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 NS  Vol.12 No.7 , July 2020
pLoc_Deep-mHum: Predict Subcellular Localization of Human Proteins by Deep Learning
Abstract: Recently, the life of human beings around the entire world has been endangering by the spreading of pneumonia-causing virus, such as Coronavirus, COVID-19, and H1N1. To develop effective drugs against Coronavirus, knowledge of protein subcellular localization is indispensable. In 2019, a predictor called “pLoc_bal-mHum” was developed for identifying the subcellular localization of human 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-mHum was still not trained by a “deep learning”, a very powerful technique developed recently. The present study was devoted to incorporate the “deep-learning” technique and develop a new predictor called “pLoc_Deep-mHum”. 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-mHum/, which will become a very useful tool for fighting pandemic coronavirus and save the mankind of this planet.

1. INTRODUCTION

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.

The development of predicting human subcellular localization can be briefly summarized as follows. The early human protein predictor HSLPred [4] can only predict four subcellular localizations. In 2006, Chou et al. developed Hum-PLoc [5], which can cover up to 12 subcellular localizations, but Hum-PLoc can only predict a single position. In 2011, by extracting the GO (Gene Ontology) information of the proteins [6], the same predictor can be used to deal with multiple locations proteins, achieving 76% accuracy. It is through these kinds of procedures and follow-up procedures, that the capacity in dealing with multi-site systems and raising the accuracy is further improved. Particularly for the predictor called pLoc_bal-mHum [6].

Although the pLoc_bal-mHum predictor [7] has the aforementioned merits, it has not been trained at a deeper level yet [8-11].

The present study was initiated in an attempt to address this problem. As done in pLoc_bal-mHum [12] as well as many other recent publications in developing new prediction methods (see, e.g., [9-11,13-50]), the guidelines of the 5-step rule [51] 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-mHum [12].

2. MATERIALS AND METHODS

2.1. Benchmark Dataset

The benchmark dataset used in this study is exactly the same as that in pLoc_bal-mHum [12]. It can be formulated as

S = S 1 S 2 S u S 13 S 14 (1)

where S 1 only contains the protein samples from the “Centrosome” location (cf. Table 1), S 2 only contains those from the “Cytoplasm” location, and so forth; denotes the symbol for “union” in the set theory [52]. The detailed sequences of these proteins and their accession numbers (or ID codes) are given in Supporting Information S1 that is also available at http://www.jci-bioinfo.cn/pLoc_bal-mHum/Supp1.pdf where none of proteins included has ≥25% sequence identity to any other in the same subset.

2.2. Proteins Sample Formulation

Now let us consider the 2nd step of the 5-step rule [51]; 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

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

aSee Eq. 4 for the definition of the metrics. bSee [12], 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 [12] for pLoc_bal-mHum.

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 [53] or PseAAC [54]. Ever since then, the concept of “Pseudo Amino Acid Composition” or “PseAAC” 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., [11,55-207]).

Because it has been widely and increasingly used, recently three powerful open access soft-wares, called “PseAAC-Builder” [87], “propy” [144], and “PseAAC-General” [114], were established: the former two are for generating various modes of special PseAAC [208]; while the 3rd one for those of general PseAAC [51], 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 [209] that have proved very successful as well [210-221]. According to the concept of general PseAAC [51], any protein sequence can be formulated as a PseAAC vector given by

P = [ Ψ 1 Ψ 2 Ψ u Ψ Ω ] T

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 [12]. Thus, by following exactly the same procedures as described in the Section 2.2 of [12], each of the protein samples in the benchmark dataset can be uniquely defined as a 14-D numerical vector as given in columns 16-29 of Supporting Information S2, which can also be directly downloaded at http://www.jci-bioinfo.cn/pLoc_bal-mHum/Supp2.pdf.

2.3. Architecture for the Novel CNN-BiLSTM Network

In this study, a novel deep CNN-BiLSTM neural network (Figure 1) model included one convolution layer and one BiLSTM block is proposed to predict subcellular localization of multi-label human proteins. The strong point of this model is that it allows extracting the maximum amount of information from human protein features using CNN convolution layers. This output becomes the BiLSTM input, which allows keeping the dependency relationship between the features in both directions.

