Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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:

iEnhancer-2L: a two-layer predictor for identifying enhancers and their strength by pseudo k-tuple nucleotide composition
Bin Liu, Longyun Fang, Ren Long, et al.
Bioinformatics (2015) Vol. 32, Iss. 3, pp. 362-369
Open Access | Times Cited: 352

Showing 1-25 of 352 citing articles:

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
iLearn: an integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data
Zhen Chen, Pei Zhao, Fuyi Li, et al.
Briefings in Bioinformatics (2019) Vol. 21, Iss. 3, pp. 1047-1057
Closed Access | Times Cited: 372
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
iPromoter-2L: a two-layer predictor for identifying promoters and their types by multi-window-based PseKNC
Bin Liu, Fan Yang, De-Shuang Huang, et al.
Bioinformatics (2017) Vol. 34, Iss. 1, pp. 33-40
Open Access | Times Cited: 318
pSuc-Lys: Predict lysine succinylation sites in proteins with PseAAC and ensemble random forest approach
Jianhua Jia, Zi Liu, Xuan Xiao, et al.
Journal of Theoretical Biology (2016) Vol. 394, pp. 223-230
Closed Access | Times Cited: 315
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
iRSpot-EL: identify recombination spots with an ensemble learning approach
Bin Liu, Shanyi Wang, Long Ren, et al.
Bioinformatics (2016) Vol. 33, Iss. 1, pp. 35-41
Open Access | Times Cited: 295
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
iRNA-PseU: Identifying RNA pseudouridine sites.
Wei Chen, Hua Tang, Jing Ye, et al.
DOAJ (DOAJ: Directory of Open Access Journals) (2016) Vol. 5, pp. e332-e332
Closed Access | Times Cited: 284
iSuc-PseOpt: Identifying lysine succinylation sites in proteins by incorporating sequence-coupling effects into pseudo components and optimizing imbalanced training dataset
Jianhua Jia, Zi Liu, Xuan Xiao, et al.
Analytical Biochemistry (2015) Vol. 497, pp. 48-56
Closed Access | Times Cited: 266
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
iPTM-mLys: identifying multiple lysine PTM sites and their different types
Wang‐Ren Qiu, Bi‐Qian Sun, Xuan Xiao, et al.
Bioinformatics (2016) Vol. 32, Iss. 20, pp. 3116-3123
Open 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
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
iRNA-AI: identifying the adenosine to inosine editing sites in RNA sequences
Wei Chen, Pengmian Feng, Hui Yang, et al.
Oncotarget (2016) Vol. 8, Iss. 3, pp. 4208-4217
Open Access | Times Cited: 229
pLoc-mEuk: Predict subcellular localization of multi-label eukaryotic proteins by extracting the key GO information into general PseAAC
Xiang Cheng, Xuan Xiao, Kuo‐Chen Chou
Genomics (2017) Vol. 110, Iss. 1, pp. 50-58
Open Access | Times Cited: 228
iATC-mISF: a multi-label classifier for predicting the classes of anatomical therapeutic chemicals
Xiang Cheng, Shuguang Zhao, Xuan Xiao, et al.
Bioinformatics (2016) Vol. 33, Iss. 3, pp. 341-346
Open Access | Times Cited: 217
iDHS-EL: identifying DNase I hypersensitive sites by fusing three different modes of pseudo nucleotide composition into an ensemble learning framework
Bin Liu, Long Ren, Kuo‐Chen Chou
Bioinformatics (2016) Vol. 32, Iss. 16, pp. 2411-2418
Closed Access | Times Cited: 208
Meta-4mCpred: A Sequence-Based Meta-Predictor for Accurate DNA 4mC Site Prediction Using Effective Feature Representation
Balachandran Manavalan, Shaherin Basith, Tae Hwan Shin, et al.
Molecular Therapy — Nucleic Acids (2019) Vol. 16, pp. 733-744
Open Access | Times Cited: 200
iLearnPlus:a comprehensive and automated machine-learning platform for nucleic acid and protein sequence analysis, prediction and visualization
Zhen Chen, Pei Zhao, Chen Li, et al.
Nucleic Acids Research (2021) Vol. 49, Iss. 10, pp. e60-e60
Open Access | Times Cited: 199
iRNAm5C-PseDNC: identifying RNA 5-methylcytosine sites by incorporating physical-chemical properties into pseudo dinucleotide composition
Wang‐Ren Qiu, Shiyu Jiang, Zhaochun Xu, et al.
Oncotarget (2017) Vol. 8, Iss. 25, pp. 41178-41188
Open Access | Times Cited: 198
iEnhancer-EL: identifying enhancers and their strength with ensemble learning approach
Bin Liu, Kai Li, De-Shuang Huang, et al.
Bioinformatics (2018) Vol. 34, Iss. 22, pp. 3835-3842
Open Access | Times Cited: 198
Recurrent Neural Network for Predicting Transcription Factor Binding Sites
Zhen Shen, Wenzheng Bao, De-Shuang Huang
Scientific Reports (2018) Vol. 8, Iss. 1
Open Access | Times Cited: 198
Page 1 - Next Page

Scroll to top