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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Combining evolutionary information extracted from frequency profiles with sequence-based kernels for protein remote homology detection
Bin Liu, Deyuan Zhang, Ruifeng Xu, et al.
Bioinformatics (2013) Vol. 30, Iss. 4, pp. 472-479
Open Access | Times Cited: 269

Showing 1-25 of 269 citing articles:

iPro54-PseKNC: a sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition
Hao Lin, En-Ze Deng, Hui Ding, et al.
Nucleic Acids Research (2014) Vol. 42, Iss. 21, pp. 12961-12972
Open Access | Times Cited: 491
Predicting antimicrobial peptides with improved accuracy by incorporating the compositional, physico-chemical and structural features into Chou’s general PseAAC
Prabina Kumar Meher, Tanmaya Kumar Sahu, Varsha Saini, et al.
Scientific Reports (2017) Vol. 7, Iss. 1
Open Access | Times Cited: 453
iACP: a sequence-based tool for identifying anticancer peptides
Wei Chen, Hui Ding, Pengmian Feng, et al.
Oncotarget (2016) Vol. 7, Iss. 13, pp. 16895-16909
Open Access | Times Cited: 409
A novel features ranking metric with application to scalable visual and bioinformatics data classification
Quan Zou, Jiancang Zeng, Liujuan Cao, et al.
Neurocomputing (2015) Vol. 173, pp. 346-354
Closed Access | Times Cited: 378
BioSeq-Analysis2.0: an updated platform for analyzing DNA, RNA and protein sequences at sequence level and residue level based on machine learning approaches
Bin Liu, Xin Gao, Hanyu Zhang
Nucleic Acids Research (2019) Vol. 47, Iss. 20, pp. e127-e127
Open Access | Times Cited: 356
Prediction and analysis of essential genes using the enrichments of gene ontology and KEGG pathways
Lei Chen, Yu-Hang Zhang, ShaoPeng Wang, et al.
PLoS ONE (2017) Vol. 12, Iss. 9, pp. e0184129-e0184129
Open Access | Times Cited: 348
iRNA-Methyl: Identifying N6-methyladenosine sites using pseudo nucleotide composition
Wei Chen, Pengmian Feng, Hui Ding, et al.
Analytical Biochemistry (2015) Vol. 490, pp. 26-33
Closed Access | Times Cited: 336
iDNA-Prot|dis: Identifying DNA-Binding Proteins by Incorporating Amino Acid Distance-Pairs and Reduced Alphabet Profile into the General Pseudo Amino Acid Composition
Bin Liu, Jinghao Xu, Xun Lan, et al.
PLoS ONE (2014) Vol. 9, Iss. 9, pp. e106691-e106691
Open Access | Times Cited: 307
BioSeq-Analysis: a platform for DNA, RNA and protein sequence analysis based on machine learning approaches
Bin Liu
Briefings in Bioinformatics (2017) Vol. 20, Iss. 4, pp. 1280-1294
Closed Access | Times Cited: 301
iDNA6mA-PseKNC: Identifying DNA N6-methyladenosine sites by incorporating nucleotide physicochemical properties into PseKNC
Pengmian Feng, Hui Yang, Hui Ding, et al.
Genomics (2018) Vol. 111, Iss. 1, pp. 96-102
Open Access | Times Cited: 298
iRNA-PseColl: Identifying the Occurrence Sites of Different RNA Modifications by Incorporating Collective Effects of Nucleotides into PseKNC
Pengmian Feng, Hui Ding, Hui Yang, et al.
Molecular Therapy — Nucleic Acids (2017) Vol. 7, pp. 155-163
Open Access | Times Cited: 285
iPPI-Esml: An ensemble classifier for identifying the interactions of proteins by incorporating their physicochemical properties and wavelet transforms into PseAAC
Jianhua Jia, Zi Liu, Xuan Xiao, et al.
