Use Chou’s 5-Steps Rule to Predict Remote Homology Proteins by Merging Grey Incidence Analysis and Domain Similarity Analysis

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https://www.scirp.org/journal/ns Natural Science, 2020, Vol. 12, (No. 3), pp: 181-198
https://doi.org/10.4236/ns.2020.123016 181 Natural Science
Use Chou’s 5-Steps Rule to Predict Remote Homology
Proteins by Merging Grey Incidence Analysis
and Domain Similarity Analysis
Weizhong Lin1, Xuan Xiao1,2, Wangren Qiu1, Kuo-Chen Chou2
1Computer Department, Jing-De-Zhen Ceramic Institute, Jing-De-Zhen, China; 2Gordon Life Science Institute, Boston,
MA 02478, USA
Correspondence to: Xuan Xiao,
Keywords: Remote Homology Proteins, Grey Model, Domain Similarity, Chou’s 5-Steps Rules
Received: February 29, 2020 Accepted: March 22, 2020 Published: March 25, 2020
Copyright © 2020 by author(s) and Scientific Research Publishing Inc.
This work is licensed under the Creative Commons Attribution International License (CC BY 4.0).
http://creativecommons.org/licenses/by/4.0/
ABSTRACT
Detecting remote homology proteins is a challenging problem for both basic research and
drug development. Although there are a couple of methods to deal with this problem, the
benchmark datasets based on which the existing methods were trained and tested contain
many high homologous samples as reflected by the fact that the cutoff threshold was set at
95%. In this study, we reconstructed the benchmark dataset by setting the threshold at 40%,
meaning none of the proteins included in the benchmark dataset has more than 40% pairwise
sequence identity with any other in the same subset. Using the new benchmark dataset, we
proposed a new predictor called “dRHP-GreyFun” based on the grey modeling and functional
domain approach. Rigorous cross-validations have indicated that the new predictor is supe-
rior to its counterparts in both enhancing success rates and reducing computational cost. The
predictor can be downloaded from https://github.com/jcilwz/dRHP-GreyFun.
1. INTRODUCTION
Detecting remote homology relationship among proteins plays one of the fundamental and central
roles in computational proteomics. It is particularly useful for drug development [1, 2]. With the ava-
lanche of protein sequences generated in the post-genomic age, it is highly desired to timely detect the re-
mote homology proteins. Although X-ray crystallography is a powerful tool in determining protein 3D
structures, it is time-consuming and expensive. Particularly, not all proteins can be successfully crystal-
lized, particularly for membrane proteins. 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. Although NMR is indeed a very powerful tool in determining the 3D structures of membrane
proteins (see, e.g., [3-7]), it is also time-consuming and costly. To acquire the structural information in a
Open Access

https://doi.org/10.4236/ns.2020.123016 182 Natural Science
timely manner, a series of 3D protein structures have been developed by means of structural bioinformat-
ics tools (see, e.g., [8-20]). 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 infor-
mation, such as PTM (posttranslational modification) sites in proteins [21, 22], protein-drug interaction
in cellular networking [23], DNA-methylation sites [24], recombination spots [25], and sigma-54 promo-
ters [26], have been deducted by various sequential bioinformatics tools such as PseAAC approach [27]
and PseKNC approach [28]. Actually, the rapid development in sequential bioinformatics and structural
bioinformatics have driven the medicinal chemistry undergoing an unprecedented revolution [29], in
which the computational biology has played increasingly important roles in stimulating the development
of finding novel drugs. In view of this, the computational methods were also utilized in this study for de-
tecting remote homology.
To acquire the structural information in a timely manner, one has to resort to various structural bio-
informatics tools based on the sequence similarity principle (see, e.g., [30]). Unfortunately, such principle
cannot cover the cases of remote homology proteins. In view of this, considerable efforts [31-35] have been
made to detect remote homology proteins.
Although these methods each had their own merits and did play a stimulating role in this area, fur-
ther work is needed. Firstly, the benchmark datasets used in their studies had high similarity. For instance,
the benchmark dataset in [33, 34] contains 7329 proteins from 1070 different super families, with pairwise
sequence identity cutoff set at 95%. In other words, it would allow those proteins with higher than 80%
similarity in the benchmark dataset. Secondly, the ranking algorithm used in those studies would spend a
lot of time to train or learn the model. For example, if the training dataset had  proteins, the Lambda-
MART would need to deal with
N
2 proteins pair samples.
As demonstrated by a series of recent publications [23, 25, 26, 36-71], to develop a really useful pre-
dictor for a biological system, one needs to follow Chou’s 5-step 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 pre-
dicted; 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 us-
er-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 oper-
ation, 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 on how to deal with these five steps one by one.
2. MATERIALS AND METHOD
2.1. Benchmark Dataset
According to Chou’s 5-step rules [72], the first prerequisite in establishing a new predictor is to con-
struct or select an effective benchmark dataset.
In this study, the benchmark dataset was taken from Liu
et al
. [33]. It contains 7329 proteins from
1070 different super families and 1824 families derived from SCOP database. To reduce the redundancy
and homology bias, the program CD-HIT [73] was adopted to remove those proteins that had ≥40% pair-
wise sequence identity to any other in the same subset. Meanwhile, removed were also those families that
only had one protein sequence. Finally, we obtained 3128 proteins from 540 super-families and 777 fami-
lies.
2.2. Sample Formulation
Most biological systems have two remarkable features: one is of evolution and the other is of com-
plexity. All biological species have developed beginning from a very limited number of ancestral species. It

https://doi.org/10.4236/ns.2020.123016 183 Natural Science
is true for protein sequence as well [30]. Their evolution involves changes of single residues, insertions and
deletions of several residues, gene doubling, and gene fusion [9, 74]. With these changes accumulated for a
long period of time, many similarities between initial and resultant amino acid sequences are gradually
eliminated, but the corresponding proteins may still share many common attributes, such as having basi-
cally the same biological function, subcellular location and similar binding site. To take into account the
evolution information, many investigators used the PSSM (Position-Specific Scoring Matrix) approach
[75], as done in a series of previous publications (see, e.g., [76-81]). On the other hand, biological systems
are extremely complicated with a lot of uncertainties. According to the grey system theory [82], if the in-
formation of an investigated system is fully known, it is called a ‘‘white system;’’ if completely unknown, a
‘‘black system;’’ if partially known, a ‘‘grey system.’’ Actually, most biological systems belong to the grey
systems, and hence it is particularly effective to treat them with the grey model approach [83-86].
