protein samples from the “Acrosome” location, S 2 only contains those from the “Cell membrane” location, and so forth; denotes the symbol for “union” in the set theory. For readers’ convenience, their detailed sequences and accession numbers (or ID codes) are given in Supporting Information S1 that is also available at http://www.jci-bioinfo.cn/pLoc_bal-mEuk/Supp1.pdf, where none of proteins included has ≥25% sequence identity to any other in the same subset (subcellular location).

2.2. Proteins Sample Formulation

Now let us consider the 2nd step of the 5-step rule [58]; i.e., how to formulate the biological sequence samples with an effective mathematical expression that can truly reflect their essential correlation with the target concerned. Given a protein sequence P, its most straightforward expression is

P = R 1 R 2 R 3 R 4 R 5 R 6 R 7 R L (2)

where L denotes the protein’s length or the number of its constituent amino acid residues, R 1 is the 1st residue, R 2 the 2nd residue, R 3 the 3rd residue, and so forth. Since all the existing machine-learning algorithms} can only handle vectors as elaborated in [3], one has to convert a protein sample from its sequential expression (Equation (2)) to a vector. But a vector defined in a discrete model might completely miss all the sequence-order or pattern information. To deal with this problem, the Pseudo Amino Acid Composition [59] or PseAAC [60]. Ever since then, the concept of “Pseudo Amino Acid Composition” has been widely used in nearly all the areas of computational proteomics with the aim to grasp various different sequence patterns that are essential to the targets investigated (see, e.g., [4,10,23,24,61-227]). Because it has been widely and increasingly used, recently three powerful open access soft-wares, called “PseAAC- Builder” [93], “propy” [181], and “PseAAC-General” [120], were established: the former two are for generating various modes of special PseAAC [228]; while the 3rd one for those of general PseAAC [58], including not only all the special modes of feature vectors for proteins but also the higher level feature vectors such as “Functional Domain” mode, “Gene Ontology” mode, and “Sequential Evolution” or “PSSM” mode. Encouraged by the successes of using PseAAC to deal with protein/peptide sequences, its idea and approach were extended to PseKNC (Pseudo K-tuple Nucleotide Composition) to generate various feature vectors for DNA/RNA sequences [229] that have proved very successful as well [141,146,147,230-238]. According to the concept of general PseAAC [58], any protein sequence can be formulated as a PseAAC vector given by

P = [ Ψ 1 Ψ 2 Ψ u Ψ Ω ] T (3)

where T is a transpose operator, while the integer Ω is a parameter and its value as well as the components Ψ u ( u = 1 , 2 , , Ω ) will depend on how to extract the desired information from the amino acid sequence of P, as elaborated in [4]. Thus, by following exactly the same procedures as described in the Section 2.2 of [4], each of the protein samples in the benchmark dataset can be uniquely defined as a 22-D numerical vector as given in columns 3-24 of Supporting Information S2, which can also be directly downloaded at http://www.jci-bioinfo.cn/pLoc_bal-mEuk/Supp2.pdf.

2.3. Installing Deep-Learning for Three Deeper Levels

In this study, a dense neural network with 3 fully connected layers was used to predict subcellular localization of multi-label eukaryotic proteins, as illustrated in Figure 1. The predicted results were decided by the output of the threshold θ. If the output is greater than 0.5, the outcome was true; otherwise, false. For more information about this, see [11], where the details have been clearly elaborated and hence there is no need to repeat here.

The new predictor developed via the above procedures is called “pLoc_Deep-mEuk”, where “pLoc_Deep” stands for “predict subcellular localization by deep learning”, and “mEuk” for “multi-label eukaryotic proteins”.

Figure 1. An illustration to show a dense neural network with 3 fully connected layers. Adapted from [11] with permission.

3. RESULTS AND DISCUSSION

According to the 5-step rules [58], one of the important procedures in developing a new predictor is how to properly evaluate its anticipated accuracy. To deal with that, two issues need to be considered. 1) What metrics should be used to quantitatively reflect the predictor’s quality? 2) What test method should be applied to score the metrics?

3.1. A Set of Five Metrics for Multi-Label Systems

Different from the metrics used to measure the prediction quality of single-label systems, the metrics for the multi-label systems are much more complicated. To make them more intuitive and easier to understand for most experimental scientists, here we use the following intuitive Chou’s five metrics [239] that have recently been widely used for studying various multi-label systems (see, e.g., [240,241]):

{ Aiming = 1 N q k = 1 N q ( L k L k * L k * ) , [ 0 , 1 ] Coverage = 1 N q k = 1 N q ( L k L k * L k ) , [ 0 , 1 ] Accuracy = 1 N q k = 1 N q ( L k L k * L k L k * ) , [ 0 , 1 ] Absolutetrue = 1 N q k = 1 N q Δ ( L k , L k * ) , [ 0 , 1 ] Absolutefalse = 1 N q k = 1 N q ( L k L k * L k L k * M ) , [ 1 , 0 ] (4)

where N q is the total number of query proteins or tested proteins, M is the total number of different labels for the investigated system (for the current study it is L cell = 22 ), means the operator acting on the set therein to count the number of its elements, means the symbol for the “union” in the set theory, denotes the symbol for the “intersection”, L k denotes the subset that contains all the labels observed by experiments for the k-th tested sample, L k * represents the subset that contains all the labels predicted for the k-th sample, and

Δ ( L k , L k * ) = { 1 , if all the labels in L k * are identical to those in L k 0 , otherwise (5)

In Equation (4), the first four metrics with an upper arrow ↑ are called positive metrics, meaning that the larger the rate is the better the prediction quality will be; the 5th metrics with a down arrow ↓ is called negative metrics, implying just the opposite meaning.

From Equation (4) we can see the following: 1) the “Aiming” defined by the 1st sub-equation is for checking the rate or percentage of the correctly predicted labels over the practically predicted labels; 2) the “Coverage” defined in the 2nd sub-equation is for checking the rate of the correctly predicted labels over the actual labels in the system concerned; 3) the “Accuracy” in the 3rd sub-equation is for checking the average ratio of correctly predicted labels over the total labels including correctly and incorrectly predicted labels as well as those real labels but are missed in the prediction; 4) the “Absolute true” in the 4th sub-equation is for checking the ratio of the perfectly or completely correct prediction events over the total prediction events; 5) the “Absolute false” in the 5th sub-equation is for checking the ratio of the completely wrong prediction over the total prediction events.

