Using subsite coupling to predict signal peptides

Identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites

Nielsen, H., Engelbrecht, J., Brunak, S., & von HEIJNE, G. (1997) · Protein Engineering Design and Selection · doi:10.1093/protein/10.1.1 · Cited by 5,343

A new method for predicting signal sequence cleavage sites

von HEIJNE, G. (1986) · Nucleic Acids Research · doi:10.1093/nar/14.11.4683 · Cited by 4,473

Prediction of Protein Structural Classes

Chou, K., & Zhang, C. (1995) · Critical Reviews in Biochemistry and Molecular Biology · doi:10.3109/10409239509083488 · Cited by 1,054

Bioinformatics the machine learning approach

Baldi, P., & Brunak, S. (n.d.) · Cited by 925

Machine learning approaches for the prediction of signal peptides and other protein sorting signals

Nielsen, H., Brunak, S., & von HEIJNE, G. (1999) · Protein Engineering Design and Selection · doi:10.1093/protein/12.1.3 · Cited by 609

Using neural networks for prediction of the subcellular location of proteins

Reinhardt, A. (1998) · Nucleic Acids Research · doi:10.1093/nar/26.9.2230 · Cited by 597

Improved recognition of native-like protein structures using a combination of sequence-dependent and sequence-independent features of proteins

Simons, K. T., Ruczinski, I., Kooperberg, C. L., Fox, B. A., Bystroff, C., & Baker, D. A. (1999) · Proteins Structure Function and Bioinformatics · doi:10.1002/(sici)1097-0134(19990101)34:1 · Cited by 443

Signal sequences

Gierasch, L. M. (1989) · Biochemistry · doi:10.1021/bi00429a001 · Cited by 424

Relation between amino acid composition and cellular location of proteins 1 1Edited by F. E. Cohen

Cedano, J. A., Aloy, P., Perez-Pons, J. A., & Querol, E. (1997) · Journal of Molecular Biology · doi:10.1006/jmbi.1996.0804 · Cited by 397

Protein subcellular location prediction

Chou, K., & Elrod, D. W. (1999) · Protein Engineering Design and Selection · doi:10.1093/protein/12.2.107 · Cited by 375

Support vector machines for prediction of protein signal sequences and their cleavage sites

Cai, Y., Lin, S.-L., & Chou, K. (2003) · Peptides · doi:10.1016/s0196-9781(02)00289-9 · Cited by 71

SPEPlip: the detection of signal peptide and lipoprotein cleavage sites

Fariselli, P., Finocchiaro, G., & Casadio, R. (2003) · Bioinformatics · doi:10.1093/bioinformatics/btg360 · Cited by 71

Identification and Characterization of Maizefloury4as a Novel Semidominant Opaque Mutant That Disrupts Protein Body Assembly

Wang, G., Qi, W., Wu, Q., Yao, D., Zhang, J., Zhu, J., Wang, G., Wang, G., Tang, Y., & Song, R. (2014) · PLANT PHYSIOLOGY · doi:10.1104/pp.114.238030 · Cited by 68

Delaunay triangulation with partial least squares projection to latent structures: a model for G-protein coupled receptors classification and fast structure recognition

Wen, Z., Li, M., Li, Y., Guo, Y., & Wang, K. (2006) · Amino Acids · doi:10.1007/s00726-006-0341-y · Cited by 68

iPhosY-PseAAC: identify phosphotyrosine sites by incorporating sequence statistical moments into PseAAC

Khan, Y. D., Rasool, N., Hussain, W., Khan, S. A., & Chou, K. (2018) · Molecular Biology Reports · doi:10.1007/s11033-018-4417-z · Cited by 67

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

Kabir, M., Ahmad, S., Iqbal, M. J., & Hayat, M. (2019) · Genomics · doi:10.1016/j.ygeno.2019.02.006 · Cited by 66

iSulfoTyr-PseAAC: Identify Tyrosine Sulfation Sites by Incorporating Statistical Moments via Chou’s 5-steps Rule and Pseudo Components