2.2.1. Grey Incidence Analysis of Proteins Formulated by Grey-PSSM
Given a protein with
L
amino acid residues, it is usually expressed by
123 iL
RRR R R
=P
(1)
where
( )
1, 2, ,
i
Ri L=
is the
i
-th residue in the protein. Because all the existing machine-learning algo-
rithms (such as “Optimization” algorithm [87], “Covariance Discriminant” or “CD” algorithm [88, 89],
“Nearest Neighbor” or “NN” algorithm [90], and “Support Vector Machine” or “SVM” algorithm [90])
can only handle vectors as elaborated in a comprehensive review [29]. However, a vector defined in a dis-
crete model may completely lose all the sequence-pattern information. To avoid completely losing the se-
quence-pattern information for proteins, the pseudo amino acid composition [27] or PseAAC [91] was
proposed. Ever since then, it has been widely used in nearly all the areas of computational proteomics (see,
e.g., [92-95] as well as a long list of references cited in [96]). Because it has been widely and increasingly
used, four powerful open access soft-wares, called “PseAAC” [97], “PseAAC-Builder” [98], “propy” [99],
and “PseAAC-General” [100], were established: the former three are for generating various modes of
Chou’s special PseAAC [101]; while the 4th one for those of Chou’s general PseAAC [72], 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 [72]), “Gene Ontology” mode (see Eqs.11-12 of [72]), and
“Sequential Evolution” or “PSSM” mode (see Eqs.13-14 of [72]). Encouraged by the successes of using
PseAAC to deal with protein/peptide sequences, the concept of PseKNC (Pseudo K-tuple Nucleotide
Composition) [28] was developed for generating various feature vectors for DNA/RNA sequences [102,
103] that have proved very useful as well. Particularly, recently a very powerful web-server called
“Pse-in-One” [104] and its updated version “Pse-in-One2.0” [105] 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.
According to the general PseAAC [72], the protein of Equation (1) can be formulated as
[ ]
T
12 uΩ
=ΨΨ Ψ ΨP
(2)
where T is the transposing operator, the subscript Ω is an integer, and its value and the components
( )
Ψ1, 2,
u
u=
will depend on how to extract the desired features and properties from the protein se-
quence.
In this study, the model, Grey-PSSM proposed by Lin
et al
. [85, 86] is adopted. It has extracted the
sequential evolution information by the Position Specific Scoring Matrix (PSSM). After the Grey-PSSM
treatment, we have finally got a 60-D PseKNC vector for Equation (2);
i.e.
, its subscript parameter Ω =
60and each of the 60 components therein has been uniquely defined below. Suppose the set of protein
samples is
123 iN
PPP R R=
(3)

https://doi.org/10.4236/ns.2020.123016 184 Natural Science
where
( )
1
i
P iN≤≤
is the
i
-th protein. According to Eqs.6-11 in Lin
et al
. [106], the distance
( )
,
ij
PPΓ
is defined as the grey incidence degree between
i
P
and
j
P
. The larger the value of
( )
,
ij
PPΓ
, the more
similar between
i
P
and
j
P
will be.
2.2.2. Domain Similarity Analysis
In addition to the PseAAC [27, 91] approach, the functional domain [107-112] can also be used to
characterize protein sample,
i
P∈
, according to the following steps.
Step 1. Searching UniProt release 2018_08 Swiss-Prot FASTA format flatfile by HMMER [113-115]
for the homology set of protein
i
P
, we have obtained
homo
i

. If the outcome has more than 10 protein se-
quences, only the top 10-ranking ones are used.
Step 2. For the protein in
homo
i

,
()
homo
1 10
i
ki
Pk
∈ ≤≤

, annotate its functional domains by running
hmmscan program against Pfam-A database (Pfam release 32.0). The Pfam-A contains 17,929 functional
domains and 688 clans, as defined by
{ }
{ }
1 2 3 17929
1 2 3 688
,, ,
,, ,
,
,
fff f
ccc c
=

=





(4)
where
()
1 17929
i
fi
≤≤
denote the
i
-th functional domain in

, and
( )
1 688
i
ci≤≤
the
i
-th clan in

.
Some functional domains may have the same clan. For example, the domains of “PF15884” and “PF17050”
have the same clan “CL0683”. Thus, the functional domain set of protein
i
k
P
, the
k-
th homology protein
i
P
, is denoted as a set
{}
|
i
k ik
D ff
= ∈
(5)
meaning that all functional domains of
i
k
P
contains the set
i
k
D
.
Step 3. The protein
i
P
can be expressed by the following domains set
10
1
ii
k
k
DD
=
=
(6)
where

denotes union in the set theory.
As we can see from Equations ((5), (6)) the distance (Dis) between
i
P
and
j
P
is within the range
( )
0 Dis , 1
ij
PP≤≤
.
2.3. Operation Engine or Algorithm
In this study, the Grey Relational Analysis [82, 116] and the Domain Similarity Index was utilized to
rank the relationship of proteins. Given a query protein, the system will search the benchmark dataset for
it and return the top-ranking similar proteins. The predictor thus formed is called “dRHP-GreyFun”. Illu-
strated in Figure 1 is a flowchart to show how the proposed predictor is working. In this paper, w(1) and
w(2) are equal to 0.5.
3. RESULTS AND DISCUSSION
Among the independent dataset test, sub-sampling (e.g., 5 or 10-fold cross-validation) test, and jack-
knife test, which are often used for examining the accuracy of a statistical prediction method [117], the
jackknife test was deemed the least arbitrary that can always yield a unique result for a given benchmark
dataset [118, 119], as clearly elucidated in a comprehensive review paper [72] and demonstrated by
Eqs.28-32 therein. Therefore, the jackknife test has been increasingly recognized and widely adopted by
investigators to test the power of various prediction methods (see, e.g., [120-123]). However, to reduce the
computational time, we adopted the 5-fold and 10-fold cross-validation in this study as done by many in-
vestigators with SVM as the prediction engine. This is also because the LambdaMART ranking algorithm
used in preview studies [33, 34] would consume a lot of training time and computer memory. As a com-
promise, the 5-fold cross-validation test was adopted there. But, now we employed the operation engine

https://doi.org/10.4236/ns.2020.123016 185 Natural Science
Figure 1. A flowchart to show how the proposed predictor “dRHP-GreyFun” is working by following the
guidelines of Chou’s 5-steps rule.