3.2. Comparison with the State-of-the-Art Predictor

Listed in Table 1 are the rates achieved by the current pLoc_Deep-mEuk predictor via the cross validations on the same experiment-confirmed dataset as used in [4]. For facilitating comparison, listed there are also the corresponding results obtained by the pLoc_bal-mEuk [4], the existing most powerful predictor for identifying the subcellular localization of eukaryotic proteins with both single and multiple location sites. As shown in Table 1, the newly proposed predictor pLoc_Deep-mEuk is remarkably superior to the existing state-of-the-art predictor pLoc_bal-mEuk in all the five metrics. Particularly, it can be seen from the table that the absolute true rate achieved by the new predictor is over 81%, which is far beyond the reach of any other existing methods. This is because it is extremely difficult to enhance the absolute true rate of a prediction method for a multi-label system as clearly elucidated in [4]. Actually, to avoid embarrassment, many investigators even chose not to mention the metrics of absolute true rate in dealing with multi-label systems (see, e.g., [91,178,184]).

Moreover, to in-depth examine the prediction quality of the new predictor for the proteins in each of the subcellular locations concerned (cf. Table 2), we used a set of four intuitive metrics that were derived in [242] based on the Chou’s symbols introduced for studying protein signal peptides [243] and that have ever since been widely concurred or justified (see, e.g., [242,244]). For the current study, the set of metrics can be formulated as:

{ Sn ( i ) = 1 N + ( i ) N + ( i ) 0 Sn 1 Sp ( i ) = 1 N + ( i ) N ( i ) 0 Sp 1 Acc ( i ) = 1 N + ( i ) + N + ( i ) N + ( i ) + N ( i ) 0 Acc 1 MCC ( i ) = 1 ( N + ( i ) N + ( i ) + N + ( i ) N ( i ) ) ( 1 + N + ( i ) N + ( i ) N + ( i ) ) ( 1 + N + ( i ) N + ( i ) N ( i ) ) 1 MCC 1 ( i = 1 , 2 , , 22 ) (6)

Table 1. Comparison with the state-of-the-art method in predicting eukaryotic protein subcellular localizationa.

aSee Equation (4) for the definition of the metrics. bSee [4], where the reported metrics rates were obtained by the jackknife test on the benchmark dataset of Supporting Information S1 that contains experiment-confirmed proteins only. cThe proposed predictor; to assure that the test was performed on exactly the same experimental data as reported in [4] for pLoc_bal-mEuk.

Table 2. Performanceof pLoc_Deep-mEukfor each of the 22 subcellular locations.

aSee Table 1 and the relevant context for further explanation. bSee Equation (6) for the metrics definition.

where Sn, Sp, Acc, and MCC represent the sensitivity, specificity, accuracy, and Mathew’s correlation coefficient, respectively (Chen et al., 2007), and i denotes the i-th subcellular location (or subset) in the benchmark dataset. N + ( i ) is the total number of the samples investigated in the i-th subset, whereas N + ( i ) is the number of the samples in N + ( i ) that are incorrectly predicted to be of other locations; N ( i ) is the total number of samples in any locations but not the i-th location, whereas N + ( i ) is the number of the samples in N ( i ) that are incorrectly predicted to be of the i-th location.

Listed in Table 2 are the results achieved by pLoc_Deep-mEuk for the eukaryotic proteins in each of 22 subcellular locations. As we can see from the table, nearly all the success rates achieved by the new predictor for the eukaryotic proteins in each of the 22 subcellular locations are within the range of 90%-100%, which is once again far beyond the reach of any of its counterparts.

3.3. Web Server and User Guide

As pointed out in [245], user-friendly and publicly accessible web-servers represent the future direction for developing practically more useful predictors. Actually, user-friendly web-servers as given in a series of recent publications (see, e.g., [219,220,234,246-300]) will significantly enhance the impacts of theoretical work because they can attract the broad experimental scientists [301]. In view of this, the web-server of the current pLoc_Deep-mEuk predictor has also been established. Moreover, to maximize users’ convenience, a step-by-step guide is given below.

Step 1. Click the link at http://www.jci-bioinfo.cn/pLoc_Deep-mEuk/, the top page of the pLoc_Deep- mEukweb-server will appear on your computer screen, as shown in Figure 2. Click on the Read Me button to see a brief introduction about the predictor.

Step 2. Either type or copy/paste the sequences of query eukaryotic proteins into the input box at the center of Figure 2. The input sequence should be in the FASTA format. For the examples of sequences in FASTA format, click the Example button right above the input box.

Step 3. Click on the Submit button to see the predicted result. For instance, if you use the four protein sequences in the Example window as the input, after 10 seconds or so, you will see a new screen (Figure 3) occurring. On its upper part are listed the names of the subcellular locations numbered from (1) to (22) covered by the current predictor. On its lower part are the predicted results: the query protein Q63564 of example-1 corresponds to “1,” meaning it belonging to “Acrosome” only; the query protein P23276 of example-2 corresponds to “2, 8” meaning it belonging to “Cell membrane” and “Cytoskeleton”; the query protein Q9VVV9 of example-3 corresponds to “2, 7, 18”, meaning it belonging to “Cell membrane”, “Cytoplasm”, and “Nucleus”; the query protein Q673G8 of example-4 corresponds to “2, 7, 10, 18”, meaning it belonging to “Cell membrane”, “Cytoplasm”, “Endosome”, and “Nucleus”. All these results are perfectly consistent with experimental observations.

Step 4. As shown on the lower panel of Figure 2, you may also choose the batch prediction by entering your e-mail address and your desired batch input file (in FASTA format of course) via the Browse button. To see the sample of batch input file, click on the button Batch-example. After clicking the button Batch-submit, you will see “Your batch job is under computation; once the results are available, you will be notified by e-mail”.

Figure 2. A semi screenshot for the top page of pLoc_Deep-mEuk.

Figure 3. A semi screenshot for the webpage obtained by following Step 2 of Section 3.3.

Step 5. Click on the Citation button to find the papers that have played the key role in developing the current predictor of pLoc_Deep-mEuk.

Step 6. Click the Supporting Information button to download the Supporting Informations mentioned in this paper.

4. CONCLUSION

It is anticipated that the pLoc_Deep-Euk predictor holds very high potential to become a useful high throughput tool in identifying the subcellular localization of eukaryotic proteins, particularly for finding multi-target drugs that is currently a very hot trend in drug development.

ACKNOWLEDGEMENTS

The authors wish to thank the two anonymous reviewers, whose constructive comments are very helpful for further strengthening the presentation of this paper. This work was supported by the grants from the National Natural Science Foundation of China (No. 31560316, 61261027, 61262038, 61202313 and 31260273), the Province National Natural Science Foundation of JiangXi (No. 20132BAB201053), the Jiangxi Provincial Foreign Scientific and Technological Cooperation Project (No. 20120BDH80023), the Department of Education of JiangXi Province (GJJ160866).