CNN is known for its ability to extract as many features as possible from the data. In our model, 14 human protein features are used as convolutional neural network input. Then, fourteen filters of sizes 2 with a stride of 1 are applied. This gives a 14 * 14 feature map. After each filter, a layer of MaxPooling1D is applied to update and reduce the size of the data and this gives a 7 * 14 feature map.

Then, the 7 * 14 feature map is concatenated to build the BiLSTM input, which applies a BiLSTM layer to filter the information, using its three gates. The output of this step is the input of the fully connected layer which consists of 3 hidden layer. It links each piece of input information with a piece of output information. Finally, we apply the sigmoid function as an activation function in order to transform the vector into probability to define the class of each output. The label of human protein was decided by the Probability value θ. If the output is greater than 0.5, the outcome was true; otherwise, false.

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

Figure 1. An illustration to show a CNN and LSTM neural network models to predict the subcellular localization of human proteins.

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

3. RESULTS AND DISCUSSION

According to the 5-step rules [51], 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 [222]. For the latter, two kinds of metrics are needed: one is the “global metrics” and the other is the “local metrics”.

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

Listed in Table 1 are the rates achieved by the current pLoc_Deep-mHum predictor via the cross validations on the same experiment-confirmed dataset as used in [22] according to the scores of “global metrics”. For facilitating comparison, listed there are also the corresponding results obtained by the pLoc_bal-mHum [12], the existing most powerful predictor for identifying the subcellular localization of human proteins with both single and multiple location sites. As shown in Table 1, the newly proposed predictor pLoc_Deep-mHum is remarkably superior to the existing state-of-the-art predictor pLoc_bal-mHum 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 [12]. 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., [85,141,147]).

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 “local metrics” to score its accuracy,

Table 2. Performance of pLoc_Deep-mHum for each of the 14 subcellular locations.

aSee Table 1 and the relevant context for further explanation. bSee Eq.6 for the metrics definition.

which were derived in [223] based on the Chou’s symbols introduced for studying protein signal peptides [224] and that have ever since been widely concurred or justified (see, e.g., [209-217,223,225-274]).

Listed in Table 2 are the rates achieved by pLoc_Deep-mHum for the human 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 human 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.

Meanwhile, as a byproduct, the present paper has also stimulated some very interesting or challenging papers (see, e.g., [275-280]).

3.3. Comparison with Several Classic Machine Learning Methods

In this paper, the CNN-BiLSTM neural network model is constructed through the Python language and Keras, and the model is trained through binary_crossentropy and Adam optimizers (lr = 0.001, beta_1 = 0.9, beta_2 = 0.999, epsilon = 10−8).

In order to improve the performance of the CNN-BiLSTM neural network model, based on the above parameter settings, the hyperparameters of the constructed CNN-BiLSTM model are adjusted. Through experimental analysis, we find that four parameters have a greater impact on the performance of the deep learning model: the height of the CNN filter, the number of filters in the convolutional layer, the output dimension of BiLSTM, and Dropout rate. Finally, the four optimal parameters are set to (2, 14, 14, 0.1).

We compared the performance of the pLoc_Deep-mHum model with seven state-of-the-art methods in Table 3, including Rake1D [281], LabelPowerset [282], ML-SVM [283], MLARAM{Benites, 2015 #8224}, RandomForestClassifier [284], MLKNN [285], BRkNNaClassifier{Spyromitros, 2008 #8227}. It

Table 3. Comparison with several classic machine learning methods in predicting human protein subcellular localizationa.

can be found that the performance of the pLoc_Deep-mHum model is significantly better than its cohorts in the multi-label metrics scores.

As pointed out in [286], 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., Song, 2018 #3338; Wang, 2018 #3353}) will significantly enhance the impacts of theoretical work because they can attract the broad experimental scientists [287]. In view of this, the web-server of the current pLoc_Deep-mHum predictor has also been established at http://www.jci-bioinfo.cn/pLoc_Deep-mHum/, by which the majority of experimental scientists can easily get the data they need.