Table 1. A comparison of the jackknife test results for protein remote homology detection on the bench-
mark dataset.
Methods ROC1 ROC50
PSI-BLAST 0.7113 0.7647
GRA (Grey-PSSM) 0.8937 0.7149
Jaccard Index 0.8196 0.8070
Domain Similarity Index (DSI) 0.9053 0.8454
GRA and Jaccard Index 0.9301 0.8533
dRHP-GreyFun 0.9620 0.8861
based on the grey modeling and functional domains to detect the remote homology proteins, significantly
reducing the computing time and memory. Therefore, it would be feasible to use the most rigorous jack-
knife test to examine the prediction quality. The outcomes thus obtained are given in Table 1, where we
can see that dRHP-GreyFun achieved the best performance in both the score of ROC1 and the score of
ROC50.
4. CONCLUSIONS
Protein remote homology detection is vitally important for studying protein structures and functions.
It is anticipated that the proposed method may become a useful high throughput tool for both basic re-
search and drug design.

https://doi.org/10.4236/ns.2020.123016 186 Natural Science
As pointed out in [124] and demonstrated in a series of recent publications (see, e.g., [40, 125-144]),
user-friendly and publicly accessible web-servers represent the future direction for developing practically
more useful prediction methods and computational tools. Actually, many practically useful web-servers
have significantly increased the impacts of bioinformatics on medical science [29], driving medicinal che-
mistry into an unprecedented revolution [96]. Accordingly, we have also provided a web-server for the
prediction method presented in this paper, by which users can easily get their desired results without the
need to go through the complicated math equation involved. Also, all the programs can be downloaded
from https://github.com/jcilwz/dRHP-GreyFun.
It is illuminating that using graphic approaches to study biological and medical systems can provide
an intuitive vision and useful insights for helping analyze complicated relations therein, as indicated by
many previous studies on a series of important biological topics, (see, e.g., [145-158]), particularly what
happened is for the topics of enzyme kinetics, protein folding rates [153, 159-161], and low-frequency in-
ternal motion [162, 163].
For the remarkable and awesome roles of the “5-steps rule” in driving proteome, genome analyses
and drug development, see a series of recent papers [139, 164-188], where the rule and its wide applica-
tions have been very impressively presented from various aspects or at different angles.
ACKNOWLEDGEMENTS
This work was support by the grants from the National Natural Science Foundation of China
(No.61462047, 31560316, 31760315). Natural Science Foundation of Jiangxi Province, China (No.
20171ACB20023), the Department of Education of JiangXi Province (GJJ160866), The funders had no role
in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
CONFLICTS OF INTEREST
The authors declare no conflicts of interest regarding the publication of this paper.
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33. Liu, B., Chen, J. and Wang, X. (2015) Protein Remote Homology Detection by Combining Chou’s Distance-Pair
Pseudo Amino Acid Composition and Principal Component Analysis.
Molecular Genetics and Genomics
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34. Chen, J., Long, R., Wang, X.L., Liu, B. and Chou, K.C. (2016) dRHP-PseRA: Detecting Remote Homology Pro-
teins Using Profile-Based Pseudo Protein Sequence and Rank Aggregation.
Scientific Reports
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35. Chen, J., Guo, M., Wang, X. and Liu, B. (2018) A Comprehensive Review and Comparison of Different Compu-
tational Methods for Protein Remote Homology Detection.
Brief Bioinform
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36. 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.
https://doi.org/10.1016/j.ab.2013.05.024
37. 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
38. Ding, H., Deng, E.Z., Yuan, L.F., Liu, L., Lin, H., Chen, W. and Chou, K.C. (2014) iCTX-Type: A Se-
quence-Based Predictor for Identifying the Types of Conotoxins in Targeting Ion Channels.
BioMed Research
International
(
BMRI
), 2014, Article ID: 286419. https://doi.org/10.1155/2014/286419
39. 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
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Inosine Editing Sites in RNA Sequences.
Oncotarget
, 8, 4208-4217. https://doi.org/10.18632/oncotarget.13758
41. 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.
https://doi.org/10.1016/j.ab.2018.09.002
42. 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
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, 11, 468-474.
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43. Butt, A.H. and Khan, Y.D. (2018) Prediction of S-Sulfenylation Sites Using Statistical Moments Based Features
via Chou’s 5-Step Rule.
International Journal of Peptide Research and Therapeutics
(
IJPRT
).
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44. Awais, M., Hussain, W., Khan, Y.D., Rasool, N., Khan, S.A. and Chou, K.C. (2019) iPhosH-PseAAC: Identify
Phosphohistidine Sites in Proteins by Blending Statistical Moments and Position Relative Features According to
the Chou’s 5-Step Rule and General Pseudo Amino Acid Composition.
IEEE
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ACM Transactions on Computa-
tional Biology and Bioinformatics
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45. Barukab, O., Khan, Y.D., Khan, S.A. and Chou, K.C. (2019) iSulfoTyr-PseAAC: Identify Tyrosine Sulfation Sites
by Incorporating Statistical Moments via Chou’s 5-Steps Rule and Pseudo Components.
Current Genomics
, 20,
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46. Butt, A.H. and Khan, Y.D. (2019) Prediction of S-Sulfenylation Sites Using Statistical Moments Based Features
via Chou’s 5-Step Rule.
International Journal of Peptide Research and Therapeutics
(
IJPRT
).
https://doi.org/10.1007/s10989-019-09931-2
47. Chen, Y. and Fan, X. (2019) Use Chou’s 5-Steps Rule to Reveal Active Compound and Mechanism of Shua-
ngsheng Pingfei San on Idiopathic Pulmonary Fibrosis.
Current Molecular Medicine
, 20, 220-230.
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48. Du, X., Diao, Y., Liu, H. and Li, S. (2019) MsDBP: Exploring DNA-Binding Proteins by Integrating Multi-Scale
Sequence Information via Chou’s 5-Steps Rule.