Cite this paper: Shao, Y. and Chou, K.C. (2020) pLoc_Deep-mEuk: Predict Subcellular Localization of Eukaryotic Proteins by Deep Learning. Natural Science, 12, 400-428. doi: 10.4236/ns.2020.126034.
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[38]   Akbar, S., Rahman, A.U., Hayat, M. and Sohail, M. (2020) cACP: Classifying Anticancer Peptides Using Discriminative Intelligent Model via Chou’s 5-Step Rules and General Pseudo Components. Chemometrics and Intelligent Laboratory (CHEMOLAB), 196, Article ID: 103912.
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[39]   Akmal, M.A., Hussain, W., Rasool, N., Khan. Y.D., Khan, S.A. and Chou, K.C. (2020) Using Chou’s 5-Steps Rule to Predict O-Linked Serine Glycosylation Sites by Blending Position Relative Features and Statistical Moment. IEEE/ACM Transactions on Computational Biology and Bioinformatics, in press.
https://doi.org/10.1109/TCBB.2020.2968441

[40]   Charoenkwan, P., Schaduangrat, N., Nantasenamat, C., Piacham, T. and Shoombuatong, W.(2020) iQSP: A Sequence-Based Tool for the Prediction and Analysis of Quorum Sensing Peptides via Chou’s 5-Steps Rule and Informative Physicochemical Properties. International Journal of Molecular Sciences, 21, 75.
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[41]   Charoenkwan, P., Schaduangrat, N., Nantasenamat, C., Piacham, T. and Shoombuatong, W. (2020) iQSP: A Sequence-Based Tool for the Prediction and Analysis of Quorum Sensing Peptides via Chou’s 5-Steps Rule and Informative Physicochemical Properties. International Journal of Molecular Sciences, 21, 75. (Correction on 2020, Vol. 21, No. 7, 2629)
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[42]   Chen, Y. and Fan, X. (2020) Use of Chou’s 5-Steps Rule to Reveal Active Compound and Mechanism of Shuangshen Pingfei San on Idiopathic Pulmonary Fibrosis. Current Molecular Medicine, 20, 220-230.
https://doi.org/10.2174/1566524019666191011160543

[43]   Dobosz, R., Mucko, J. and Gawinecki, R. (2020) Using Chou’s 5-Step Rule to Evaluate the Stability of Tautomers: Susceptibility of 2-[(Phenylimino)-methyl]-cyclohexane-1,3-diones to Tautomerization Based on the Calculated Gibbs Free Energies. Energies, 13, 183.
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[44]   Du, L., Meng, Q., Jiang, H. and Li, Y. (2020) Using Evolutionary Information and Multi-Label Linear Discriminant Analysis to Predict the Subcellular Location of Multi-Site Bacterial Proteins via Chou’s 5-Steps Rule. IEEE Access, 8, 56452-56461.
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[45]   Dutta, A., Dalmia, A., Athul R., Singh, K.K. and Anand, A. (2020) Using the Chou’s 5-Steps Rule to Predict Splice Junctions with Interpretable Bidirectional Long Short-Term Memory Networks. Computer in Biology and Medicine, 116, Article ID: 103558.
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[46]   Ju, Z. and Wang, S.Y. (2020) Prediction of Lysine Formylation Sites Using the Composition of K-Spaced Amino Acid Pairs via Chou’s 5-Steps Rule and General Pseudo Components. Genomics, 112, 859-866.
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[47]   Kabir, M., Ahmad, S., Iqbal, M. and Hayat, M. (2020) INR-2L: A Two-Level Sequence-Based Predictor Developed via Chou’s 5-Steps Rule and General PseAAC for Identifying Nuclear Receptors and Their Families. Genomics, 112, 276-285.
https://doi.org/10.1016/j.ygeno.2019.02.006

[48]   Khan, Y.D., Amin, N., Hussain, W., Rasool, N., Khan, S.A. and Chou, K.C. (2020) iProtease-PseAAC(2L): A Two-Layer Predictor for Identifying Proteases and Their Types Using Chou’s 5-Step-Rule and General PseAAC. Analytical Biochemistry, 588, Article ID: 113477.
https://doi.org/10.1016/j.ab.2019.113477

[49]   Lin, W., Xiao, X., Qiu, W. and Chou, K.C. (2020) Use Chou’s 5-Steps Rule to Predict Remote Homology Proteins by Merging Grey Incidence Analysis and Domain Similarity Analysis. Natural Science, 12, 181-198.
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[50]   Vishnoi, S., Garg, P. and Arora, P. (2020) Physicochemical n-Grams Tool: A Tool for Protein Physicochemical Descriptor Generation via Chou’s 5-Step Rule. Chemical Biology & Drug Design, 95, 79-86.
https://doi.org/10.1111/cbdd.13617

[51]   Vundavilli, H., Datta, A., Sima, C., Hua, J.P. Lopes, R. and Bittner, M.L. (2020) Using Chou’s 5-Steps Rule to Model Feedback in Lung Cancer. IEEE Journal of Biomedical and Health Informatics.
https://doi.org/10.1109/JBHI.2019.2958042

[52]   Yang, L., Lv, Y., Wang, S., Zhang, Q., Pan, Y., Su, D., Lu, Q. and Zuo, Y.C. (2020) Identifying FL11 Subtype by Characterizing Tumor Immune Microenvironment in Prostate Adenocarcinoma via Chou’s 5-Steps Rule. Genomics, 112, 1500-1515.
https://doi.org/10.1016/j.ygeno.2019.08.021

[53]   Xuan, P., Cui, H., Shen, T. Sheng, N. and Zhang, T. (2019) HeteroDualNet: A Dual Convolutional Neural Network With Heterogeneous Layers for Drug-Disease Association Prediction via Chou’s Five-Step Rule. Frontiers in Pharmacology, 10, 1301.
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[54]   Chou, K.C. (2020) Showcase to Illustrate How the Web-Server pLoc_Deep-mPlant Is Working.

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[56]   Behbahani, M., Nosrati, M., Moradi, M. and Mohabatkar, H. (2020) 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, 190, 1035-1048.
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[78]   Esmaeili, M., Mohabatkar, H. and Mohsenzadeh, S. (2010) Using the Concept of Chou’s Pseudo Amino Acid Composition for Risk Type Prediction of Human Papillomaviruses. Journal of Theoretical Biology, 263, 203-209.
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[84]   Guo, J., Rao, N., Liu, G., Yang, Y. and Wang, G. (2011) Predicting Protein Folding Rates Using the Concept of Chou’s Pseudo Amino Acid Composition. Journal of Computational Chemistry, 32, 1612-1617.
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[86]   Lin, J., Wang, Y. and Xu, X. (2011)A Novel Ensemble and Composite Approach for Classifying Proteins Based on Chou’s Pseudo Amino Acid Composition. African Journal of Biotechnology, 10, 16963-16968.

