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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:

iACP-GAEnsC: Evolutionary genetic algorithm based ensemble classification of anticancer peptides by utilizing hybrid feature space
Shahid Akbar, Maqsood Hayat, Muhammad Iqbal, et al.
Artificial Intelligence in Medicine (2017) Vol. 79, pp. 62-70
Closed Access | Times Cited: 152

Showing 1-25 of 152 citing articles:

Machine intelligence in peptide therapeutics: A next‐generation tool for rapid disease screening
Shaherin Basith, Balachandran Manavalan, Tae Hwan Shin, et al.
Medicinal Research Reviews (2020) Vol. 40, Iss. 4, pp. 1276-1314
Closed Access | Times Cited: 256
AntiCP 2.0: an updated model for predicting anticancer peptides
Piyush Agrawal, Dhruv Bhagat, Manish Mahalwal, et al.
Briefings in Bioinformatics (2020) Vol. 22, Iss. 3
Open Access | Times Cited: 223
ACPred: A Computational Tool for the Prediction and Analysis of Anticancer Peptides
Nalini Schaduangrat, Chanin Nantasenamat, Virapong Prachayasittikul, et al.
Molecules (2019) Vol. 24, Iss. 10, pp. 1973-1973
Open Access | Times Cited: 182
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: 96
Schizophrenia classification using machine learning on resting state EEG signal
Juan Ruiz de Miras, Antonio J. Ibáñez‐Molina, María Felipa Soriano, et al.
Biomedical Signal Processing and Control (2022) Vol. 79, pp. 104233-104233
Open Access | Times Cited: 76
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: 75
Optimizing classification of diseases through language model analysis of symptoms
Esraa Hassan, Tarek Abd El‐Hafeez, Mahmoud Y. Shams
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 65
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: 56
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: 54
Prediction of protein solubility based on sequence physicochemical patterns and distributed representation information with DeepSoluE
Chao Wang, Quan Zou
BMC Biology (2023) Vol. 21, Iss. 1
Open Access | Times Cited: 51
Anti-Cancer Peptides: Status and Future Prospects
Gehane Ghaly, Hatem Tallima, Eslam Dabbish, et al.
Molecules (2023) Vol. 28, Iss. 3, pp. 1148-1148
Open Access | Times Cited: 47
ToxinPred 3.0: An improved method for predicting the toxicity of peptides
Anand Singh Rathore, Shubham Choudhury, Akanksha Arora, et al.
Computers in Biology and Medicine (2024) Vol. 179, pp. 108926-108926
Open Access | Times Cited: 45
Optimizing Gene Selection and Cancer Classification with Hybrid Sine Cosine and Cuckoo Search Algorithm
Abrar Yaqoob, Navneet Kumar Verma, Rabia Musheer Aziz
Journal of Medical Systems (2024) Vol. 48, Iss. 1
Closed Access | Times Cited: 40
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: 8
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
Meta-iAVP: A Sequence-Based Meta-Predictor for Improving the Prediction of Antiviral Peptides Using Effective Feature Representation
Nalini Schaduangrat, Chanin Nantasenamat, Virapong Prachayasittikul, et al.
International Journal of Molecular Sciences (2019) Vol. 20, Iss. 22, pp. 5743-5743
Open Access | Times Cited: 111
Unraveling the bioactivity of anticancer peptides as deduced from machine learning.
Watshara Shoombuatong, Nalini Schaduangrat, Chanin Nantasenamat
PubMed (2018) Vol. 17, pp. 734-752
Closed Access | Times Cited: 100
PTPD: predicting therapeutic peptides by deep learning and word2vec
Chuanyan Wu, Rui Gao, Yusen Zhang, et al.
BMC Bioinformatics (2019) Vol. 20, Iss. 1
Open Access | Times Cited: 99
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
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
Computational identification of N6-methyladenosine sites in multiple tissues of mammals
Fanny Dao, Hao Lv, Yuhe R. Yang, et al.
Computational and Structural Biotechnology Journal (2020) Vol. 18, pp. 1084-1091
Open Access | Times Cited: 84
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
DeepACP: A Novel Computational Approach for Accurate Identification of Anticancer Peptides by Deep Learning Algorithm
Lezheng Yu, Runyu Jing, Fengjuan Liu, et al.
Molecular Therapy — Nucleic Acids (2020) Vol. 22, pp. 862-870
Open Access | Times Cited: 72
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
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