4. CONCLUSION

It is anticipated that the pLoc_Deep-Hum predictor holds very high potential to become a useful high throughput tool in identifying the subcellular localization of human proteins, particularly for finding multi-target drugs that is currently a very hot trend in drug development. Particularly, it will become a very useful tool to find novel drugs against the current pandemic of Coronavirus endangering mankind’s life.

ACKNOWLEDGMENTS

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. , Liu, X. , Lu, Z. and Chou, K. (2020) pLoc_Deep-mHum: Predict Subcellular Localization of Human Proteins by Deep Learning. Natural Science, 12, 526-551. doi: 10.4236/ns.2020.127042.
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[219]   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

[220]   Zhang, L. and Kong, L. (2018) iRSpot-ADPM: Identify Recombination Spots by Incorporating the Associated Dinucleotide Product Model into Chou’s Pseudo Components. Journal of Theoretical Biology, 441, 1-8.
https://doi.org/10.1016/j.jtbi.2017.12.025

[221]   Zhang, S., Yang, K., Lei, Y. and Song, K. (2018) iRSpot-DTS: Predict Recombination Spots by Incorporating the Dinucleotide-Based Spare-Cross Covariance Information into Chou’s Pseudo Components. Genomics, 11, 457-464.

[222]   Chou, K.C. (2019) Two Kinds of Metrics for Computational Biology. Genomics.
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[223]   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.
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[224]   Chou, K.C. (2001) Using Subsite Coupling to Predict Signal Peptides. Protein Engineering, 14, 75-79.
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[225]   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 (JBSD), 34, 1946-1961.
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[226]   Xiao, X., Wang, P., Lin, W.Z., Jia, J.H. and Chou, K.C. (2013) iAMP-2L: A Two-Level Multi-Label Classifier for Identifying Antimicrobial Peptides and Their Functional Types. Analytical Biochemistry, 436, 168-177.
https://doi.org/10.1016/j.ab.2013.01.019

[227]   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

[228]   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.
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[229]   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

[230]   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

[231]   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, E95.
https://doi.org/10.3390/molecules21010095

[232]   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

[233]   Liu, Z., Xiao, X., Yu, D.J., Jia, J., Qiu, W.R. and Chou, K.C. (2016) pRNAm-PC: Predicting N-Methyladenosine Sites in RNA Sequences via Physical-Chemical Properties. Analytical Biochemistry, 497, 60-67.
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[234]   Xiao, X., Ye, H.X., Liu, Z., Jia, J.H. and Chou, K.C. (2016) iROS-gPseKNC: Predicting Replication Origin Sites in DNA by Incorporating Dinucleotide Position-Specific Propensity into General Pseudo Nucleotide Composition. Oncotarget, 7, 34180-34189.
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[235]   Qiu, W.R., Sun, B.Q., Xiao, X., Xu, Z.C., Jia, J.H. and Chou, K.C. (2018) iKcr-PseEns: Identify Lysine Crotonylation Sites in Histone Proteins with Pseudo Components and Ensemble Classifier. Genomics, 110, 239-246.
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[236]   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.
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[237]   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.
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[238]   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.
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[239]   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.
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[240]   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.
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[241]   Xu, Y. and Chou, K.C. (2016) Recent Progress in Predicting Posttranslational Modification Sites in Proteins. Current Topics in Medicinal Chemistry, 16, 591-603.
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[242]   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. Journal of Medicinal Chemistry, 13, 552-559.
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[243]   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. Journal of Medicinal Chemistry, 13, 544-551.
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[244]   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.
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[245]   Chen, J., Long, R., Wang, X.L., Liu, B. and Chou, K.C. (2016) dRHP-PseRA: Detecting Remote Homology Proteins Using Profile-Based Pseudo Protein Sequence and Rank Aggregation. Scientific Reports, 6, Article No. 32333.
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[246]   Xu, R., Zhou, J., Liu, B., He, Y.A., Zou, Q., Wang, X. and Chou, K.C. (2015) Identification of DNA-Binding Proteins by Incorporating Evolutionary Information into Pseudo Amino Acid Composition via the Top-N-Gram Approach. Journal of Biomolecular Structure & Dynamics (JBSD), 33, 1720-1730.
https://doi.org/10.1080/07391102.2014.968624

