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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:

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 51-75 of 352 citing articles:

PseUI: Pseudouridine sites identification based on RNA sequence information
Jingjing He, Ting Fang, Zizheng Zhang, et al.
BMC Bioinformatics (2018) Vol. 19, Iss. 1
Open Access | Times Cited: 116
iMem-2LSAAC: A two-level model for discrimination of membrane proteins and their types by extending the notion of SAAC into chou's pseudo amino acid composition
Muhammad Arif, Maqsood Hayat, Zahoor Jan
Journal of Theoretical Biology (2018) Vol. 442, pp. 11-21
Closed Access | Times Cited: 108
iPPI-PseAAC(CGR): Identify protein-protein interactions by incorporating chaos game representation into PseAAC
Jianhua Jia, Xiaoyan Li, Wang‐Ren Qiu, et al.
Journal of Theoretical Biology (2018) Vol. 460, pp. 195-203
Closed Access | Times Cited: 105
EnhancerPred: a predictor for discovering enhancers based on the combination and selection of multiple features
Cangzhi Jia, Wenying He
Scientific Reports (2016) Vol. 6, Iss. 1
Open Access | Times Cited: 101
Imbalanced multi-label learning for identifying antimicrobial peptides and their functional types
Wei‐Zhong Lin, Dong Xu
Bioinformatics (2016) Vol. 32, Iss. 24, pp. 3745-3752
Open Access | Times Cited: 96
Sequence-based predictive modeling to identify cancerlectins
Hong-Yan Lai, Xinxin Chen, Wei Chen, et al.
Oncotarget (2017) Vol. 8, Iss. 17, pp. 28169-28175
Open Access | Times Cited: 96
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
Muhammad Awais, Waqar Hussain, Yaser Daanial Khan, et al.
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2019) Vol. 18, Iss. 2, pp. 596-610
Closed Access | Times Cited: 95
Prediction of Protein Submitochondrial Locations by Incorporating Dipeptide Composition into Chou’s General Pseudo Amino Acid Composition
Khurshid Ahmad, Muhammad Adeel Waris, Maqsood Hayat
The Journal of Membrane Biology (2016) Vol. 249, Iss. 3, pp. 293-304
Closed Access | Times Cited: 93
Prediction of N-linked glycosylation sites using position relative features and statistical moments
Muhammad Aizaz Akmal, Nouman Rasool, Yaser Daanial Khan
PLoS ONE (2017) Vol. 12, Iss. 8, pp. e0181966-e0181966
Open Access | Times Cited: 92
pLoc_bal-mHum: Predict subcellular localization of human proteins by PseAAC and quasi-balancing training dataset
Kuo‐Chen Chou, Xiang Cheng, Xuan Xiao
Genomics (2018) Vol. 111, Iss. 6, pp. 1274-1282
Open Access | Times Cited: 92
PDC-SGB: Prediction of effective drug combinations using a stochastic gradient boosting algorithm
Qian Xu, Yi Xiong, Hao Dai, et al.
Journal of Theoretical Biology (2017) Vol. 417, pp. 1-7
Closed Access | Times Cited: 91
iDNA6mA (5-step rule): Identification of DNA N6-methyladenine sites in the rice genome by intelligent computational model via Chou's 5-step rule
Muhammad Tahir, Hilal Tayara, Kil To Chong
Chemometrics and Intelligent Laboratory Systems (2019) Vol. 189, pp. 96-101
Closed Access | Times Cited: 91
Advances in Predicting Subcellular Localization of Multi-label Proteins and its Implication for Developing Multi-target Drugs
Kuo‐Chen Chou
Current Medicinal Chemistry (2019) Vol. 26, Iss. 26, pp. 4918-4943
Closed Access | Times Cited: 91
A Review of DNA-binding Proteins Prediction Methods
Kaiyang Qu, Leyi Wei, Quan Zou
Current Bioinformatics (2018) Vol. 14, Iss. 3, pp. 246-254
Closed Access | Times Cited: 89
iPSW(2L)-PseKNC: A two-layer predictor for identifying promoters and their strength by hybrid features via pseudo K-tuple nucleotide composition
Xuan Xiao, Zhaochun Xu, Wang‐Ren Qiu, et al.
Genomics (2018) Vol. 111, Iss. 6, pp. 1785-1793
Open Access | Times Cited: 88
Prediction of lysine formylation sites using the composition of k-spaced amino acid pairs via Chou's 5-steps rule and general pseudo components
Zhe Ju, Shiyun Wang
Genomics (2019) Vol. 112, Iss. 1, pp. 859-866
Closed Access | Times Cited: 85
70ProPred: a predictor for discovering sigma70 promoters based on combining multiple features
Wenying He, Cangzhi Jia, Yucong Duan, et al.
BMC Systems Biology (2018) Vol. 12, Iss. S4
Open Access | Times Cited: 84
pLoc_bal-mGneg: Predict subcellular localization of Gram-negative bacterial proteins by quasi-balancing training dataset and general PseAAC
Xiang Cheng, Xuan Xiao, Kuo‐Chen Chou
Journal of Theoretical Biology (2018) Vol. 458, pp. 92-102
Closed Access | Times Cited: 84
MsDBP: Exploring DNA-Binding Proteins by Integrating Multiscale Sequence Information via Chou’s Five-Step Rule
Xiuquan Du, Yanyu Diao, Heng Liu, et al.
Journal of Proteome Research (2019) Vol. 18, Iss. 8, pp. 3119-3132
Closed Access | Times Cited: 84
iEnhancer-XG: interpretable sequence-based enhancers and their strength predictor
Lijun Cai, Xuanbai Ren, Xiangzheng Fu, et al.
Bioinformatics (2020) Vol. 37, Iss. 8, pp. 1060-1067
Open Access | Times Cited: 83
Progresses in Predicting Post-translational Modification
Kuo‐Chen Chou
International Journal of Peptide Research and Therapeutics (2019) Vol. 26, Iss. 2, pp. 873-888
Closed Access | Times Cited: 82
i6mA-Fuse: improved and robust prediction of DNA 6 mA sites in the Rosaceae genome by fusing multiple feature representation
Md Mehedi Hasan, Balachandran Manavalan, Watshara Shoombuatong, et al.
Plant Molecular Biology (2020) Vol. 103, Iss. 1-2, pp. 225-234
Closed Access | Times Cited: 78
Deepm5C: A deep-learning-based hybrid framework for identifying human RNA N5-methylcytosine sites using a stacking strategy
Md Mehedi Hasan, Sho Tsukiyama, Jae Youl Cho, et al.
Molecular Therapy (2022) Vol. 30, Iss. 8, pp. 2856-2867
Open Access | Times Cited: 66
Identification of a rapidly‐spreading triple mutant for high‐level metabolic insecticide resistance in Anopheles gambiae provides a real‐time molecular diagnostic for antimalarial intervention deployment
Harun Njoroge, Arjèn van’t Hof, Ambrose Oruni, et al.
Molecular Ecology (2022) Vol. 31, Iss. 16, pp. 4307-4318
Open Access | Times Cited: 47
ADH-Enhancer: an attention-based deep hybrid framework for enhancer identification and strength prediction
Faiza Mehmood, Shazia Arshad, Muhammad Shoaib
Briefings in Bioinformatics (2024) Vol. 25, Iss. 2
Open Access | Times Cited: 12

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