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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 1-25 of 140 citing articles:

LightGBM-PPI: Predicting protein-protein interactions through LightGBM with multi-information fusion
Cheng Chen, Qingmei Zhang, Qin Ma, et al.
Chemometrics and Intelligent Laboratory Systems (2019) Vol. 191, pp. 54-64
Open Access | Times Cited: 230
WHISTLE: a high-accuracy map of the human N6-methyladenosine (m6A) epitranscriptome predicted using a machine learning approach
Kunqi Chen, Zhen Wei, Qing Zhang, et al.
Nucleic Acids Research (2019) Vol. 47, Iss. 7, pp. e41-e41
Open Access | Times Cited: 197
iRNA(m6A)-PseDNC: Identifying N6-methyladenosine sites using pseudo dinucleotide composition
Wei Chen, Hui Ding, Xu Zhou, et al.
Analytical Biochemistry (2018) Vol. 561-562, pp. 59-65
Closed Access | Times Cited: 186
cACP-DeepGram: Classification of anticancer peptides via deep neural network and skip-gram-based word embedding model
Shahid Akbar, Maqsood Hayat, Muhammad Tahir, et al.
Artificial Intelligence in Medicine (2022) Vol. 131, pp. 102349-102349
Closed Access | Times Cited: 94
AIPs-SnTCN: Predicting Anti-Inflammatory Peptides Using fastText and Transformer Encoder-Based Hybrid Word Embedding with Self-Normalized Temporal Convolutional Networks
Ali Raza, Jamal Uddin, Abdullah Almuhaimeed, et al.
Journal of Chemical Information and Modeling (2023) Vol. 63, Iss. 21, pp. 6537-6554
Closed Access | Times Cited: 73
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: 68
pAtbP-EnC: Identifying Anti-Tubercular Peptides Using Multi-Feature Representation and Genetic Algorithm-Based Deep Ensemble Model
Shahid Akbar, Ali Raza, Tamara Al Shloul, et al.
IEEE Access (2023) Vol. 11, pp. 137099-137114
Open Access | Times Cited: 53
Deepstacked-AVPs: predicting antiviral peptides using tri-segment evolutionary profile and word embedding based multi-perspective features with deep stacking model
Shahid Akbar, Ali Raza, Quan Zou
BMC Bioinformatics (2024) Vol. 25, Iss. 1
Open Access | Times Cited: 53
iAFPs-Mv-BiTCN: Predicting antifungal peptides using self-attention transformer embedding and transform evolutionary based multi-view features with bidirectional temporal convolutional networks
Shahid Akbar, Quan Zou, Ali Raza, et al.
Artificial Intelligence in Medicine (2024) Vol. 151, pp. 102860-102860
Closed Access | Times Cited: 50
StackedEnC-AOP: prediction of antioxidant proteins using transform evolutionary and sequential features based multi-scale vector with stacked ensemble learning
Gul Rukh, Shahid Akbar, Gauhar Rehman, et al.
BMC Bioinformatics (2024) Vol. 25, Iss. 1
Open Access | Times Cited: 25
pACP-HybDeep: predicting anticancer peptides using binary tree growth based transformer and structural feature encoding with deep-hybrid learning
Muhammad Khalil Shahid, Maqsood Hayat, Wajdi Alghamdi, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access | Times Cited: 3
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
SPalmitoylC-PseAAC: A sequence-based model developed via Chou's 5-steps rule and general PseAAC for identifying S-palmitoylation sites in proteins
Waqar Hussain, Yaser Daanial Khan, Nouman Rasool, et al.
Analytical Biochemistry (2018) Vol. 568, pp. 14-23
Closed Access | Times Cited: 122
Comprehensive review and assessment of computational methods for predicting RNA post-transcriptional modification sites from RNA sequences
Zhen Chen, Pei Zhao, Fuyi Li, et al.
Briefings in Bioinformatics (2019) Vol. 21, Iss. 5, pp. 1676-1696
Closed Access | Times Cited: 121
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
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
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
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
iHBP-DeepPSSM: Identifying hormone binding proteins using PsePSSM based evolutionary features and deep learning approach
Shahid Akbar, Salman Khan, Farman Ali, et al.
Chemometrics and Intelligent Laboratory Systems (2020) Vol. 204, pp. 104103-104103
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: 87
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
Deep-AntiFP: Prediction of antifungal peptides using distanct multi-informative features incorporating with deep neural networks
Ashfaq Ahmad, Shahid Akbar, Salman Khan, et al.
Chemometrics and Intelligent Laboratory Systems (2020) Vol. 208, pp. 104214-104214
Closed Access | Times Cited: 85
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
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