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[88]   Mohammad, B.M., Behjati, M. and Mohabatkar, H. (2011) Prediction of Metalloproteinase Family Based on the Concept of Chou’s Pseudo Amino Acid Composition Using a Machine Learning Approach. Journal of Structural and Functional Genomics, 12, 191-197.
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[91]   Cao, J.Z. Liu, W.Q. and Gu, H. (2012) Predicting Viral Protein Subcellular Localization with Chou’s Pseudo Amino Acid Composition and Imbalance-Weighted Multi-Label K-Nearest Neighbor Algorithm. Protein and Peptide Letters, 19, 1163-1169.
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[94]   Fan, G.L. and Li, Q.Z. (2012) Predict Mycobacterial Proteins Subcellular Locations by Incorporating Pseudo-Average Chemical Shift into the General Form of Chou’s Pseudo Amino Acid Composition. Journal of Theoretical Biology, 304, 88-95.
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[95]   Fan, G.L. and Li, Q.Z. (2012) Predicting Protein Submitochondria Locations by Combining Different Descriptors into the General Form of Chou’s Pseudo Amino Acid Composition. Amino Acids, 43, 545-555.
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[99]   Nanni, L., Lumini, A., Gupta, D. and Garg, A. (2012) Identifying Bacterial Virulent Proteins by Fusing a Set of Classifiers Based on Variants of Chou’s Pseudo Amino Acid Composition and on Evolutionary Information. IEEE/ACM Transaction on Computational Biology and Bioinformatics, 9, 467-475.
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[100]   Niu, X.H., Hu, X.H., Shi, F. and Xia, J.B. (2012) Predicting Protein Solubility by the General Form of Chou’s Pseudo Amino Acid Composition: Approached from Chaos Game Representation and Fractal Dimension. Protein & Peptide Letters, 19, 940-948.
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[101]   Ren, L.Y., Zhang, Y.S. and Gutman, I. (2012) Predicting the Classification of Transcription Factors by Incorporating Their Binding Site Properties into a Novel Mode of Chou’s Pseudo Amino Acid Composition. Protein & Peptide Letters, 19, 1170-1176.
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[102]   Zhao, X.W., Ma, Z.Q. and Yin, M.H. (2012) Predicting Protein-Protein Interactions by Combing Various Sequence-Derived Features into the General form of Chou’s Pseudo Amino Acid Composition. Protein & Peptide Letters, 19, 492-500.
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[103]   Zia-ur-Rehman and Khan, A. (2012) Identifying GPCRs and Their Types with Chou’s Pseudo Amino Acid Composition: An Approach from Multi-Scale Energy Representation and Position Specific Scoring Matrix. Protein & Peptide Letters, 19, 890-903.
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[105]   Fan, G.L. and Li, Q.Z. (2013) Discriminating Bioluminescent Proteins by Incorporating Average Chemical Shift and Evolutionary Information into the General Form of Chou’s Pseudo Amino Acid Composition. Journal of Theoretical Biology, 334, 45-51.
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[110]   Huang, C. and Yuan, J.Q. (2013) Predicting Protein Subchloroplast Locations with Both Single and Multiple Sites via Three Different Modes of Chou’s Pseudo Amino Acid Compositions. Journal of Theoretical Biology, 335, 205-212.
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[118]   Wang, X., Li, G.Z. and Lu, W.C. (2013) Virus-ECC-mPLoc: A Multi-Label Predictor for Predicting the Subcellular Localization of Virus Proteins with Both Single and Multiple Sites Based on a General Form of Chou’s Pseudo Amino Acid Composition. Protein & Peptide Letters, 20, 309-317.
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[207]   Xiao, X., Cheng, X., Su, S., Nao, Q. and Chou, K.C. (2017) pLoc-mGpos: Incorporate Key Gene Ontology Information into General PseAAC for Predicting Subcellular Localization of Gram-Positive Bacterial Proteins. Natural Science, 9, 331-349.
https://doi.org/10.4236/ns.2017.99032

[208]   Qiu, W.R., Zheng, Q.S., Sun, B.Q. and Xiao, X. (2017) Multi-iPPseEvo: A Multi-Label Classifier for Identifying Human Phosphorylated Proteins by Incorporating Evolutionary Information into Chou’s General PseAAC via Grey System Theory. Molecular Informatics, 36, UNSP 1600085.
https://doi.org/10.1002/minf.201600085

[209]   Qiu, W.R., Sun, B.Q., Xiao, X., Xu, D. and Chou, K.C. (2017) iPhos-PseEvo: Identifying Human Phosphorylated Proteins by Incorporating Evolutionary Information into General PseAAC via Grey System Theory. Molecular Informatics, 36, UNSP 1600010.
https://doi.org/10.1002/minf.201600010

[210]   Meher, P.K., Sahu, T.K., Saini, V. and Rao, A.R. (2017) Predicting Antimicrobial Peptides with Improved Accuracy by Incorporating the Compositional, Physico-Chemical and Structural Features into Chou’s General PseAAC. Scientific Reports, 7, Article No. 42362.
https://doi.org/10.1038/srep42362

[211]   Liu, L.M., Xu, Y. and Chou, K.C. (2017) iPGK-PseAAC: Identify Lysine Phosphoglycerylation Sites in Proteins by Incorporating Four Different Tiers of Amino Acid Pairwise Coupling Information into the General PseAAC. Medicinal Chemistry, 13, 552-559.
https://doi.org/10.2174/1573406413666170515120507

[212]   Khan, M., Hayat, M., Khan, S.A. and Iqbal, N. (2017) Unb-DPC: Identify Mycobacterial Membrane Protein Types by Incorporating Un-Biased Dipeptide Composition into Chou’s General PseAAC. Journal of Theoretical Biology, 415, 13-19.
https://doi.org/10.1016/j.jtbi.2016.12.004

[213]   Ju, Z. and He, J.J. (2017) Prediction of Lysine Crotonylation Sites by Incorporating the Composition of k-Spaced Amino Acid Pairs into Chou’s General PseAAC. Journal of Molecular Graphics and Modelling, 77, 200-204.
https://doi.org/10.1016/j.jmgm.2017.08.020

[214]   Cheng, X., Xiao, X. and Chou, K.C. (2017) pLoc-mVirus: Predict Subcellular Localization of Multi-Location Virus Proteins via Incorporating the Optimal GO Information into General PseAAC. Gene, 628, 315-321. (Erratum: ibid., 2018, Vol. 644, 156-156)
https://doi.org/10.1016/j.gene.2017.07.036