Journal of Theoretical Biology (2015) Vol. 377, pp. 47-56
Closed Access | Times Cited: 277
iTIS-PseTNC: A sequence-based predictor for identifying translation initiation site in human genes using pseudo trinucleotide composition
Wei Chen, Pengmian Feng, En-Ze Deng, et al.
Analytical Biochemistry (2014) Vol. 462, pp. 76-83
Closed Access | Times Cited: 258
pRNAm-PC: Predicting N6-methyladenosine sites in RNA sequences via physical–chemical properties
Zi Liu, Xuan Xiao, Dong‐Jun Yu, et al.
Analytical Biochemistry (2015) Vol. 497, pp. 60-67
Closed Access | Times Cited: 257
iDNA-Methyl: Identifying DNA methylation sites via pseudo trinucleotide composition
Zi Liu, Xuan Xiao, Wang‐Ren Qiu, et al.
Analytical Biochemistry (2015) Vol. 474, pp. 69-77
Closed Access | Times Cited: 256
Local-DPP: An improved DNA-binding protein prediction method by exploring local evolutionary information
Leyi Wei, Jijun Tang, Quan Zou
Information Sciences (2016) Vol. 384, pp. 135-144
Closed Access | Times Cited: 254
Pse-in-One 2.0: An Improved Package of Web Servers for Generating Various Modes of Pseudo Components of DNA, RNA, and Protein Sequences
Bin Liu, Hao Wu, Kuo‐Chen Chou
Natural Science (2017) Vol. 09, Iss. 04, pp. 67-91
Open Access | Times Cited: 252
Multi-categorical deep learning neural network to classify retinal images: A pilot study employing small database
Joon Yul Choi, Tae Keun Yoo, Jeong Gi Seo, et al.
PLoS ONE (2017) Vol. 12, Iss. 11, pp. e0187336-e0187336
Open Access | Times Cited: 251
Similarity computation strategies in the microRNA-disease network: a survey
Quan Zou, Jinjin Li, Song Li, et al.
Briefings in Functional Genomics (2015), pp. elv024-elv024
Open Access | Times Cited: 244
Gram-positive and Gram-negative protein subcellular localization by incorporating evolutionary-based descriptors into Chou׳s general PseAAC
Abdollah Dehzangi, Rhys Heffernan, Alok Sharma, et al.
Journal of Theoretical Biology (2014) Vol. 364, pp. 284-294
Open Access | Times Cited: 241
2L-piRNA: A Two-Layer Ensemble Classifier for Identifying Piwi-Interacting RNAs and Their Function
Bin Liu, Fan Yang, Kuo‐Chen Chou
Molecular Therapy — Nucleic Acids (2017) Vol. 7, pp. 267-277
Open Access | Times Cited: 241
Identification of Real MicroRNA Precursors with a Pseudo Structure Status Composition Approach
Bin Liu, Longyun Fang, Fule Liu, et al.
PLoS ONE (2015) Vol. 10, Iss. 3, pp. e0121501-e0121501
Open Access | Times Cited: 239
iRSpot-TNCPseAAC: Identify Recombination Spots with Trinucleotide Composition and Pseudo Amino Acid Components
Wang-Ren Qiu, Xuan Xiao, Kuo‐Chen Chou
International Journal of Molecular Sciences (2014) Vol. 15, Iss. 2, pp. 1746-1766
Open Access | Times Cited: 236
iNitro-Tyr: Prediction of Nitrotyrosine Sites in Proteins with General Pseudo Amino Acid Composition
Yan Xu, Xin Wen, Wen Lishu, et al.
PLoS ONE (2014) Vol. 9, Iss. 8, pp. e105018-e105018
Open Access | Times Cited: 232
Finding the Best Classification Threshold in Imbalanced Classification
Quan Zou, Sifa Xie, Ziyu Lin, et al.
Big Data Research (2016) Vol. 5, pp. 2-8
Closed Access | Times Cited: 225
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