Barukab, O., Khan, Y. D., Khan, S. A., & Chou, K. (2019) · Current Genomics · doi:10.2174/1389202920666190819091609 · Cited by 66

iMethylK-PseAAC: Improving Accuracy of Lysine Methylation Sites Identification by Incorporating Statistical Moments and Position Relative Features into General PseAAC via Chou’s 5-steps Rule

Ilyas, S., Hussain, W., Ashraf, A., Khan, Y. D., Khan, S. A., & Chou, K. C. (2019) · Current Genomics · doi:10.2174/1389202920666190809095206 · Cited by 65

A novel alignment-free method to classify protein folding types by combining spectral graph clustering with Chou's pseudo amino acid composition

Tripathi, P., & Pandey, P. N. (2017) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2017.04.027 · Cited by 64

dForml(KNN)-PseAAC: Detecting formylation sites from protein sequences using K-nearest neighbor algorithm via Chou's 5-step rule and pseudo components

Qiao, N., Ma, Z., & Zhao, X. (2019) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2019.03.011 · Cited by 64

Prediction of antioxidant proteins by incorporating statistical moments based features into Chou's PseAAC

Butt, A. H., Rasool, N., & Khan, Y. D. (2019) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2019.04.019 · Cited by 64

RNAm5CPred: Prediction of RNA 5-Methylcytosine Sites Based on Three Different Kinds of Nucleotide Composition

Fang, T., Zhang, Z., Sun, R., Zhu, L., He, J., Huang, B., Xiong, Y., & Zhu, X. (2019) · Molecular Therapy — Nucleic Acids · doi:10.1016/j.omtn.2019.10.008 · Cited by 64

Identification of 4-carboxyglutamate residue sites based on position based statistical feature and multiple classification

Shah, A. A., & Khan, Y. D. (2020) · Scientific Reports · doi:10.1038/s41598-020-73107-y · Cited by 62

Identifying SNAREs by Incorporating Deep Learning Architecture and Amino Acid Embedding Representation

Le, N. Q. K., & Huỳnh, T.-T. (2019) · Frontiers in Physiology · doi:10.3389/fphys.2019.01501 · Cited by 62

pLoc_bal-mEuk: Predict Subcellular Localization of Eukaryotic Proteins by General PseAAC and Quasi-balancing Training Dataset

Chou, K., Cheng, X., & Xiao, X. (2018) · Medicinal Chemistry · doi:10.2174/1573406415666181218102517 · Cited by 59

pLoc_bal-mVirus: Predict Subcellular Localization of Multi-Label Virus Proteins by Chou's General PseAAC and IHTS Treatment to Balance Training Dataset

Xiao, X., Cheng, X., Chen, G.-Q., Mao, Q., & Chou, K. (2018) · Medicinal Chemistry · doi:10.2174/1573406415666181217114710 · Cited by 58

iPhosS(Deep)-PseAAC: Identification of Phosphoserine Sites in Proteins Using Deep Learning on General Pseudo Amino Acid Compositions

Naseer, S., Hussain, W., Khan, Y. D., & Rasool, N. (2020) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2020.3040747 · Cited by 58

Identification of Lysine Carboxylation Sites in Proteins by Integrating Statistical Moments and Position Relative Features via General PseAAC

Amanat, S., Ashraf, A., Hussain, W., Rasool, N., & Khan, Y. D. (2019) · Current Bioinformatics · doi:10.2174/1574893614666190723114923 · Cited by 57

Sequence based predictor for discrimination of enhancer and their types by applying general form of Chou's trinucleotide composition

Tahir, M., Hayat, M., & Kabir, M. (2017) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2017.05.008 · Cited by 55

iHyd-PseAAC (EPSV): Identifying Hydroxylation Sites in Proteins by Extracting Enhanced Position and Sequence Variant Feature via Chou's 5- Step Rule and General Pseudo Amino Acid Composition