[215]   Cheng, X., Xiao, X. and Chou, K.C. (2017) pLoc-mPlant: Predict Subcellular Localization of Multi-Location Plant Proteins via Incorporating the Optimal GO Information into General PseAAC. Molecular BioSystems, 13, 1722-1727.
https://doi.org/10.1039/C7MB00267J

[216]   Tiwari, A.K. (2016) Prediction of G-Protein Coupled Receptors and Their Subfamilies by Incorporating Various Sequence Features into Chou’s General PseAAC. Computer Methods and Programs in Biomedicine, 134, 197- 213.
https://doi.org/10.1016/j.cmpb.2016.07.004

[217]   Qiu, W.R., Sun, B.Q., Xiao, X., Xu, Z.C. and Chou, K.C. (2016) iHyd-PseCp: Identify Hydroxyproline and Hydroxylysine in Proteins by Incorporating Sequence-Coupled Effects into General PseAAC. Oncotarget, 7, 44310- 44321.
https://doi.org/10.18632/oncotarget.10027

[218]   Ju, Z., Cao, J.Z. and Gu, H. (2016) Predicting Lysine Phosphoglycerylation with Fuzzy SVM by Incorporating k-Spaced Amino Acid Pairs into Chou’s General PseAAC. Journal of Theoretical Biology, 397, 145-150.
https://doi.org/10.1016/j.jtbi.2016.02.020

[219]   Jia, J., Zhang, L., Liu, Z., Xiao, X. and Chou, K.C. (2016) pSumo-CD: Predicting Sumoylation Sites in Proteins with Covariance Discriminant Algorithm by Incorporating Sequence-Coupled Effects into General PseAAC. Bioinformatics, 32, 3133-3141.
https://doi.org/10.1093/bioinformatics/btw387

[220]   Jia, J., Liu, Z., Xiao, X., Liu, B. and Chou, K.C. (2016) iCar-PseCp: Identify Carbonylation Sites in Proteins by Monto Carlo Sampling and Incorporating Sequence Coupled Effects into General PseAAC. Oncotarget, 7, 34558-34570.
https://doi.org/10.18632/oncotarget.9148

[221]   Zhang, S.L. (2015) Accurate Prediction of Protein Structural Classes by Incorporating PSSS and PSSM into Chou’s General PseAAC. Chemometrics and Intelligent Laboratory Systems, 142, 28-35.
https://doi.org/10.1016/j.chemolab.2015.01.004

[222]   Zhang, M., Zhao, B. and Liu, X. (2015) Predicting Industrial Polymer Melt Index via Incorporating Chaotic Characters into Chou’s General PseAAC. Chemometrics and Intelligent Laboratory Systems, 146, 232-240.
https://doi.org/10.1016/j.chemolab.2015.05.028

[223]   Sharma, R., Dehzangi, A., Lyons, J., Paliwal, K., Tsunoda, T. and Sharma, A. (2015) Predict Gram-Positive and Gram-Negative Subcellular Localization via Incorporating Evolutionary Information and Physicochemical Features Into Chou’s General PseAAC. IEEE Transactions on NanoBioscience, 14, 915-926.
https://doi.org/10.1109/TNB.2015.2500186

[224]   Ju, Z., Cao, J.Z. and Gu, H. (2015) iLM-2L: A Two-Level Predictor for Identifying Protein Lysine Methylation Sites and Their Methylation Degrees by Incorporating K-Gap Amino Acid Pairs into Chous General PseAAC. Journal of Theoretical Biology, 385, 50-57.
https://doi.org/10.1016/j.jtbi.2015.07.030

[225]   Dehzangi, A., Heffernan, R., Sharma, A., Lyons, J., Paliwal, K. and Sattar, A. (2015) Gram-Positive and Gram- Negative Protein Subcellular Localization by Incorporating Evolutionary-Based Descriptors into Chou’s General PseAAC. Journal of Theoretical Biology, 364, 284-294.
https://doi.org/10.1016/j.jtbi.2014.09.029

[226]   Ahmad, S., Kabir, M. and Hayat, M. (2015) Identification of Heat Shock Protein families and J-Protein Types by Incorporating Dipeptide Composition into Chou’s General PseAAC. Computer Methods and Programs in Biomedicine, 122, 165-174.
https://doi.org/10.1016/j.cmpb.2015.07.005

[227]   Hayat, M. and Iqbal, N. (2014) Discriminating Protein Structure Classes by Incorporating Pseudo Average Chemical Shift to Chou’s General PseAAC and Support Vector Machine. Computer Methods and Programs in Biomedicine, 116, 184-192.
https://doi.org/10.1016/j.cmpb.2014.06.007

[228]   Chou, K.C. (2009) Pseudo Amino Acid Composition and Its Applications in Bioinformatics, Proteomics and System Biology. Current Proteomics, 6, 262-274.
https://doi.org/10.2174/157016409789973707

[229]   Chen, W., Lei, T.Y., Jin, D.C., Lin, H. and Chou, K.C. (2014) PseKNC: A Flexible Web-Server for Generating Pseudo K-Tuple Nucleotide Composition. Analytical Biochemistry, 456, 53-60.
https://doi.org/10.1016/j.ab.2014.04.001

[230]   Chen, W., Lin, H. and Chou, K.C. (2015) Pseudo Nucleotide Composition or PseKNC: An Effective Formulation for Analyzing Genomic Sequences. Molecular BioSystems, 11, 2620-2634.
https://doi.org/10.1039/C5MB00155B

[231]   Chen, W., Feng, P.M., Lin, H. and Chou, K.C. (2014) iSS-PseDNC: Identifying Splicing Sites Using Pseudo Dinucleotide Composition. Biomed Research International, 2014, Article ID: 623149.
https://doi.org/10.1155/2014/623149

[232]   Chen, W., Tang, H., Ye, J., Lin, H. and Chou, K.C. (2016) iRNA-PseU: Identifying RNA Pseudouridine Sites. Molecular Therapy—Nucleic Acids, 5, e332.

