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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:

iHyd-PseCp: Identify hydroxyproline and hydroxylysine in proteins by incorporating sequence-coupled effects into general PseAAC
Wang‐Ren Qiu, Bi‐Qian Sun, Xuan Xiao, et al.
Oncotarget (2016) Vol. 7, Iss. 28, pp. 44310-44321
Open Access | Times Cited: 161

Showing 51-75 of 161 citing articles:

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
Sarah Ilyas, Waqar Hussain, Adeel Ashraf, et al.
Current Genomics (2019) Vol. 20, Iss. 4, pp. 275-292
Open Access | Times Cited: 65
iSulfoTyr-PseAAC: Identify Tyrosine Sulfation Sites by Incorporating Statistical Moments via Chou’s 5-steps Rule and Pseudo Components
Omar Barukab, Yaser Daanial Khan, Sher Afzal Khan, et al.
Current Genomics (2019) Vol. 20, Iss. 4, pp. 306-320
Open Access | Times Cited: 65
Prediction of the aquatic toxicity of aromatic compounds to tetrahymena pyriformis through support vector regression
Qiang Su, Wencong Lu, Dongshu Du, et al.
Oncotarget (2017) Vol. 8, Iss. 30, pp. 49359-49369
Open Access | Times Cited: 64
Optimization of serine phosphorylation prediction in proteins by comparing human engineered features and deep representations
Sheraz Naseer, Waqar Hussain, Yaser Daanial Khan, et al.
Analytical Biochemistry (2020) Vol. 615, pp. 114069-114069
Closed Access | Times Cited: 58
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
Asma Ehsan, Muhammad Khalid Mahmood, Yaser Daanial Khan, et al.
Current Genomics (2019) Vol. 20, Iss. 2, pp. 124-133
Open Access | Times Cited: 56
Insights into Machine Learning-based Approaches for Virtual Screening in Drug Discovery: Existing Strategies and Streamlining Through FP-CADD
Waqar Hussain, Nouman Rasool, Yaser Daanial Khan
Current Drug Discovery Technologies (2020) Vol. 18, Iss. 4, pp. 463-472
Closed Access | Times Cited: 53
pLoc_bal-mEuk: Predict Subcellular Localization of Eukaryotic Proteins by General PseAAC and Quasi-balancing Training Dataset
Kuo‐Chen Chou, Xiang Cheng, Xuan Xiao
Medicinal Chemistry (2018) Vol. 15, Iss. 5, pp. 472-485
Closed Access | Times Cited: 59
pLoc_bal-mVirus: Predict Subcellular Localization of Multi-Label Virus Proteins by Chou's General PseAAC and IHTS Treatment to Balance Training Dataset
Xuan Xiao, Xiang Cheng, Gen-Qiang Chen, et al.
Medicinal Chemistry (2018) Vol. 15, Iss. 5, pp. 496-509
Closed Access | Times Cited: 58
MFSC: Multi-voting based feature selection for classification of Golgi proteins by adopting the general form of Chou's PseAAC components
Ahmad Jamal, Maqsood Hayat
Journal of Theoretical Biology (2018) Vol. 463, pp. 99-109
Closed Access | Times Cited: 56
Research progress in protein posttranslational modification site prediction
Wenying He, Leyi Wei, Quan Zou
Briefings in Functional Genomics (2018) Vol. 18, Iss. 4, pp. 220-229
Closed Access | Times Cited: 56
Identify and analysis crotonylation sites in histone by using support vector machines
Wang‐Ren Qiu, Bi‐Qian Sun, Hua Tang, et al.
Artificial Intelligence in Medicine (2017) Vol. 83, pp. 75-81
Closed Access | Times Cited: 55
S-SulfPred: A sensitive predictor to capture S-sulfenylation sites based on a resampling one-sided selection undersampling-synthetic minority oversampling technique
Cangzhi Jia, Yun Zuo
Journal of Theoretical Biology (2017) Vol. 422, pp. 84-89
