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:

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

Showing 1-25 of 36 citing articles:

SPrenylC-PseAAC: A sequence-based model developed via Chou's 5-steps rule and general PseAAC for identifying S-prenylation sites in proteins
Waqar Hussain, Yaser Daanial Khan, Nouman Rasool, et al.
Journal of Theoretical Biology (2019) Vol. 468, pp. 1-11
Closed Access | Times Cited: 136
iN6-Methyl (5-step): Identifying RNA N6-methyladenosine sites using deep learning mode via Chou's 5-step rules and Chou's general PseKNC
Iman Nazari, Muhammad Tahir, Hilal Tayara, et al.
Chemometrics and Intelligent Laboratory Systems (2019) Vol. 193, pp. 103811-103811
Closed Access | Times Cited: 97
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
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: 82
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
Prediction of drug-target interaction based on protein features using undersampling and feature selection techniques with boosting
Saifuddin Mahmud, Wenyu Chen, Meng Han, et al.
Analytical Biochemistry (2019) Vol. 589, pp. 113507-113507
Closed Access | Times Cited: 71
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: 68
Deep learning for mining protein data
Qiang Shi, Weiya Chen, Siqi Huang, et al.
Briefings in Bioinformatics (2019) Vol. 22, Iss. 1, pp. 194-218
Closed Access | Times Cited: 67
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
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: 64
Using CHOU'S 5-Steps Rule to Predict O-Linked Serine Glycosylation Sites by Blending Position Relative Features and Statistical Moment
Muhammad Aizaz Akmal, Waqar Hussain, Nouman Rasool, et al.
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2020) Vol. 18, Iss. 5, pp. 2045-2056
Closed Access | Times Cited: 44
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
PepCNN deep learning tool for predicting peptide binding residues in proteins using sequence, structural, and language model features
Abel Chandra, Alok Sharma, Abdollah Dehzangi, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 12
iPHLoc-ES: Identification of bacteriophage protein locations using evolutionary and structural features
Swakkhar Shatabda, Sanjay Saha, Alok Sharma, et al.
Journal of Theoretical Biology (2017) Vol. 435, pp. 229-237
Closed Access | Times Cited: 32
Prediction of Lysine Malonylation Sites Based on Pseudo Amino Acid
Qilin Xiang, Kai‐Yan Feng, Bo Liao, et al.
Combinatorial Chemistry & High Throughput Screening (2017) Vol. 20, Iss. 7
Closed Access | Times Cited: 32
Proposing Pseudo Amino Acid Components is an Important Milestone for Proteome and Genome Analyses
Kuo‐Chen Chou
International Journal of Peptide Research and Therapeutics (2019) Vol. 26, Iss. 2, pp. 1085-1098
Closed Access | Times Cited: 29
Accurately Predicting Glutarylation Sites Using Sequential Bi-Peptide-Based Evolutionary Features
Md. Easin Arafat, Md. Wakil Ahmad, S.M. Shovan, et al.
Genes (2020) Vol. 11, Iss. 9, pp. 1023-1023
Open Access | Times Cited: 24
EvolStruct-Phogly: incorporating structural properties and evolutionary information from profile bigrams for the phosphoglycerylation prediction
Abel Chandra, Alok Sharma, Abdollah Dehzangi, et al.
BMC Genomics (2019) Vol. 19, Iss. S9
Open Access | Times Cited: 24
RF-MaloSite and DL-Malosite: Methods based on random forest and deep learning to identify malonylation sites
Hussam J. AL-barakati, Niraj Thapa, Hiroto Saigo, et al.
Computational and Structural Biotechnology Journal (2020) Vol. 18, pp. 852-860
Open Access | Times Cited: 21
Protein feature engineering framework for AMPylation site prediction
Hardik Prabhu, Hrushikesh Bhosale, Aamod Sane, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 2
Mal-Light: Enhancing Lysine Malonylation Sites Prediction Problem Using Evolutionary-based Features
Md. Wakil Ahmad, Md. Easin Arafat, Ghazaleh Taherzadeh, et al.
IEEE Access (2020) Vol. 8, pp. 77888-77902
Open Access | Times Cited: 19
MDCAN-Lys: A Model for Predicting Succinylation Sites Based on Multilane Dense Convolutional Attention Network
Huiqing Wang, Hong Sheng Zhao, Zhiliang Yan, et al.
Biomolecules (2021) Vol. 11, Iss. 6, pp. 872-872
Open Access | Times Cited: 16
Bigram-PGK: phosphoglycerylation prediction using the technique of bigram probabilities of position specific scoring matrix
Abel Chandra, Alok Sharma, Abdollah Dehzangi, et al.
BMC Molecular and Cell Biology (2019) Vol. 20, Iss. S2
Open Access | Times Cited: 19
Prediction of post-translational modification sites using multiple kernel support vector machine
BingHua Wang, Minghui Wang, Ao Li
PeerJ (2017) Vol. 5, pp. e3261-e3261
Open Access | Times Cited: 17
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: 14
Page 1 - Next Page

Scroll to top