[233]   Liu, B., Fang, L., Long, R., Lan, X. and Chou, K.C. (2016) iEnhancer-2L: A Two-Layer Predictor for Identifying Enhancers and Their Strength by Pseudo k-Tuple Nucleotide Composition. Bioinformatics, 32, 362-369.
https://doi.org/10.1093/bioinformatics/btv604

[234]   Liu, B., Long, R. and Chou, K.C. (2016) iDHS-EL: Identifying DNase I Hypersensi-Tivesites by Fusing Three Different Modes of Pseudo Nucleotide Composition into an Ensemble Learning Framework. Bioinformatics, 32, 2411-2418.
https://doi.org/10.1093/bioinformatics/btw186

[235]   Feng, P., Ding, H., Yang, H., Chen, W., Lin, H. and Chou, K.C. (2017) iRNA-PseColl: Identifying the Occurrence Sites of Different RNA Modifications by Incorporating Collective Effects of Nucleotides into PseKNC. Molecular Therapy—Nucleic Acids, 7, 155-163.
https://doi.org/10.1016/j.omtn.2017.03.006

[236]   Liu, B., Wang, S., Long, R. and Chou, K.C. (2017) iRSpot-EL: Identify Recombination Spots with an Ensemble Learning Approach. Bioinformatics, 33, 35-41.
https://doi.org/10.1093/bioinformatics/btw539

[237]   Liu, B., Yang, F. and Chou, K.C. (2017) 2L-piRNA: A Two-Layer Ensemble Classifier for Identifying Piwi-Inter- acting RNAs and Their Function. Molecular Therapy—Nucleic Acids, 7, 267-277.
https://doi.org/10.1016/j.omtn.2017.04.008

[238]   Sabooh, M.F., Iqbal, N., Khan, M., Khan, M. and Maqbool, H.F. (2018) Identifying 5-Methylcytosine Sites in RNA Sequence Using Composite Encoding Feature into Chou’s PseKNC. Journal of Theoretical Biology, 452, 1-9.
https://doi.org/10.1016/j.jtbi.2018.04.037

[239]   Chou, K.C. (2013) Some Remarks on Predicting Multi-Label Attributes in Molecular Biosystems. Molecular Biosystems, 9, 1092-1100.
https://doi.org/10.1039/c3mb25555g

[240]   Cheng, X., Zhao, S.G., Xiao, X. and Chou, K.C. (2017) iATC-mISF: A Multi-Label Classifier for Predicting the Classes of Anatomical Therapeutic Chemicals. Bioinformatics, 33, 341-346. (Corrigendum, ibid., 2017, Vol. 33, 2610)
https://doi.org/10.1093/bioinformatics/btx387

[241]   Cheng, X., Zhao, S.G., Xiao, X. and Chou, K.C. (2017) iATC-mHyb: A Hybrid Multi-Label Classifier for Predicting the Classification of Anatomical Therapeutic Chemicals. Oncotarget, 8, 58494-58503.
https://doi.org/10.18632/oncotarget.17028

[242]   Chen, W., Feng, P.M., Lin, H. and Chou, K.C. (2013) iRSpot-PseDNC: Identify Recombination Spots with Pseudo Dinucleotide Composition. Nucleic Acids Research, 41, e68.
https://doi.org/10.1093/nar/gks1450

[243]   Chou, K.C. (2001) Using Subsite Coupling to Predict Signal Peptides. Protein Engineering, Design and Selection, 14, 75-79.
https://doi.org/10.1093/protein/14.2.75

[244]   Jia, J., Liu, Z., Xiao, X., Liu, B. and Chou, K.C. (2016) Identification of Protein-Protein Binding Sites by Incorporating the Physicochemical Properties and Stationary Wavelet Transforms into Pseudo Amino Acid Composition (iPPBS-PseAAC). Journal of Biomolecular Structure and Dynamics, 34, 1946-1961.
https://doi.org/10.1080/07391102.2015.1095116

[245]   Chou, K.C. and Shen, H.B. (2009) Recent Advances in Developing Web-Servers for Predicting Protein Attributes. Natural Science, 1, 63-92.
https://doi.org/10.4236/ns.2009.12011

[246]   Song, J., Li, F., Leier, A., Marquez-Lago, T.T., Akutsu, T., Haffari, G., Chou, K.C., Webb, G.I. and Pike, R.N. (2018) PROSPERous: High-Throughput Prediction of Substrate Cleavage Sites for 90 Proteases with Improved Accuracy. Bioinformatics, 34, 684-687.
https://doi.org/10.1093/bioinformatics/btx670

[247]   Yang, H., Qiu, W.R., Liu, G., Guo, F.B., Chen, W., Chou, K.C. and Lin, H. (2018) iRSpot-Pse6NC: Identifying Recombination Spots in Saccharomyces cerevisiae by Incorporating Hexamer Composition into General PseKNC. International Journal of Biological Sciences, 14, 883-891.
https://doi.org/10.7150/ijbs.24616

[248]   Xu, Y., Li, C. and Chou, K.C. (2017) iPreny-PseAAC: Identify C-Terminal Cysteine Prenylation Sites in Proteins by Incorporating Two Tiers of Sequence Couplings into PseAAC. Medicinal Chemistry, 13, 544-551.
https://doi.org/10.2174/1573406413666170419150052

[249]   Xu ,Y. and Chou, K.C. (2016) Recent Progress in Predicting Posttranslational Modification Sites in Proteins. Current Topics in Medicinal Chemistry, 16, 591-603.
https://doi.org/10.2174/1568026615666150819110421

[250]   Xu, Y., Wen, X., Wen, L.S., Wu, L.Y., Deng, N.Y. and Chou, K.C. (2014) iNitro-Tyr: Prediction of Nitrotyrosine Sites in Proteins with General Pseudo Amino Acid Composition. PLoS ONE, 9, e105018.
https://doi.org/10.1371/journal.pone.0105018

[251]   Xu, Y., Wen, X., Shao, X.J., Deng, N.Y. and Chou, K.C. (2014) iHyd-PseAAC: Predicting Hydroxyproline and Hydroxylysine in Proteins by Incorporating Dipeptide Position-Specific Propensity into Pseudo Amino Acid Composition. International Journal of Molecular Sciences, 15, 7594-7610.
https://doi.org/10.3390/ijms15057594

[252]   Xu, Y., Shao, X.J., Wu, L.Y., Deng, N.Y. and Chou, K.C. (2013) iSNO-AAPair: Incorporating Amino Acid Pairwise Coupling into PseAAC for Predicting Cysteine S-Nitrosylation Sites in Proteins. PeerJ, 1, e171.
https://doi.org/10.7717/peerj.171

[253]   Xu, Y., Ding, J., Wu, L.Y. and Chou, K.C. (2013) iSNO-PseAAC: Predict Cysteine S-Nitrosylation Sites in Proteins by Incorporating Position Specific Amino Acid Propensity into Pseudo Amino Acid Composition. PLoS ONE, 8, e55844.
https://doi.org/10.1371/journal.pone.0055844

[254]   Xu, Y., Ding, J., Huang, Q. and Deng, N.Y. (2013) Prediction of Protein Methylation Sites Using Conditional Random Field. Protein & Peptide Letters, 20, 71-77.
https://doi.org/10.2174/092986613804096865

[255]   Jia, J., Liu, Z., Xiao, X. and Chou, K.C. (2015) iPPI-Esml: An Ensemble Classifier for Identifying the Interactions of Proteins by Incorporating Their Physicochemical Properties and Wavelet Transforms into PseAAC. Journal of Theoretical Biology, 377, 47-56.
https://doi.org/10.1016/j.jtbi.2015.04.011