Journal of Proteome Research
, 18, 3119-3132.
https://doi.org/10.1021/acs.jproteome.9b00226
49. Dutta, A., Dalmia, A., Singh, K.K. and Anand, A. (2019) Using the Chou’s 5-Steps Rule to Predict Splice Junc-
tions with Interpretable Bidirectional Long Short-Term Memory Networks.
Computers in Biology and Medi-
cine
, 116, Article ID: 103558. https://doi.org/10.1016/j.compbiomed.2019.103558
50. Ehsan, A., Mahmood, M.K., Khan, Y.D., Barukab, O.M., Khan, S.A. and Chou, K.C. (2019) iHyd-PseAAC
(EPSV): Identify Hydroxylation Sites in Proteins by Extracting Enhanced Position and Sequence Variant Fea-
ture via Chou’s 5-Step Rule and General Pseudo Amino Acid Composition.
Current Genomics
, 20, 124-133.
https://doi.org/10.2174/1389202920666190325162307
51. Hussain, W., Khan, S.D., Rasool, N., Khan, S.A. and Chou, K.C. (2019) SPalmitoylC-PseAAC: A Se-
quence-Based Model Developed via Chou’s 5-Steps Rule and General PseAAC for Identifying S-Palmitoylation
Sites in Proteins.
Analytical Biochemistry
, 568, 14-23. https://doi.org/10.1016/j.ab.2018.12.019
52. Hussain, W., Khan, Y.D., Rasool, N., Khan, S.A. and Chou, K.C. (2019) SPrenylC-PseAAC: A Sequence-Based
Model Developed via Chou’s 5-Steps Rule and General PseAAC for Identifying S-Prenylation Sites in Proteins.
Journal of Theoretical Biology
, 468, 1-11. https://doi.org/10.1016/j.jtbi.2019.02.007
53. 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
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54. Kabir, M., Ahmad, S., Iqbal, M. and Hayat, M. (2020) iNR-2L: A Two-Level Sequence-Based Predictor Devel-
oped via Chou’s 5-Steps Rule and General PseAAC for Identifying Nuclear Receptors and Their Families.
Ge-
nomics
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55. Khan, Z.U., Ali, F., Khan, I.A., Hussain, Y. and Pi, D. (2019) iRSpot-SPI: Deep Learning-Based Recombination
Spots Prediction by Incorporating Secondary Sequence Information Coupled with Physio-Chemical Properties
via Chou’s 5-Step Rule and Pseudo Components.
Chemometrics and Intelligent Laboratory Systems
(
CHEMOLAB
), 189, 169-180. https://doi.org/10.1016/j.chemolab.2019.05.003
56. Lan, J., Liu, J., Liao, C., Merkler, D.J., Han, Q. and Li, J. (2019) A Study for Therapeutic Treatment against Par-
kinson’s Disease via Chou’s 5-Steps Rule.
Current Topics in Medicinal Chemistry
, 19, 2318-2333.
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57. Le, N.Q.K. (2019) iN6-Methylat (5-Step): Identifying DNA N(6)-Methyladenine Sites in Rice Genome Using
Continuous Bag of Nucleobases via Chou’s 5-Step Rule.
Molecular Genetics and Genomics
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58. Le, N.Q.K., Yapp, E.K.Y., Ho, Q.T., Nagasundaram, N., Ou, Y.Y. and Yeh, H.Y. (2019) iEnhancer-5Step: Identi-
fying Enhancers Using Hidden Information of DNA Sequences via Chou’s 5-Step Rule and Word Embedding.
Analytical Biochemistry
, 571, 53-61. https://doi.org/10.1016/j.ab.2019.02.017
59. Le, N.Q.K., Yapp, E.K.Y., Ou, Y.Y. and Yeh, H.Y. (2019) iMotor-CNN: Identifying Molecular Functions of Cy-
toskeleton Motor Proteins Using 2D Convolutional Neural Network via Chou’s 5-Step Rule.
Analytical Bioche-
mistry
, 575, 17-26. https://doi.org/10.1016/j.ab.2019.03.017
60. Liang, R., Xie, J., Zhang, C., Zhang, M., Huang, H., Huo, H., Cao, X. and Niu, B. (2019) Identifying Cancer Tar-
gets Based on Machine Learning Methods via Chou’s 5-Steps Rule and General Pseudo Components.
Current
Topics in Medical Chemistry
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61. Liang, Y. and Zhang, S. (2019) Identifying DNase I Hypersensitive Sites Using Multi-Features Fusion and
F-Score Features Selection via Chou’s 5-Steps Rule.
Biophysical Chemistry
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62. Liu, Z., Dong, W., Jiang, W. and He, Z. (2019) csDMA: An Improved Bioinformatics Tool for Identifying DNA
6 ma Modifications via Chou’s 5-Step Rule.
Scientific Reports
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63. Malebary, S.J., Rehman, M.S.U. and Khan, Y.D. (2019) iCrotoK-PseAAC: Identify Lysine Crotonylation Sites by
Blending Position Relative Statistical Features According to the Chou’s 5-Step Rule.
PLoS ONE
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64. Nazari, I., Tahir, M., Tayari, H. and Chong, K.T. (2019) iN6-Methyl (5-Step): Identifying RNA N6-Methyladenosine
Sites Using Deep Learning Mode via Chou’s 5-Step Rules and Chou’s General PseKNC.
Chemometrics and In-
telligent Laboratory Systems
(
CHEMOLAB
), 193, Article ID: 103811.
https://doi.org/10.1016/j.chemolab.2019.103811
65. Ning, Q., Ma, Z. and Zhao, X. (2019) dForml(KNN)-PseAAC: Detecting Formylation Sites from Protein Se-
quences Using K-Nearest Neighbor Algorithm via Chou’s 5-Step Rule and Pseudo Components.
Journal of
Theoretical Biology
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66. Tahir, M., Tayara, H. and Chong, K.T. (2019) iDNA6mA (5-Step Rule): Identification of DNA N6-Methyladenine
Sites in the Rice Genome by Intelligent Computational Model via Chou’s 5-Step Rule.
CHEMOLAB
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96-101. https://doi.org/10.1016/j.chemolab.2019.04.007
67. Vishnoi, S., Garg, P. and Arora, P. (2020) Physicochemical n-Grams Tool: A Tool for Protein Physicochemical
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68. Wiktorowicz, A., Wit, A., Dziewierz, A., Rzeszutko, L., Dudek, D. and Kleczynski, P. (2019) Calcium Pattern
Assessment in Patients with Severe Aortic Stenosis via the Chou’s 5-Steps Rule.