Ehsan, A., Mahmood, M. K., Khan, Y. D., Barukab, O., Khan, S. A., & Chou, K. (2019) · Current Genomics · doi:10.2174/1389202920666190325162307 · Cited by 56

iGluK-Deep: computational identification of lysine glutarylation sites using deep neural networks with general pseudo amino acid compositions

Naseer, S., Ali, R. F., Khan, Y. D., & Dominic, P. D. D. (2021) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2021.1962738 · Cited by 58

Prediction of S-Sulfenylation Sites Using Statistical Moments Based Features via CHOU’S 5-Step Rule

Butt, A. H., & Khan, Y. D. (2019) · International Journal of Peptide Research and Therapeutics · doi:10.1007/s10989-019-09931-2 · Cited by 53

Identifying DNase I hypersensitive sites using multi-features fusion and F-score features selection via Chou's 5-steps rule

Liang, Y., & Zhang, S. (2019) · Biophysical Chemistry · doi:10.1016/j.bpc.2019.106227 · Cited by 51

An Epidemic Avian Influenza Prediction Model Based on Google Trends

Lu, Y., Wang, S., Wang, J., Zhou, G., Zhang, Q., Zhou, X., Niu, B., Chen, Q., & Chou, K. (2018) · Letters in Organic Chemistry · doi:10.2174/1570178615666180724103325 · Cited by 51

Predicting Secretory Proteins of Malaria Parasite by Incorporating Sequence Evolution Information into Pseudo Amino Acid Composition via Grey System Model

Lin, W., Fang, J., Xiao, X., & Chou, K. (2012) · PLoS ONE · doi:10.1371/journal.pone.0049040 · Cited by 51

iProtease-PseAAC(2L): A two-layer predictor for identifying proteases and their types using Chou's 5-step-rule and general PseAAC

Khan, Y. D., Amin, N., Hussain, W., Rasool, N., Khan, S. A., & Chou, K. (2019) · Analytical Biochemistry · doi:10.1016/j.ab.2019.113477 · Cited by 50

iSUMOK-PseAAC: prediction of lysine sumoylation sites using statistical moments and Chou’s PseAAC

Khan, Y. D., Khan, N. S., Naseer, S., & Butt, A. H. (2021) · PeerJ · doi:10.7717/peerj.11581 · Cited by 48

iPro70-FMWin: identifying Sigma70 promoters using multiple windowing and minimal features

Rahman, M. S., Aktar, U., Jani, M. R., & Shatabda, S. (2018) · Molecular Genetics and Genomics · doi:10.1007/s00438-018-1487-5 · Cited by 46

TargetFreeze: Identifying Antifreeze Proteins via a Combination of Weights using Sequence Evolutionary Information and Pseudo Amino Acid Composition

He, X., Han, K., Hu, J. S., Yan, H., Yang, J.-Y., Shen, H., & Yu, D. (2015) · The Journal of Membrane Biology · doi:10.1007/s00232-015-9811-z · Cited by 48

Using string kernel to predict signal peptide cleavage site based on subsite coupling model

Wang, M., Yang, J., & Chou, K.-C. (2005) · Amino Acids · doi:10.1007/s00726-005-0189-6 · Cited by 48

Fu-SulfPred: Identification of Protein S-sulfenylation Sites by Fusing Forests via Chou’s General PseAAC

Wang, L., Zhang, R., & Mu, Y. (2018) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2018.10.046 · Cited by 46

Sequence-based Identification of Allergen Proteins Developed by Integration of PseAAC and Statistical Moments via 5-Step Rule

Khan, Y. D., Alzahrani, E. O., Alghamdi, W., & Ullah, M. Z. (2020) · Current Bioinformatics · doi:10.2174/1574893615999200424085947 · Cited by 46

BlaPred: Predicting and classifying β-lactamase using a 3-tier prediction system via Chou's general PseAAC

Srivastava, A., Kumar, R., & Kumar, M. (2018) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2018.08.030 · Cited by 46