Closed Access | Times Cited: 55
Sequence based predictor for discrimination of enhancer and their types by applying general form of Chou's trinucleotide composition
Muhammad Tahir, Maqsood Hayat, Muhammad Kabir
Computer Methods and Programs in Biomedicine (2017) Vol. 146, pp. 69-75
Closed Access | Times Cited: 55
Effective DNA binding protein prediction by using key features via Chou’s general PseAAC
Sheikh Adilina, Dewan Md. Farid, Swakkhar Shatabda
Journal of Theoretical Biology (2018) Vol. 460, pp. 64-78
Closed Access | Times Cited: 54
Prediction of S-Sulfenylation Sites Using Statistical Moments Based Features via CHOU’S 5-Step Rule
Ahmad Hassan Butt, Yaser Daanial Khan
International Journal of Peptide Research and Therapeutics (2019) Vol. 26, Iss. 3, pp. 1291-1301
Closed Access | Times Cited: 53
iProtease-PseAAC(2L): A two-layer predictor for identifying proteases and their types using Chou's 5-step-rule and general PseAAC
Yaser Daanial Khan, Najm Amin, Waqar Hussain, et al.
Analytical Biochemistry (2019) Vol. 588, pp. 113477-113477
Closed Access | Times Cited: 51
Sequence-based Identification of Allergen Proteins Developed by Integration of PseAAC and Statistical Moments via 5-Step Rule
Yaser Daanial Khan, Ebraheem Alzahrani, Wajdi Alghamdi, et al.
Current Bioinformatics (2020) Vol. 15, Iss. 9, pp. 1046-1055
Closed Access | Times Cited: 45
iHyd-LysSite (EPSV): Identifying Hydroxylysine Sites in Protein Using Statistical Formulation by Extracting Enhanced Position and Sequence Variant Feature Technique
Muhammad Khalid Mahmood, Asma Ehsan, Yaser Daanial Khan, et al.
Current Genomics (2020) Vol. 21, Iss. 7, pp. 536-545
Open Access | Times Cited: 44
Predicting protein submitochondrial locations by incorporating the positional-specific physicochemical properties into Chou's general pseudo-amino acid compositions
Ya-Sen Jiao, Pu-Feng Du
Journal of Theoretical Biology (2017) Vol. 416, pp. 81-87
Closed Access | Times Cited: 45
Fu-SulfPred: Identification of Protein S-sulfenylation Sites by Fusing Forests via Chou’s General PseAAC
Lidong Wang, Ruijun Zhang, Yashuang Mu
Journal of Theoretical Biology (2018) Vol. 461, pp. 51-58
Closed Access | Times Cited: 45
Detecting Succinylation sites from protein sequences using ensemble support vector machine
Ning Qiao, Xiaosa Zhao, Lingling Bao, et al.
BMC Bioinformatics (2018) Vol. 19, Iss. 1
Open Access | Times Cited: 44
Analysis and prediction of ion channel inhibitors by using feature selection and Chou's general pseudo amino acid composition
Juan Mei, Yi Fu, Ji Zhao
Journal of Theoretical Biology (2018) Vol. 456, pp. 41-48
Closed Access | Times Cited: 43
Impacts of Pseudo Amino Acid Components and 5-steps Rule to Proteomics and Proteome Analysis
Kuo‐Chen Chou
Current Topics in Medicinal Chemistry (2019) Vol. 19, Iss. 25, pp. 2283-2300
Closed Access | Times Cited: 39
Prediction of presynaptic and postsynaptic neurotoxins by combining various Chou’s pseudo components
Haiyan Huo, Tao Li, Shiyuan Wang, et al.
Scientific Reports (2017) Vol. 7, Iss. 1
Open Access | Times Cited: 37
Protein classification using modified n-grams and skip-grams
S. M. Ashiqul Islam, Benjamin J. Heil, Christopher M. Kearney, et al.
Bioinformatics (2017) Vol. 34, Iss. 9, pp. 1481-1487
Open Access | Times Cited: 34

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