Current Pharmaceutical Design
,
25, 3769-3775. https://doi.org/10.2174/1381612825666190930101258
69. Yang, L., Lv, Y., Wang, S., Zhang, Q., Pan, Y., Su, D., Lu, Q. and Zuo, Y. (2019) Identifying FL11 Subtype by
Characterizing Tumor Immune Microenvironment in Prostate Adenocarcinoma via Chou’s 5-Steps Rule.
Ge-
nomics
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70. Vundavilli, H., Datta, A., Sima, C., Hua, J., Lopes, R. and Bittner, M. (2020) Using Chou’s 5-Steps Rule to Model
Feedback in Lung Cancer.
IEEE Journal of Biomedical and Health Informatics
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71. 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
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Anniversary Year Review, 5-Steps Rule).
Journal of Theoretical Biology
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ing Biological Sequences.
Bioinformatics
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74. Chou, K.C. (1995) The Convergence-Divergence Duality in Lectin Domains of the Selectin Family and Its Im-
plications.
FEBS Letters
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75. Schaffer, A.A., Aravind, L., Madden, T.L., Shavirin, S., Spouge, J.L., Wolf, Y.I., Koonin, E.V. and Altschul, S.F.
(2001) Improving the Accuracy of PSI-BLAST Protein Database Searches with Composition-Based Statistics and
Other Refinements.
Nucleic Acids Research
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76. Chou, K.C. and Shen, H.B. (2007) MemType-2L: A Web Server for Predicting Membrane Proteins and Their
Types by Incorporating Evolution Information through Pse-PSSM.
Biochemical and Biophysical Research
Communications
(
BBRC
), 360, 339-345. https://doi.org/10.1016/j.bbrc.2007.06.027
77. Shen, H.B. and Chou, K.C. (2007) EzyPred: A Top-Down Approach for Predicting Enzyme Functional Classes
and Subclasses.
Biochemical and Biophysical Research Communications
(
BBRC
), 364, 53-59.
https://doi.org/10.1016/j.bbrc.2007.09.098
78. Shen, H.B. and Chou, K.C. (2009) QuatIdent: A Web Server for Identifying Protein Quaternary Structural
Attribute by Fusing Functional Domain and Sequential Evolution Information.
Journal of Proteome Research
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79. Chou, K.C. and Shen, H.B. (2010) A New Method for Predicting the Subcellular Localization of Eukaryotic Pro-
teins with Both Single and Multiple Sites: Euk-mPLoc 2.0.
PLoS ONE
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80. Wu, Z.C., Xiao, X. and Chou, K.C. (2011) iLoc-Plant: A Multi-Label Classifier for Predicting the Subcellular
Localization of Plant Proteins with Both Single and Multiple Sites.
Molecular BioSystems
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https://doi.org/10.1039/c1mb05232b
81. Chou, K.C., Wu, Z.C. and Xiao, X. (2012) iLoc-Hum: Using Accumulation-Label Scale to Predict Subcellular
Locations of Human Proteins with Both Single and Multiple Sites.
Molecular BioSystems
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82. Deng, J.L. (1989) Introduction to Grey System Theory.
The Journal of Grey System
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G-Protein-Coupled Receptor Functional Classes.
Journal of Computational Chemistry
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https://doi.org/10.1002/jcc.21163
84. Lin, W.Z., Fang, J.A., Xiao, X. and Chou, K.C. (2011) iDNA-Prot: Identification of DNA Binding Proteins Using
Random Forest with Grey Model.
PLoS ONE
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85. Lin, W.Z., Fang, J.A., Xiao, X. and Chou, K.C. (2012) Predicting Secretory Proteins of Malaria Parasite by In-
corporating Sequence Evolution Information into Pseudo Amino Acid Composition via Grey System Model.
PLoS ONE
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86. Lin, W.Z., Fang, J.A., Xiao, X. and Chou, K.C. (2013) iLoc-Animal: A Multi-Label Learning Classifier for Pre-
dicting Subcellular Localization of animal Proteins.
Molecular BioSystems
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https://doi.org/10.1039/c3mb25466f
87. Zhang, C.T. and Chou, K.C. (1992) An Optimization Approach to Predicting Protein Structural Class from
Amino Acid Composition.
Protein Science
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88. Chou, K.C. and Elrod, D.W. (2002) Bioinformatical Analysis of G-Protein-Coupled Receptors.
Journal of
Proteome Research
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89. Chou, K.C. and Cai, Y.D. (2003) Prediction and Classification of Protein Subcellular Location: Sequence-Order
Effect and Pseudo Amino Acid Composition.
Journal of Cellular Biochemistry
, 90, 1250-1260.
https://doi.org/10.1002/jcb.10719
90. Hu, L., Huang, T., Shi, X., Lu, W.C., Cai, Y.D. and Chou, K.C. (2011) Predicting Functions of Proteins in Mouse
Based on Weighted Protein-Protein Interaction Network and Protein Hybrid Properties.
PLoS ONE
, 6, e14556.
https://doi.org/10.1371/journal.pone.0014556
91. Chou, K.C. (2005) Using Amphiphilic Pseudo Amino Acid Composition to Predict Enzyme Subfamily Classes.
Bioinformatics
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92. Kabir, M. and Hayat, M. (2016) iRSpot-GAEnsC: Identifying Recombination Spots via Ensemble Classifier and
Extending the Concept of Chou’s PseAAC to Formulate DNA Samples.
Molecular Genetics and Genomics
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93. Meher, P.K., Sahu, T.K., Saini, V. and Rao, A.R. (2017) Predicting Antimicrobial Peptides with Improved Accu-
racy by Incorporating the Compositional, Physico-Chemical and Structural Features into Chou’s General
PseAAC.
Scientific Reports
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94. Ju, Z. and He, J.J. (2017) Prediction of Lysine Propionylation Sites Using Biased SVM and Incorporating Four
Different Sequence Features into Chou’s PseAAC.
Journal of Molecular Graphics and Modelling
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95. Yu, B., Li, S., Qiu, W.Y., Chen, C., Chen, R.X., Wang, L., Wang, M.H. and Zhang, Y. (2017) Accurate Prediction
of Subcellular Location of Apoptosis Proteins Combining Chou’s PseAAC and PsePSSM Based on Wavelet De-
noising.