Review: Prediction of in Vivo Fates of Proteins in the Era of Genomics and Proteomics

Nakai, K. (2001) · Journal of Structural Biology · doi:10.1006/jsbi.2001.4378 · Cited by 46

Analysis and prediction of ion channel inhibitors by using feature selection and Chou's general pseudo amino acid composition

Mei, J., Fu, Y., & Zhao, J. J. (2018) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2018.07.040 · Cited by 45

Using CHOU'S 5-Steps Rule to Predict O-Linked Serine Glycosylation Sites by Blending Position Relative Features and Statistical Moment

Akmal, M. A., Hussain, W., Rasool, N., Khan, Y. D., Khan, S. A., & Chou, K. (2020) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2020.2968441 · Cited by 46

csDMA: an improved bioinformatics tool for identifying DNA 6 mA modifications via Chou’s 5-step rule

Liu, Z., Dong, W., Jiang, W., & He, Z. (2019) · Scientific Reports · doi:10.1038/s41598-019-49430-4 · Cited by 44

Glioma stages prediction based on machine learning algorithm combined with protein-protein interaction networks

Niu, B., Liang, C., Lu, Y., Zhao, M., Chen, Q., Zhang, Y., Zheng, L., & Chou, K. (2019) · Genomics · doi:10.1016/j.ygeno.2019.05.024 · Cited by 44

Prediction of piRNAs and their function based on discriminative intelligent model using hybrid features into Chou’s PseKNC

Khan, S. A., Khan, M., Iqbal, N., Khan, S. A., & Chou, K. (2020) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2020.104056 · Cited by 46

Predicting secretory protein signal sequence cleavage sites by fusing the marks of global alignments

Liu, D.-Q., Liu, H., Shen, H., Yang, J., & Chou, K.-C. (2006) · Amino Acids · doi:10.1007/s00726-006-0466-z · Cited by 44

iEnhancer-DHF: Identification of Enhancers and Their Strengths Using Optimize Deep Neural Network With Multiple Features Extraction Methods

Inayat, N., Khan, M., Iqbal, N., Khan, S. A., Raza, M., Khan, D. M., Khan, A., & Wei, D. (2021) · IEEE Access · doi:10.1109/access.2021.3062291 · Cited by 43

A Sequence-Based Predictor of Zika Virus Proteins Developed by Integration of PseAAC and Statistical Moments

Hussain, W., Rasool, N., & Khan, Y. D. (2020) · Combinatorial Chemistry & High Throughput Screening · doi:10.2174/1386207323666200428115449 · Cited by 42

N-MyristoylG-PseAAC: Sequence-based Prediction of N-Myristoyl Glycine Sites in Proteins by Integration of PseAAC and Statistical Moments

Khan, S. A., Khan, Y. D., Ahmad, S., & Allehaibi, K. H. S. (2018) · Letters in Organic Chemistry · doi:10.2174/1570178616666181217153958 · Cited by 40

Analysis and prediction of animal toxins by various Chou's pseudo components and reduced amino acid compositions

Pan, Y., Wang, S., Zhang, Q., Lu, Q., Su, D., Zuo, Y., & Yang, L. (2018) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2018.11.010 · Cited by 39

Predicting Golgi-resident protein types using pseudo amino acid compositions: Approaches with positional specific physicochemical properties

Jiao, Y.-S., & Du, P.-F. (2015) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2015.11.009 · Cited by 39

Prediction of Recombination Spots Using Novel Hybrid Feature Extraction Method via Deep Learning Approach

Khan, F. H., Khan, M., Iqbal, N., Khan, S. A., Khan, D. M., Khan, A., & Wei, D. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.539227 · Cited by 40

Impacts of Pseudo Amino Acid Components and 5-steps Rule to Proteomics and Proteome Analysis

Chou, K. (2019) · Current Topics in Medicinal Chemistry · doi:10.2174/1568026619666191018100141 · Cited by 38

