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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:

iMethyl-STTNC: Identification of N6-methyladenosine sites by extending the idea of SAAC into Chou's PseAAC to formulate RNA sequences
Shahid Akbar, Maqsood Hayat
Journal of Theoretical Biology (2018) Vol. 455, pp. 205-211
Closed Access | Times Cited: 140

Showing 26-50 of 140 citing articles:

iN6-methylat (5-step): identifying DNA N6-methyladenine sites in rice genome using continuous bag of nucleobases via Chou’s 5-step rule
Nguyen Quoc Khanh Le
Molecular Genetics and Genomics (2019) Vol. 294, Iss. 5, pp. 1173-1182
Closed 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
iAtbP-Hyb-EnC: Prediction of antitubercular peptides via heterogeneous feature representation and genetic algorithm based ensemble learning model
Shahid Akbar, Ashfaq Ahmad, Maqsood Hayat, et al.
Computers in Biology and Medicine (2021) Vol. 137, pp. 104778-104778
Closed Access | Times Cited: 76
AFP-CMBPred: Computational identification of antifreeze proteins by extending consensus sequences into multi-blocks evolutionary information
Farman Ali, Shahid Akbar, Ali Ghulam, et al.
Computers in Biology and Medicine (2021) Vol. 139, pp. 105006-105006
Closed Access | Times Cited: 71
iAFPs-EnC-GA: Identifying antifungal peptides using sequential and evolutionary descriptors based multi-information fusion and ensemble learning approach
Ashfaq Ahmad, Shahid Akbar, Muhammad Tahir, et al.
Chemometrics and Intelligent Laboratory Systems (2022) Vol. 222, pp. 104516-104516
Closed Access | Times Cited: 69
Evaluating machine learning methodologies for identification of cancer driver genes
Sharaf J. Malebary, Yaser Daanial Khan
Scientific Reports (2021) Vol. 11, Iss. 1
Open Access | Times Cited: 67
Prediction of Antiviral peptides using transform evolutionary & SHAP analysis based descriptors by incorporation with ensemble learning strategy
Shahid Akbar, Farman Ali, Maqsood Hayat, et al.
Chemometrics and Intelligent Laboratory Systems (2022) Vol. 230, pp. 104682-104682
Closed Access | Times Cited: 40
Identifying Neuropeptides via Evolutionary and Sequential Based Multi-Perspective Descriptors by Incorporation With Ensemble Classification Strategy
Shahid Akbar, Heba G. Mohamed, Hashim Ali, et al.
IEEE Access (2023) Vol. 11, pp. 49024-49034
Open Access | Times Cited: 32
Screening ovarian cancer by using risk factors: machine learning assists
Raoof Nopour
BioMedical Engineering OnLine (2024) Vol. 23, Iss. 1
Open Access | Times Cited: 10
pSSbond-PseAAC: Prediction of disulfide bonding sites by integration of PseAAC and statistical moments
Yaser Daanial Khan, Mehreen Jamil, Waqar Hussain, et al.
Journal of Theoretical Biology (2018) Vol. 463, pp. 47-55
Closed Access | Times Cited: 77
cACP: Classifying anticancer peptides using discriminative intelligent model via Chou’s 5-step rules and general pseudo components
Shahid Akbar, Ateeq Ur Rahman, Maqsood Hayat, et al.
Chemometrics and Intelligent Laboratory Systems (2019) Vol. 196, pp. 103912-103912
Closed Access | Times Cited: 76
RAACBook: a web server of reduced amino acid alphabet for sequence-dependent inference by using Chou’s five-step rule
Lei Zheng, Shenghui Huang, Nengjiang Mu, et al.
Database (2019) Vol. 2019
Open Access | Times Cited: 70
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
Muhammad Kabir, Saeed Ahmad, Muhammad Iqbal, et al.
Genomics (2019) Vol. 112, Iss. 1, pp. 276-285
Open Access | Times Cited: 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
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
A comprehensive comparison and analysis of computational predictors for RNA N6-methyladenosine sites of Saccharomyces cerevisiae
Xiaolei Zhu, Jingjing He, Shihao Zhao, et al.
Briefings in Functional Genomics (2019)
Closed Access | Times Cited: 60
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
A CNN-Based RNA N6-Methyladenosine Site Predictor for Multiple Species Using Heterogeneous Features Representation
Waleed Alam, Syed Danish Ali, Hilal Tayara, et al.
IEEE Access (2020) Vol. 8, pp. 138203-138209
Open Access | Times Cited: 56
iPhosS(Deep)-PseAAC: Identify Phosphoserine Sites in Proteins using Deep Learning on General Pseudo Amino Acid Compositions via Modified 5-Steps Rule
Sheraz Naseer, Waqar Hussain, Yaser Daanial Khan, et al.
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2020) Vol. 19, Iss. 3, pp. 1703-1714
Closed Access | Times Cited: 51
ProtoPred: Advancing Oncological Research Through Identification of Proto-Oncogene Proteins
Sharaf J. Malebary, Rabia Khan, Yaser Daanial Khan
IEEE Access (2021) Vol. 9, pp. 68788-68797
Open Access | Times Cited: 49
DL-m6A: Identification of N6-Methyladenosine Sites in Mammals Using Deep Learning Based on Different Encoding Schemes
Mobeen Ur Rehman, Hilal Tayara, Kil To Chong
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2022) Vol. 20, Iss. 2, pp. 904-911
Closed Access | Times Cited: 32
Prediction of Amyloid Proteins using Embedded Evolutionary & Ensemble Feature Selection based Descriptors with eXtreme Gradient Boosting Model
Shahid Akbar, Hashim Ali, Ashfaq Ahmad, et al.
IEEE Access (2023) Vol. 11, pp. 39024-39036
Open Access | Times Cited: 22
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
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

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