Oncotarget
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96. Chou, K.C. (2017) An Unprecedented Revolution in Medicinal Chemistry Driven by the Progress of Biological
Science.
Current Topics in Medicinal Chemistry
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97. Shen, H.B. and Chou, K.C. (2008) PseAAC: A Flexible Web-Server for Generating Various Kinds of Protein
Pseudo Amino Acid Composition.
Analytical Biochemistry
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98. Du, P., Wang, X., Xu, C. and Gao, Y. (2012) PseAAC-Builder: A Cross-Platform Stand-Alone Program for Ge-
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Analytical Biochemistry
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99. Cao, D.S., Xu, Q.S. and Liang, Y.Z. (2013) Propy: A Tool to Generate Various Modes of Chou’s PseAAC.
Bioin-
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100. Du, P., Gu, S. and Jiao, Y. (2014) PseAAC-General: Fast Building Various Modes of General Form of Chou’s
Pseudo Amino Acid Composition for Large-Scale Protein Datasets.
International Journal of Molecular Sciences
,
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101. Chou, K.C. (2009) Pseudo Amino Acid Composition and Its Applications in Bioinformatics, Proteomics and
System Biology.
Current Proteomics
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102. Chen, W., Lin, H. and Chou, K.C. (2015) Pseudo Nucleotide Composition or PseKNC: An Effective Formula-
tion for Analyzing Genomic Sequences.
Molecular BioSystems
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https://doi.org/10.1039/C5MB00155B
103. 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
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104. 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
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105. 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
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106. Lin, W.Z., Xiao, X. and Chou, K.C. (2009) GPCR-GIA: A Web-Server for Identifying G-Protein Coupled Re-
ceptors and Their Families with Grey Incidence Analysis.
Protein Engineering
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107. Chou, K.C., Liu, W., Maggiora, G.M. and Zhang, C.T. (1998) Prediction and Classification of Domain Structural
Classes.
Proteins
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Protein Engineering
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109. Chou, K.C. and Cai, Y.D. (2002) Using Functional Domain Composition and Support Vector Machines for Pre-
diction of Protein Subcellular Location.
The Journal of Biological Chemistry
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110. Chou, K.C. and Cai, Y.D. (2004) Predicting Protein Structural Class by Functional Domain Composition.
Bio-
chemical and Biophysical Research Communications
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111. Chou, K.C. and Cai, Y.D. (2004) Predicting Subcellular Localization of Proteins by Hybridizing Functional Do-
main Composition and Pseudo Amino Acid Composition.
Journal of Cellular Biochemistry
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https://doi.org/10.1002/jcb.10790
112. Cai, Y.D. and Chou, K.C. (2005) Using Functional Domain Composition to Predict Enzyme Family Classes.
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Nucleic Acids Research
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116. Liu, S.F., Fang, Z.G. and Lin, Y. (2006) A New Definition for the Degree of Grey Incidence.
Scientific Inquiry
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111-124.
117. Chou, K.C. and Zhang, C.T. (1995) Review: Prediction of Protein Structural Classes.
Critical Reviews in Bio-
chemistry and Molecular Biology
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118. Chou, K.C. and Shen, H.B. (2008) Cell-PLoc: A Package of Web Servers for Predicting Subcellular Localization
of Proteins in Various Organisms.
Nature Protocols
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119. Chou, K.C. and Shen, H.B. (2010) Cell-PLoc 2.0: An Improved Package of Web-Servers for Predicting Subcellu-
lar Localization of Proteins in Various Organisms.
Natural Science
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120. Mohabatkar, H. (2010) Prediction of Cyclin Proteins Using Chou’s Pseudo Amino Acid Composition.
Protein &
Peptide Letters
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121. Sahu, S.S. and Panda, G. (2010) A Novel Feature Representation Method Based on Chou’s Pseudo Amino Acid
Composition for Protein Structural Class Prediction.
Computational Biology and Chemistry
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122. 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.
Pro-
tein & Peptide Letters
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123. 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
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124. Chou, K.C. and Shen, H.B. (2009) Recent Advances in Developing Web-Servers for Predicting Protein
Attributes.
Natural Science
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125. 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
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1722-1727. https://doi.org/10.1039/C7MB00267J
126. 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.
https://doi.org/10.1016/j.gene.2017.07.036
127. Cheng, X., Xiao, X. and Chou, K.C. (2018) pLoc-mEuk: Predict Subcellular Localization of Multi-Label Euka-
ryotic Proteins by Extracting the Key GO Information into General PseAAC.
Genomics
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https://doi.org/10.1016/j.ygeno.2017.08.005
128. Cheng, X., Xiao, X. and Chou, K.C. (2018) pLoc-mGneg: Predict Subcellular Localization of Gram-Negative
Bacterial Proteins by Deep Gene Ontology Learning via General PseAAC.
Genomics
, 110, 231-239.
https://doi.org/10.1016/j.ygeno.2017.10.002
129. Cheng, X., Zhao, S.G., Lin, W.Z., Xiao, X. and Chou, K.C. (2017) pLoc-mAnimal: Predict Subcellular Localiza-
tion of Animal Proteins with Both Single and Multiple Sites.
Bioinformatics
, 33, 3524-3531.
https://doi.org/10.1093/bioinformatics/btx476
130. Xiao, X., Cheng, X., Su, S., Nao, Q. and Chou, K.C. (2017) pLoc-mGpos: Incorporate Key Gene Ontology In-
formation into General PseAAC for Predicting Subcellular Localization of Gram-Positive Bacterial Proteins.
Natural Science
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131. Cheng, X., Xiao, X. and Chou, K.C. (2018) pLoc-mHum: Predict Subcellular Localization of Multi-Location
Human Proteins via General PseAAC to Winnow out the Crucial GO Information.
Bioinformatics
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132. Qiu, W.R., Sun, B.Q., Xiao, X., Xu, Z.C., Jia, J.H. and Chou, K.C. (2018) iKcr-PseEns: Identify Lysine Crotonyla-
tion Sites in Histone Proteins with Pseudo Components and Ensemble Classifier.
Genomics
, 110, 239-246.