Demonstration of two novel methods for predicting functional siRNA efficiency

Jia, P., Shi, T., Cai, Y., & Li, Y. (2006) · BMC Bioinformatics · doi:10.1186/1471-2105-7-271 · Cited by 38

Discrimination of membrane transporter protein types using K -nearest neighbor method derived from the similarity distance of total diversity measure

Zuo, Y., Su, W., Zhang, S., Wang, S.-S., Wu, C., Yang, L., & Li, G. (2015) · Molecular BioSystems · doi:10.1039/c4mb00681j · Cited by 38

GlyStruct: glycation prediction using structural properties of amino acid residues

Reddy, H. M., Sharma, A. K., Dehzangi, A., Shigemizu, D., Chandra, A. A., & Tsunoda, T. (2019) · BMC Bioinformatics · doi:10.1186/s12859-018-2547-x · Cited by 37

In-silico determination of Pichia pastoris signal peptides for extracellular recombinant protein production

Massahi, A., & Çalı́k, P. (2014) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2014.08.048 · Cited by 38

Prediction and analysis of protein palmitoylation sites

Hu, L., Wan, S.-B., Niu, S., Shi, X., Li, H., Cai, Y., & Chou, K. (2010) · Biochimie · doi:10.1016/j.biochi.2010.10.022 · Cited by 38

Antimicrobial activity predictors benchmarking analysis using shuffled and designed synthetic peptides

Porto, W. F., Pires, Á. S., & Franco, O. L. (2017) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2017.05.011 · Cited by 36

Deep-piRNA: Bi-Layered Prediction Model for PIWI-Interacting RNA Using Discriminative Features

Khan, S. A., Khan, M., Iqbal, N., Rahman, M. A. A., & Karim, M. K. A. (2022) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2022.022901 · Cited by 35

Prediction of Protein–Protein Interaction Sites with Machine-Learning-Based Data-Cleaning and Post-Filtering Procedures

Liu, G., Shen, H., & Yu, D. (2015) · The Journal of Membrane Biology · doi:10.1007/s00232-015-9856-z · Cited by 35

Signal-3L 2.0: A Hierarchical Mixture Model for Enhancing Protein Signal Peptide Prediction by Incorporating Residue-Domain Cross-Level Features

Zhang, Y.-Z., & Shen, H. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00484 · Cited by 35

Prediction and functional analysis of prokaryote lysine acetylation site by incorporating six types of features into Chou's general PseAAC

Chen, G., Cao, M., Yu, J., Guo, X., & Shi, S. (2018) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2018.10.047 · Cited by 35

Endogenous signal peptides in recombinant protein production by Pichia pastoris: From in-silico analysis to fermentation

Massahi, A., & Çalı́k, P. (2016) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2016.07.039 · Cited by 35

NPalmitoylDeep-PseAAC: A Predictor of N-Palmitoylation Sites in Proteins Using Deep Representations of Proteins and PseAAC via Modified 5-Steps Rule

Naseer, S., Hussain, W., Khan, Y. D., & Rasool, N. (2020) · Current Bioinformatics · doi:10.2174/1574893615999200605142828 · Cited by 35

iRSpot-DTS: Predict recombination spots by incorporating the dinucleotide-based spare-cross covariance information into Chou's pseudo components

Zhang, S., Yang, K., Lei, Y., & Song, K. (2018) · Genomics · doi:10.1016/j.ygeno.2018.11.031 · Cited by 32

iTIS-PseKNC: Identification of Translation Initiation Site in human genes using pseudo k-tuple nucleotides composition

Kabir, M., Iqbal, M. J., Ahmad, S., & Hayat, M. (2015) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2015.09.010 · Cited by 32

BLOMAP: AN ENCODING OF AMINO ACIDS WHICH IMPROVES SIGNAL PEPTIDE CLEAVAGE SITE PREDICTION

Maetschke, S., Towsey, M., & Mikael Bodén, A. (2005) · Asia-Pacific Bioinformatics Conference · doi:10.1142/9781860947322_0014 · Cited by 31