[256]   Jia, J., Liu, Z., Xiao, X., Liu, B. and Chou, K.C. (2016) iSuc-PseOpt: Identifying Lysine Succinylation Sites in Proteins by Incorporating Sequence-Coupling Effects into Pseudo Components and Optimizing Imbalanced Training Dataset. Analytical Biochemistry, 497, 48-56.
https://doi.org/10.1016/j.ab.2015.12.009

[257]   Jia, J., Liu, Z., Xiao, X., Liu, B. and Chou, K.C. (2016) pSuc-Lys: Predict Lysine Succinylation Sites in Proteins with PseAAC and Ensemble Random Forest Approach. Journal of Theoretical Biology, 394, 223-230.
https://doi.org/10.1016/j.jtbi.2016.01.020

[258]   Jia, J., Liu, Z., Xiao, X., Liu, B. and Chou, K.C. (2016) iPPBS-Opt: A Sequence-Based Ensemble Classifier for Identifying Protein-Protein Binding Sites by Optimizing Imbalanced Training Datasets. Molecules, 21, 95.
https://doi.org/10.3390/molecules21010095

[259]   Jia, J., Li, X., Qiu, W., Xiao, X. and Chou, K.C. (2019) iPPI-PseAAC(CGR): Identify Protein-Protein Interactions by Incorporating Chaos Game Representation into PseAAC. Journal of Theoretical Biology, 460, 195-203.
https://doi.org/10.1016/j.jtbi.2018.10.021

[260]   Liu, B., Xu, J., Lan, X., Xu, R., Zhou, J., Wang, X. and Chou, K.C. (2014) iDNA-Prot|dis: Identifying DNA- Binding Proteins by Incorporating Amino Acid Distance-Pairs and Reduced Alphabet Profile into the General Pseudo Amino Acid Composition. PLoS ONE, 9, e106691.
https://doi.org/10.1371/journal.pone.0106691

[261]   Liu, B., Zhang, D., Xu, R., Xu, J., Wang, X., Chen, Q., Dong, Q. and Chou, K.C. (2014) Combining Evolutionary Information Extracted from Frequency Profiles with Sequence-Based Kernels for Protein Remote Homology Detection. Bioinformatics, 30, 472-479.
https://doi.org/10.1093/bioinformatics/btt709

[262]   Liu, B., Fang, L., Liu, F., Wang, X., Chen, J. and Chou, K.C. (2015) Identification of Real microRNA Precursors with a Pseudo Structure Status Composition Approach. PLoS ONE, 10, e0121501.
https://doi.org/10.1371/journal.pone.0121501

[263]   Liu, B., Fang, L., Wang, S., Wang, X., Li, H. and Chou, K.C. (2015) Identification of microRNA Precursor with the Degenerate K-Tuple or Kmer Strategy. Journal of Theoretical Biology, 385, 153-159.
https://doi.org/10.1016/j.jtbi.2015.08.025

[264]   Liu, B., Liu, F., Fang, L., Wang, X. and Chou, K.C. (2015) repDNA: A Python Package to Generate Various Modes of Feature Vectors for DNA Sequences by Incorporating User-Defined Physicochemical Properties and Sequence-Order Effects. Bioinformatics, 31, 1307-1309.
https://doi.org/10.1093/bioinformatics/btu820

[265]   Liu, B., Liu, F., Wang, X., Chen, J., Fang, L. and Chou, K.C. (2015) Pse-in-One: A Web Server for Generating Various Modes of Pseudo Components of DNA, RNA, and Protein Sequences. Nucleic Acids Research, 43, W65- W71.
https://doi.org/10.1093/nar/gkv458

[266]   Liu, B., Fang, L., Liu, F., Wang, X. and Chou, K.C. (2016) iMiRNA-PseDPC: microRNA Precursor Identification with a Pseudo Distance-Pair Composition Approach. Journal of Biomolecular Structure and Dynamics, 34, 223- 235.
https://doi.org/10.1080/07391102.2015.1014422

[267]   Liu, B., Liu, F., Fang, L., Wang, X. and Chou, K.C. (2016) repRNA: A Web Server for Generating Various Feature Vectors of RNA Sequences. Molecular Genetics and Genomics, 291, 473-481.
https://doi.org/10.1007/s00438-015-1078-7

[268]   Liu, B., Wu, H. and Chou, K.C. (2017) Pse-in-One 2.0: An Improved Package of Web Servers for Generating Various Modes of Pseudo Components of DNA, RNA, and Protein Sequences. Natural Science, 9, 67-91.
https://doi.org/10.4236/ns.2017.94007

[269]   Liu, B., Wu, H., Zhang, D., Wang, X. and Chou, K.C. (2017) Pse-Analysis: A Python Package for DNA/RNA and Protein/Peptide Sequence Analysis Based on Pseudo Components and Kernel Methods. Oncotarget, 8, 13338- 13343.
https://doi.org/10.18632/oncotarget.14524

[270]   Liu, B. (2019) BioSeq-Analysis: A Platform for DNA, RNA, and Protein Sequence Analysis Based on Machine Learning Approaches. Briefings in Bioinformatics, 20, 1280-1294.
https://doi.org/10.1093/bib/bbx165

[271]   Liu, B., Li, K., Huang, D.S. and Chou, K.C. (2018) iEnhancer-EL: Identifying Enhancers and Their Strength with Ensemble Learning Approach. Bioinformatics, 34, 3835-3842.
https://doi.org/10.1093/bioinformatics/bty458

[272]   Liu, B., Weng, F., Huang, D.S. and Chou, K.C. (2018) iRO-3wPseKNC: Identify DNA Replication Origins by Three-Window-Based PseKNC. Bioinformatics, 34, 3086-3093.
https://doi.org/10.1093/bioinformatics/bty312

[273]   Liu, B., Yang, F., Huang, D.S. and Chou, K.C. (2018) iPromoter-2L: A Two-Layer Predictor for Identifying Promoters and Their Types by Multi-Window-Based PseKNC. Bioinformatics, 34, 33-40.
https://doi.org/10.1093/bioinformatics/btx579

[274]   Liu, B., Gao, X. and Zhang, H. (2019) BioSeq-Analysis2.0: An Updated Platform for Analyzing DNA, RNA and Protein Sequences at Sequence Level and Residue Level Based on Machine Learning Approaches. Nucleic Acids Research, 47, e127.
https://doi.org/10.1093/nar/gkz740

[275]   Chen, W., Lin, H., Feng, P.M., Ding, C., Zuo, Y.C. and Chou, K.C. (2012) iNuc-PhysChem: A Sequence-Based Predictor for Identifying Nucleosomes via Physicochemical Properties. PLoS ONE, 7, e47843.
https://doi.org/10.1371/journal.pone.0047843