[247]   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.
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[248]   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.
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[249]   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.
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[250]   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

[251]   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.
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[252]   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.
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[253]   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

[254]   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

[255]   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

[256]   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

[257]   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.
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[258]   Chen, W., Feng, P., Ding, H., Lin, H. and Chou, K.C. (2015) Benchmark Data for Identifying N6-Methyladenosine Sites in the Saccharomyces cerevisiae Genome. Data in Brief, 5, 376-378.
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[259]   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.
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[260]   Feng, P., Yang, H., Ding, H., Lin, H., Chen, W. and Chou, K.C. (2019) iDNA6mA-PseKNC: Identifying DNA N(6)-methyladenosine Sites by Incorporating Nucleotide Physicochemical Properties into PseKNC. Genomics, 111, 96-102.
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[261]   Feng, P.M., Chen, W., Lin, H. and Chou, K.C. (2013) iHSP-PseRAAAC: Identifying the Heat Shock Protein Families Using Pseudo Reduced Amino Acid Alphabet Composition. Analytical Biochemistry, 442, 118-125.
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[262]   Su, Z.D., Huang, Y., Zhang, Z.Y., Zhao, Y.W., Wang, D., Chen, W., Chou, K.C. and Lin, H. (2018) iLoc-lncRNA: Predict the Subcellular Location of lncRNAs by Incorporating Octamer Composition into General PseKNC. Bioinformatics, 34, 4196-4204.

[263]   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

[264]   Guo, S.H., Deng, E.Z., Xu, L.Q., Ding, H., Lin, H., Chen, W. and Chou, K.C. (2014) iNuc-PseKNC: A Sequence-Based Predictor for Predicting Nucleosome Positioning in Genomes with Pseudo k-tuple Nucleotide Composition. Bioinformatics, 30, 1522-1529.
https://doi.org/10.1093/bioinformatics/btu083

[265]   Zhang, C.J., Tang, H., Li, W.C., Lin, H., Chen, W. and Chou, K.C. (2016) iOri-Human: Identify Human Origin of Replication by Incorporating Dinucleotide Physicochemical Properties into Pseudo Nucleotide Composition. Oncotarget, 7, 69783-69793.
https://doi.org/10.18632/oncotarget.11975

[266]   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

[267]   Chen, W., Feng, P., Yang, H., Ding, H., Lin, H. and Chou, K.C. (2018) iRNA-3typeA: Identifying 3-Types of Modification at RNA’s Adenosine Sites. Molecular Therapy: Nucleic Acid, 11, 468-474.
https://doi.org/10.1016/j.omtn.2018.03.012

[268]   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

[269]   Chen, W., Feng, P., Ding, H., Lin, H. and Chou, K.C. (2015) iRNA-Methyl: Identifying N6-methyladenosine Sites Using Pseudo Nucleotide Composition. Analytical Biochemistry, 490, 26-33.
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[270]   Chen, W., Ding, H., Zhou, X., Lin, H. and Chou, K.C. (2018) iRNA(m6A)-PseDNC: Identifying N6-methyladenosine Sites Using Pseudo Dinucleotide Composition. Analytical Biochemistry, 561-562, 59-65.
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[271]   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

[272]   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.
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[273]   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.
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[274]   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.
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[275]   Chou, K.C. (2020) The Development of Gordon Life Science Institute: Its Driving Force and Accomplishments. Natural Science, 12, 202-217.
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[276]   Chou, K.C. (2020) The Most Important Ethical Concerns in Science. Natural Science, 12, 35-36.
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[277]   Chou, K.C. (2020) Other Mountain Stones Can Attack Jade: The 5-Steps Rule. Natural Science, 12, 59-64.
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[278]   Chou, K.C. (2020) The Problem of Elsevier Series Journals Online Submission by Using Artificial Intelligence. Natural Science, 12, 37-38.
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[279]   Chou, K.C. (2020) Proposing 5-Steps Rule Is a Notable Milestone for Studying Molecular Biology. Natural Science, 12, 74-79.
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[280]   Chou, K.C. (2020) Using Similarity Software to Evaluate Scientific Paper Quality Is a Big Mistake. Natural Science, 12, 42-58.
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[287]   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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