Prediction of protein signal sequences and their cleavage sites by statistical rulers

Liu, H., Yang, J., Ling, J., & Chou, K. (2005) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2005.10.046 · Cited by 31

Using Chou’s Pseudo Amino Acid Composition and Machine Learning Method to Predict the Antiviral Peptides

Zare, M., Mohabatkar, H., Faramarzi, F. K., Beigi, M. M., & Behbahani, M. (2015) · The Open Bioinformatics Journal · doi:10.2174/1875036201509010013 · Cited by 30

iMethyl-Deep: N6 Methyladenosine Identification of Yeast Genome with Automatic Feature Extraction Technique by Using Deep Learning Algorithm

Mahmoudi, O., Wahab, A., & Chong, K. T. (2020) · Genes · doi:10.3390/genes11050529 · Cited by 29

iNitroY-Deep: Computational Identification of Nitrotyrosine Sites to Supplement Carcinogenesis Studies Using Deep Learning

Naseer, S., Ali, R. F., Fati, S. M., & Muneer, A. (2021) · IEEE Access · doi:10.1109/access.2021.3080041 · Cited by 26

iAFP-Ense: An Ensemble Classifier for Identifying Antifreeze Protein by Incorporating Grey Model and PSSM into PseAAC

Xiao, X., Hui, M., & Liu, Z. (2016) · The Journal of Membrane Biology · doi:10.1007/s00232-016-9935-9 · Cited by 27

iAmideV-Deep: Valine Amidation Site Prediction in Proteins Using Deep Learning and Pseudo Amino Acid Compositions

Naseer, S., Ali, R. F., Muneer, A., & Fati, S. M. (2021) · Symmetry · doi:10.3390/sym13040560 · Cited by 26

iSS-PC: Identifying Splicing Sites via Physical-Chemical Properties Using Deep Sparse Auto-Encoder

Xu, Z., Wang, P., Qiu, W., & Xiao, X. (2017) · Scientific Reports · doi:10.1038/s41598-017-08523-8 · Cited by 27

The Molecular Dynamic Simulation of Zolpidem Interaction with Gamma Aminobutyric Acid Type A Receptor

Chen, C. Y. (2007) · Journal of the Chinese Chemical Society · doi:10.1002/jccs.200700093 · Cited by 24

Host Cell Proteome ofPhyscomitrella patensHarbors Proteases and Protease Inhibitors under Bioproduction Conditions

Hoernstein, S. N. W., Fode, B., Wiedemann, G., Lang, D., Niederkrüger, H., van den Berg, B. M. M., Schaaf, A., Frischmuth, T., Schlösser, A., Decker, E. L., et al. (2018) · Journal of Proteome Research · doi:10.1021/acs.jproteome.8b00423 · Cited by 24

i6mA-DNC: Prediction of DNA N6-Methyladenosine sites in rice genome based on dinucleotide representation using deep learning

Park, S., Wahab, A., Nazari, I., Ryu, J.-H., & Chong, K. T. (2020) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2020.104102 · Cited by 24

Structural class prediction of protein using novel feature extraction method from chaos game representation of predicted secondary structure

Zhang, L., Kong, L., Han, X., & Lv, J. (2016) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2016.04.011 · Cited by 23

pLoc_Deep-mVirus: A CNN Model for Predicting Subcellular Localization of Virus Proteins by Deep Learning

Shao, Y., & Chou, K. (2020) · Natural Science · doi:10.4236/ns.2020.126033 · Cited by 23

Construction of a machine learning-based artificial neural network for discriminating PANoptosis related subgroups to predict prognosis in low-grade gliomas

Chen, G., He, Z., Jiang, W., Li, L., Luo, B., Wang, X., & Zheng, X. (2022) · Scientific Reports · doi:10.1038/s41598-022-26389-3 · Cited by 23

Prediction of the subcellular localization of eukaryotic proteins using sequence signals and composition