[276]   Chen, W., Feng, P.M., Deng, E.Z., Lin, H. and Chou, K.C. (2014) iTIS-PseTNC: A Sequence-Based Predictor for Identifying Translation Initiation Site in Human Genes Using Pseudo Trinucleotide Composition. Analytical Biochemistry, 462, 76-83.
https://doi.org/10.1016/j.ab.2014.06.022

[277]   Chen, W., Zhang, X., Brooker, J., Lin, H., Zhang, L. and Chou, K.C. (2015) PseKNC-General: A Cross-Platform Package for Generating Various Modes of Pseudo Nucleotide Compositions. Bioinformatics, 31, 119-120.
https://doi.org/10.1093/bioinformatics/btu602

[278]   Chen, W., Ding, H., Feng, P., Lin, H. and Chou, K.C. (2016) iACP: A Sequence-Based Tool for Identifying Anticancer Peptides. Oncotarget, 7, 16895-16909.
https://doi.org/10.18632/oncotarget.7815

[279]   Chen, W., Feng, P., Ding, H., Lin, H. and Chou, K.C. (2016) Using Deformation Energy to Analyze Nucleosome Positioning in Genomes. Genomics, 107, 69-75.
https://doi.org/10.1016/j.ygeno.2015.12.005

[280]   Chen, W., Feng, P., Yang, H., Ding, H., Lin, H. and Chou, K.C. (2017) iRNA-AI: Identifying the Adenosine to Inosine Editing Sites in RNA Sequences. Oncotarget, 8, 4208-4217.
https://doi.org/10.18632/oncotarget.13758

[281]   Lin, H. and Li, Q.Z. (2007) Predicting Conotoxin Superfamily and Family by Using Pseudo Amino Acid Composition and Modified Mahalanobis Discriminant. Biochemical and Biophysical Research Communications, 354, 548-551.
https://doi.org/10.1016/j.bbrc.2007.01.011

[282]   Lin, H. and Li, Q.Z. (2007) Using Pseudo Amino Acid Composition to Predict Protein Structural Class: Approached by Incorporating 400 Dipeptide Components. Journal of Computational Chemistry, 28, 1463-1466.
https://doi.org/10.1002/jcc.20554

[283]   Lin, H. and Ding, H. (2011) Predicting Ion Channels and Their Types by the Dipeptide Mode of Pseudo Amino Acid Composition. Journal of Theoretical Biology, 269, 64-69.
https://doi.org/10.1016/j.jtbi.2010.10.019

[284]   Lin, H., Chen, W. and Ding, H. (2013) AcalPred: A Sequence-Based Tool for Discriminating between Acidic and Alkaline Enzymes. PLoS ONE, 8, e75726.
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[285]   Lin, H., Ding, C., Yuan, L.F., Chen, W., Ding, H., Li, Z.Q., Guo, F.B., Hung, J. and Rao, N.N. (2013) Predicting Subchloroplast Locations of Proteins Based on the General Form of Chou’s Pseudo Amino Acid Composition: Approached from Optimal Tripeptide Composition. International Journal of Biomathematics, 6, Article No. 1350003.
https://doi.org/10.1142/S1793524513500034

[286]   Lin, H., Deng, E.Z., Ding, H., Chen, W. and Chou, K.C. (2014) iPro54-PseKNC: A Sequence-Based Predictor for Identifying Sigma-54 Promoters in Prokaryote with Pseudo k-Tuple Nucleotide Composition. Nucleic Acids Research, 42, 12961-12972.
https://doi.org/10.1093/nar/gku1019

[287]   Shao, Y.T. and Chou, K.C. (2020) pLoc_Deep-mAnimal: A Novel Deep CNN-BLSTM Network to Predict Subcellular Localization of Animal Proteins. Natural Science, 12, 281-291.
https://doi.org/10.4236/ns.2020.125024

[288]   Chou, K.C. (2020) Showcase to Illustrate How the Web-Server pLoc_Deep-mAnimal Is Working. American Journal of Virology & Disease, 2, 1-2.

[289]   Shao, Y.T., Liu, X.X., Lu, Z. and Chou, K.C. (2020) pLoc_Deep-mPlant: Predict Subcellular Localization of Plant Proteins by Deep Learning. Natural Science, 12, 237-247.
https://doi.org/10.4236/ns.2020.125021

[290]   Chou, K.C. (2020) Showcase to Illustrate How the Web-Server pLoc_Deep-mPlant Is Working. Growth Journal, 1, 1-2.

[291]   Lu, Z. and Chou, K.C. (2020) iATC_Deep-mISF: A Multi-Label Classifier for Predicting the Classes of Anatomical Therapeutic Chemicals by Deep Learning. Advances in Bioscience and Biotechnology, 11, 153-159.
https://doi.org/10.4236/abb.2020.115012

[292]   Chou, K.C. (2020) Showcase to Illustrate How the Web-Server iATC_Deep-mISF Is Working. Global Journal of Science Frontier Research: G Bio-Tech & Genetics, 20, 1-3.

[293]   Liu, X.X. and Chou, K.C. (2020) pLoc_Deep-mGneg: Predict Subcellular Localization of Gram Negative Bacterial Proteins by Deep Learning. Advances in Bioscience and Biotechnology, 11, 141-152.
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[295]   Shao, Y.T., Cheng, X. and Chou, K.C. (2020) pLoc_Deep-mVirus: A CNN Model for Predicting Subcellular Localization of Virus Proteins by Deep Learning. Molecular Therapy—Nucleic Acids (MTNA), in press.

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[297]   Lu, Z. and Chou, K.C. (2020) Showcase to Illustrate How the Web-Server pLoc_Deep-mGpos Is Working. Journal of Biomedical Science and Engineering, 13, 55-65.
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[298]   Chou, K.C. (2020) Showcase to Illustrate How the Webserver pLoc_Deep-mGpos Is Working. Open Acc J Bio Sci, 2, 345-346.

[299]   Chou, K.C. (2020) How the Artificial Intelligence Tool iSuc-PseOpt Is Working for Predicting Lysine Succinylation Sites in Proteins. Biomedical Research and Clinical Reviews, 1, 1-2.

[300]   Chou, K.C., Cheng, X. and Xiao, X. (2019) pLoc_bal-mHum: Predict Subcellular Localization of Human Proteins by PseAAC and Quasi-Balancing Training Dataset. Genomics, 111, 1274-1282.
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[301]   Chou, K.C. (2017) An Unprecedented Revolution in Medicinal Chemistry Driven by the Progress of Biological Science. Current Topics in Medicinal Chemistry, 17, 2337-2358.
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