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133. 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.
https://doi.org/10.1093/bioinformatics/btx387
134. Feng, P., Ding, H., Yang, H., Chen, W., Lin, H. and Chou, K.C. (2017) iRNA-PseColl: Identifying the Occur-
rence Sites of Different RNA Modifications by Incorporating Collective Effects of Nucleotides into PseKNC.
Molecular Therapy
—
Nucleic Acids
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135. Liu, B., Wang, S., Long, R. and Chou, K.C. (2017) iRSpot-EL: Identify Recombination Spots with an Ensemble
Learning Approach.
Bioinformatics
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136. Liu, B., Yang, F. and Chou, K.C. (2017) 2L-piRNA: A Two-Layer Ensemble Classifier for Identifying Pi-
wi-Interacting RNAs and Their Function.
Molecular Therapy
—
Nucleic Acids
, 7, 267-277.
https://doi.org/10.1016/j.omtn.2017.04.008
137. Qiu, W.R., Jiang, S.Y., Xu, Z.C., Xiao, X. and Chou, K.C. (2017) iRNAm5C-PseDNC: Identifying RNA
5-Methylcytosine Sites by Incorporating Physical-Chemical Properties into Pseudo Dinucleotide Composition.
Oncotarget
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138. 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
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139. Chou, K.C., Cheng, X. and Xiao, X. (2019) pLoc_bal-mEuk: Predict Subcellular Localization of Eukaryotic Pro-
teins by General PseAAC and Quasi-Balancing Training Dataset.
Medicinal Chemistry
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140. Cheng, X., Xiao, X. and Chou, K.C. (2018) pLoc_bal-mGneg: Predict Subcellular Localization of Gram-Negative
Bacterial Proteins by Quasi-Balancing Training Dataset and General PseAAC.
Journal of Theoretical Biology
,
458, 92-102. https://doi.org/10.1016/j.jtbi.2018.09.005
141. Cheng, X., Xiao, X. and Chou, K.C. (2018) pLoc_bal-mPlant: Predict Subcellular Localization of Plant Proteins
by General PseAAC and Balancing Training Dataset.
Current Pharmaceutical Design
, 24, 4013-4022.
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142. Chou, K.C., Cheng, X. and Xiao, X. (2019) pLoc_bal-mHum: Predict Subcellular Localization of Human Pro-
teins by PseAAC and Quasi-Balancing Training Dataset.
Genomics
, 111, 1274-1282.
https://doi.org/10.1016/j.ygeno.2018.08.007
143. Xiao, X., Cheng, X., Chen, G., Mao, Q. and Chou, K.C. (2019) pLoc_bal-mGpos: Predict Subcellular Localiza-
tion of Gram-Positive Bacterial Proteins by Quasi-Balancing Training Dataset and PseAAC.
Genomics
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886-892. https://doi.org/10.1016/j.ygeno.2018.05.017
144. Cheng, X., Lin, W.Z., Xiao, X. and Chou, K.C. (2019) pLoc_bal-mAnimal: Predict Subcellular Localization of
Animal Proteins by Balancing Training Dataset and PseAAC.
Bioinformatics
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145. Chou, K.C., Jiang, S.P., Liu, W.M. and Fee, C.H. (1979) Graph Theory of Enzyme Kinetics: 1. Steady-State Reac-
tion System.
Scientia Sinica
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146. Chou, K.C. and Forsen, S. (1980) Graphical Rules for Enzyme-Catalyzed Rate Laws.
Biochemical Journal
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147. Chou, K.C., Forsen, S. and Zhou, G.Q. (1980) Three Schematic Rules for Deriving Apparent Rate Constants.
Chemica Scripta
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148. Chou, K.C., Carter, R.E. and Forsen, S. (1981) A New Graphical Method for Deriving Rate Equations for Com-
plicated Mechanisms.
Chemica Scripta
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149. Chou, K.C. and Forsen, S. (1981) Graphical Rules of Steady-State Reaction Systems.
Canadian Journal of Che-
mistry
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150. Zhou, G.P. and Deng, M.H. (1984) An Extension of Chou’s Graphic Rules for Deriving Enzyme Kinetic Equa-
tions to Systems Involving Parallel Reaction Pathways.
Biochemical Journal
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151. Chou, K.C. (1989) Graphic Rules in Steady and Non-Steady Enzyme Kinetics.
The Journal of Biological Chemi-
stry
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152. Althaus, I.W., Chou, J.J., Gonzales, A.J., Diebel, M.R., Chou, K.C., Kezdy, F.J., Romero, D.L., Aristoff, P.A.,
Tarpley, W.G. and Reusser, F. (1993) Steady-State Kinetic Studies with the Non-Nucleoside HIV-1 Reverse
Transcriptase Inhibitor U-87201E.
The Journal of Biological Chemistry
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153. Chou, K.C. (1990) Review: Applications of Graph Theory to Enzyme Kinetics and Protein Folding Kinetics.
Steady and Non-Steady State Systems.
Biophysical Chemistry
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154. Althaus, I.W., Gonzales, A.J., Chou, J.J., Diebel, M.R., Chou, K.C., Kezdy, F.J., Romero, D.L., Aristoff, P.A.,
Tarpley, W.G. and Reusser, F. (1993) The Quinoline U-78036 Is a Potent Inhibitor of HIV-1 Reverse Transcrip-
tase.
The Journal of Biological Chemistry
, 268, 14875-14880.
155. Chou, K.C. (2010) Graphic Rule for Drug Metabolism Systems.
Current Drug Metabolism
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156. Zhou, G.P. (2011) The Disposition of the LZCC Protein Residues in Wenxiang Diagram Provides New Insights
into the Protein-Protein Interaction Mechanism.
Journal of Theoretical Biology
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157. Althaus, I.W., Chou, J.J., Gonzales, A.J., Diebel, M.R., Chou, K.C., Kezdy, F.J., Romero, D.L., Aristoff, P.A.,
Tarpley, W.G. and Reusser, F. (1993) Kinetic Studies with the Nonnucleoside HIV-1 Reverse Transcriptase In-
hibitor U-88204E.
Biochemistry
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158. Chou, K.C., Lin, W.Z. and Xiao, X. (2011) Wenxiang: A Web-Server for Drawing Wenxiang Diagrams.