Reczko, M., & Hatzigerrorgiou, A. (2004) · PROTEOMICS · doi:10.1002/pmic.200300769 · Cited by 22

A novel bacterial Water Hypersensitivity-like protein showsin vivoprotection against cold and freeze damage

Anderson, D. E., Ferreras, E., Trindade, M., & Cowan, D. A. (2015) · FEMS Microbiology Letters · doi:10.1093/femsle/fnv110 · Cited by 21

Identify Lysine Neddylation Sites Using Bi-profile Bayes Feature Extraction via the Chou’s 5-steps Rule and General Pseudo Components

Ju, Z., & Wang, S. (2019) · Current Genomics · doi:10.2174/1389202921666191223154629 · Cited by 20

DNAPred_Prot: Identification of DNA-Binding Proteins Using Composition- and Position-Based Features

Barukab, O., Khan, Y. D., Khan, S. A., & Chou, K. (2022) · Applied Bionics and Biomechanics · doi:10.1155/2022/5483115 · Cited by 20

Identification of DNA N6-methyladenine sites by integration of sequence features

Wang, H., Xiao, F., Li, G., & Kong, Q. (2020) · Epigenetics & Chromatin · doi:10.1186/s13072-020-00330-2 · Cited by 19

pDHS-SVM: A prediction method for plant DNase I hypersensitive sites based on support vector machine

Zhang, S., Zhou, Z., Chen, X., Hu, Y. Q., & Yang, L. (2017) · Journal of Theoretical Biology · doi:10.1016/j.jtbi.2017.05.030 · Cited by 18

iPhosD-PseAAC: Identification of phosphoaspartate sites in proteins using statistical moments and PseAAC

Almagrabi, A. O., Khan, Y. D., & Khan, S. A. (2021) · Biocell · doi:10.32604/biocell.2021.013770 · Cited by 18

Genetic Complementation in Yeast Reveals Functional Similarities between the Catalytic Subunits of Mammalian Signal Peptidase Complex

Liang, H., VanValkenburgh, C., Chen, X., Mullins, C., Kaer, L. V., Green, N., & Fang, H. (2003) · Journal of Biological Chemistry · doi:10.1074/jbc.m307542200 · Cited by 18

PAAT, a novel ATPase and trans‐ regulator of mitochondrial ABC transporters, is critically involved in the maintenance of mitochondrial homeostasis

Yang, X., Yang, J., Li, L., Sun, L., Yi, X., Han, X., Si, W., Yan, R., Chen, Z., Xie, G., et al. (2014) · The FASEB Journal · doi:10.1096/fj.14-254045 · Cited by 17

Biochemical Genetics and Genomics of Insect Esterases

Oakeshott, J. G., Claudianos, C., Campbell, P., Newcomb, R. D., & Russell, R. J. M. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-809633-8.04063-2 · Cited by 18

Identifying anticancer peptides by using a generalized chaos game representation

Ge, L., Liu, J., Zhang, Y., & Dehmer, M. (2018) · Journal of Mathematical Biology · doi:10.1007/s00285-018-1279-x · Cited by 16

Why does ethanol induce cellular heat-shock response?

Chaudhuri, S., Jana, B., & Basu, T. (2006) · Cell Biology and Toxicology · doi:10.1007/s10565-006-0029-9 · Cited by 17

pLoc_Deep-mPlant: Predict Subcellular Localization of Plant Proteins by Deep Learning

Shao, Y., Liu, X., Lu, Z., & Chou, K. (2020) · Natural Science · doi:10.4236/ns.2020.125021 · Cited by 16

A convolution neural network-based computational model to identify the occurrence sites of various RNA modifications by fusing varied features

Tahir, M., Hayat, M., & Chong, K. T. (2021) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2021.104233 · Cited by 16

Prediction of interface residue based on the features of residue interaction network

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Influence of N-Terminal Truncations on the Functional Expression of Bacillus licheniformis γ-Glutamyltranspeptidase in Recombinant Escherichia coli

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