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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:

A Novel Modeling in Mathematical Biology for Classification of Signal Peptides
Asma Ehsan, Khalid Mahmood, Yaser Daanial Khan, et al.
Scientific Reports (2018) Vol. 8, Iss. 1
Open Access | Times Cited: 84

Showing 26-50 of 84 citing articles:

Identification of Lysine Carboxylation Sites in Proteins by Integrating Statistical Moments and Position Relative Features via General PseAAC
Saba Amanat, Adeel Ashraf, Waqar Hussain, et al.
Current Bioinformatics (2019) Vol. 15, Iss. 5, pp. 396-407
Closed Access | Times Cited: 55
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
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
iSUMOK-PseAAC: prediction of lysine sumoylation sites using statistical moments and Chou’s PseAAC
Yaser Daanial Khan, Nabeel Sabir Khan, Sheraz Naseer, et al.
PeerJ (2021) Vol. 9, pp. e11581-e11581
Open Access | Times Cited: 47
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
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
BlaPred: Predicting and classifying β-lactamase using a 3-tier prediction system via Chou's general PseAAC
Abhishikha Srivastava, Ravindra Kumar, Manish Kumar
Journal of Theoretical Biology (2018) Vol. 457, pp. 29-36
Closed Access | Times Cited: 44
Glioma stages prediction based on machine learning algorithm combined with protein-protein interaction networks
Bing Niu, Chaofeng Liang, Yi Lu, et al.
Genomics (2019) Vol. 112, Iss. 1, pp. 837-847
Open Access | Times Cited: 41
Prediction of Heart Disease using Artificial Neural Network

VFAST Transactions on Software Engineering (2018), pp. 102-112
Open Access | Times Cited: 40
Analysis and prediction of animal toxins by various Chou's pseudo components and reduced amino acid compositions
Yi Pan, Shiyuan Wang, Qi Zhang, et al.
Journal of Theoretical Biology (2018) Vol. 462, pp. 221-229
Closed Access | Times Cited: 37
PhoglyStruct: Prediction of phosphoglycerylated lysine residues using structural properties of amino acids
Abel Chandra, Alok Sharma, Abdollah Dehzangi, et al.
Scientific Reports (2018) Vol. 8, Iss. 1
Open Access | Times Cited: 36
N-MyristoylG-PseAAC: Sequence-based Prediction of N-Myristoyl Glycine Sites in Proteins by Integration of PseAAC and Statistical Moments
Sher Afzal Khan, Yaser Daanial Khan, Shakeel Ahmad, et al.
Letters in Organic Chemistry (2018) Vol. 16, Iss. 3, pp. 226-234
Closed Access | Times Cited: 36
NucPosPred: Predicting species-specific genomic nucleosome positioning via four different modes of general PseKNC
Cangzhi Jia, Qing Yang, Quan Zou
Journal of Theoretical Biology (2018) Vol. 450, pp. 15-21
Closed Access | Times Cited: 35
EvoStruct-Sub: An accurate Gram-positive protein subcellular localization predictor using evolutionary and structural features
Md Raihan Uddin, Alok Sharma, Dewan Md. Farid, et al.
Journal of Theoretical Biology (2018) Vol. 443, pp. 138-146
Open Access | Times Cited: 33
Identifying Cancer Targets Based on Machine Learning Methods via Chou’s 5-steps Rule and General Pseudo Components
Ruirui Liang, Jiayang Xie, Chi Zhang, et al.
Current Topics in Medicinal Chemistry (2019) Vol. 19, Iss. 25, pp. 2301-2317
Closed Access | Times Cited: 33
Unbiased organism-agnostic and highly sensitive signal peptide predictor with deep protein language model
Junbo Shen, Qinze Yu, Shenyang Chen, et al.
Nature Computational Science (2023) Vol. 4, Iss. 1, pp. 29-42
Closed Access | Times Cited: 10
iRSpot-DTS: Predict recombination spots by incorporating the dinucleotide-based spare-cross covariance information into Chou's pseudo components
Shengli Zhang, Kaiwen Yang, Yuqing Lei, et al.
Genomics (2018) Vol. 111, Iss. 6, pp. 1760-1770
Open Access | Times Cited: 32
Hemolytic-Pred: A machine learning-based predictor for hemolytic proteins using position and composition-based features
Gulnaz Perveen, Fahad Alturise, Tamim Alkhalifah, et al.
Digital Health (2023) Vol. 9
Open Access | Times Cited: 9
iEnhancer-RD: Identification of enhancers and their strength using RKPK features and deep neural networks
Huan Yang, Shunfang Wang, Xinnan Xia
Analytical Biochemistry (2021) Vol. 630, pp. 114318-114318
Closed Access | Times Cited: 18
C-iSUMO: A sumoylation site predictor that incorporates intrinsic characteristics of amino acid sequences
Yosvany López, Abdollah Dehzangi, Hamendra Manhar Reddy, et al.
Computational Biology and Chemistry (2020) Vol. 87, pp. 107235-107235
Closed Access | Times Cited: 15
miRNALoc: predicting miRNA subcellular localizations based on principal component scores of physico-chemical properties and pseudo compositions of di-nucleotides
Prabina Kumar Meher, Subhrajit Satpathy, A. R. Rao
Scientific Reports (2020) Vol. 10, Iss. 1
Open Access | Times Cited: 15
iPhosD-PseAAC: Identification of phosphoaspartate sites in proteins using statistical moments and PseAAC
ALAA OMRAN ALMAGRABI, Yaser Daanial Khan, Sher Afzal Khan
Biocell (2021) Vol. 45, Iss. 5, pp. 1287-1298
Open Access | Times Cited: 14

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