Natural
Science
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159. Chou, K.C. and Forsen, S. (1980) Diffusion-Controlled Effects in Reversible Enzymatic Fast Reaction System:
Critical Spherical Shell and Proximity Rate Constants.
Biophysical Chemistry
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https://doi.org/10.1016/0301-4622(80)80002-0
160. Chou, K.C., Li, T.T. and Forsen, S. (1980) The Critical Spherical Shell in Enzymatic Fast Reaction Systems.
Bio-
physical Chemistry
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161. Shen, H.B., Song, J.N. and Chou, K.C. (2009) Prediction of Protein Folding Rates from Primary Sequence by
Fusing Multiple Sequential Features.
Journal of Biomedical Science and Engineering
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https://doi.org/10.4236/jbise.2009.23024
162. Chou, K.C., Chen, N.Y. and Forsen, S. (1981) The Biological Functions of Low-Frequency Phonons: 2. Coopera-
tive Effects.
Chemica Scripta
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163. Chou, K.C. (1988) Review: Low-Frequency Collective Motion in Biomacromolecules and Its Biological Func-
tions.
Biophysical Chemistry
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164. Chou, K.C. (2019) The Cradle of Gordon Life Science Institute and Its Development and Driving Force.
Int J
Biol Genetics
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165. Chou, K.C. (2019) Showcase to Illustrate How the Web-Server iDNA6mA-PseKNC Is Working.
Journal of Pa-
thology Research Reviews & Reports
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166. Chou, K.C. (2019) The pLoc_bal-mPlant Is a Powerful Artificial Intelligence Tool for Predicting the Subcellular
Localization of Plant Proteins Purely Based on Their Sequence Information.
International Journal of Nutrition
Sciences
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167. Chou, K.C. (2019) Showcase to Illustrate How the Web-Server iNitro-Tyr Is Working.
Glo J of Com Sci and
Infor Tec
., 2, 1-16.
168. Chou, K.C. (2019) Gordon Life Science Institute: Its Philosophy, Achievements, and Perspective.
Annals of
Cancer Therapy and Pharmacology
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https://onomyscience.com/onomy/cancer_archive_volume2_issue2.html
169. Chou, K.C. (2019) The pLoc_bal-mAnimal Is a Powerful Artificial Intelligence Tool for Predicting the Subcellu-
lar Localization of Animal Proteins Based on Their Sequence Information Alone.
Scientific Journal of Biome-
trics & Biostatistics
, 2, 1-13.
170. Chou, K.C. (2020) Showcase to Illustrate How the Webserver pLoc_bal-mEuk Is Working.
Biomedical Journal
of Scientific & Technical Research
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171. Chou, K.C. (2020) The pLoc_bal-mGneg Predictor Is a Powerful Web-Server for Identifying the Subcellular
Localization of Gram-Negative Bacterial Proteins Based on Their Sequences Information Alone.
ijSci
, 9, 27-34.
https://doi.org/10.18483/ijSci.2248
172. Chou, K.C. (2020) How the Artificial Intelligence Tool iRNA-2methyl Is Working for RNA 2’-Omethylation
Sites.
Journal of Medical Care Research and Review
, 3, 348-366.
173. Chou, K.-C. (2020) Showcase to Illustrate How the Web-Server iKcr-PseEns Is Working.
Journal of Medical
Care Research and Review
, 3, 331-347. https://doi.org/10.18483/ijSci.2247
174. Chou, K.C. (2020) The pLoc_bal-mVirus Is a Powerful Artificial Intelligence Tool for Predicting the Subcellular
Localization of Virus Proteins According to Their Sequence Information Alone.
Journal of Genetics and Ge-
nomics
, 4.
175. Chou, K.C. (2019) How the Artificial Intelligence Tool iSNO-PseAAC Is Working in Predicting the Cysteine
s-Nitrosylation Sites in Proteins.
Journal of Stem Cell Research and Medicine
, 4, 1-9.
176. Chou, K.C. (2020) Showcase to Illustrate How the Web-Server iRNA-Methyl Is Working.
Journal of Molecular
Genetics
, 3, 1-7.
177. Chou, K.C. (2020) How the Artificial Intelligence Tool iRNA-PseU Is Working in Predicting the RNA Pseudou-
ridine Sites.
Biomedical Journal of Scientific & Technical Research
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178. Chou, K.C. (2020) Showcase to Illustrate How the Web-Server iSNO-AAPair Is Working.
Journal of Genetics
and Genomics
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179. Chou, K.C. (2020) The pLoc_bal-mHum Is a Powerful Web-Serve for Predicting the Subcellular Localization of
Human Proteins Purely Based on Their Sequence Information.
Advances in Bioengineering and Biomedical
Science Research
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180. Chou, K.C. (2020) Showcase to Illustrate How the Web-Server iPTM-mLys Is Working.
Infotext Journal of In-
fectious Diseases and Therapy
, 1, 1-16.
181. Chou, K.C. (2020) The pLoc_bal-mGpos Is a Powerful Artificial Intelligence Tool for Predicting the Subcellular
Localization of Gram-Positive Bacterial Proteins According to Their Sequence Information Alone.
Glo J of Com
Sci and Infor Tec
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182. Chou, K.C. (2020) Showcase to Illustrate How the Web-Server iPreny-PseAAC Is Working.
Glo J of Com Sci
and Infor Tec
., 2, 1-15.
183. Chou, K.C. (2020) Some Illuminating Remarks on Molecular Genetics and Genomics as Well as Drug Devel-
opment.
Molecular Genetics and Genomics
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184. Chou, K.C. (2020) The Problem of Elsevier Series Journals Online Submission by Using Artificial Intelligence.
Natural Science
, 12, 37-38. https://doi.org/10.4236/ns.2020.122006
185. Chou, K.C. (2020) The Most Important Ethical Concerns in Science.
Natural Science
, 12, 35-36.
https://doi.org/10.4236/ns.2020.122005
186. Chou, K.C. (2020) Other Mountain Stones Can Attack Jade: The 5-Steps Rule.
Natural Scienc
e, 12, 59-64.
https://doi.org/10.4236/ns.2020.123011
187. Chou, K.C. (2020) Using Similarity Software to Evaluate Scientific Paper Quality Is a Big Mistake,
Natural
Science
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188. Chou, K.C. (2020) Gordon Life Science Institute and Its Impacts on Computational Biology and Drug Devel-
opment.
Natural Science
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