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:

Identification of stress response proteins through fusion of machine learning models and statistical paradigms
Ebraheem Alzahrani, Wajdi Alghamdi, Malik Zaka Ullah, et al.
Scientific Reports (2021) Vol. 11, Iss. 1
Open Access | Times Cited: 14

Showing 14 citing articles:

Signal Peptide Efficiency: From High-Throughput Data to Prediction and Explanation
Stefano Grasso, Valentina Dabene, Margriet M. W. B. Hendriks, et al.
ACS Synthetic Biology (2023) Vol. 12, Iss. 2, pp. 390-404
Open Access | Times Cited: 28

LBCEPred: a machine learning model to predict linear B-cell epitopes
Wajdi Alghamdi, Muhammad Attique, Ebraheem Alzahrani, et al.
Briefings in Bioinformatics (2022) Vol. 23, Iss. 3
Closed Access | Times Cited: 23

MPMABP: A CNN and Bi-LSTM-Based Method for Predicting Multi-Activities of Bioactive Peptides
You Li, Xueyong Li, Yuewu Liu, et al.
Pharmaceuticals (2022) Vol. 15, Iss. 6, pp. 707-707
Open Access | Times Cited: 23

PADG‐Pred: Exploring Ensemble Approaches for Identifying Parkinson's Disease Associated Biomarkers Using Genomic Sequences Analysis
Ayesha Karim, Tamim Alkhalifah, Fahad Alturise, et al.
IET Systems Biology (2025) Vol. 19, Iss. 1
Open Access

TNFR‐LSTM: A Deep Intelligent Model for Identification of Tumour Necroses Factor Receptor (TNFR) Activity
Faisal Binzagr, Ansar Naseem, Muhammad Umer Farooq, et al.
IET Systems Biology (2025) Vol. 19, Iss. 1
Open Access

ORI-Deep: improving the accuracy for predicting origin of replication sites by using a blend of features and long short-term memory network
Mahwish Shahid, M. Ilyas, Waqar Hussain, et al.
Briefings in Bioinformatics (2022) Vol. 23, Iss. 2
Closed Access | Times Cited: 16

IGPred‐HDnet: Prediction of Immunoglobulin Proteins Using Graphical Features and the Hierarchal Deep Learning‐Based Approach
Ali Zakir, Fahad Alturise, Tamim Alkhalifah, et al.
Computational Intelligence and Neuroscience (2023) Vol. 2023, Iss. 1
Open Access | Times Cited: 8

DeepDBS: Identification of DNA-binding sites in protein sequences by using deep representations and random forest
Yaser Daanial Khan, Tamim Alkhalifah, Fahad Alturise, et al.
Methods (2024) Vol. 231, pp. 26-36
Closed Access | Times Cited: 2

m1A-pred: Prediction of Modified 1-methyladenosine Sites in RNA Sequences through Artificial Intelligence
Muhammad Taseer Suleman, Yaser Daanial Khan
Combinatorial Chemistry & High Throughput Screening (2022) Vol. 25, Iss. 14, pp. 2473-2484
Closed Access | Times Cited: 9

RCCC_Pred: A Novel Method for Sequence-Based Identification of Renal Clear Cell Carcinoma Genes through DNA Mutations and a Blend of Features
Arfa Hassan, Tamim Alkhalifah, Fahad Alturise, et al.
Diagnostics (2022) Vol. 12, Iss. 12, pp. 3036-3036
Open Access | Times Cited: 9

DeepBCE: Evaluation of deep learning models for identification of immunogenic B-cell epitopes
Muhammad Attique, Tamim Alkhalifah, Fahad Alturise, et al.
Computational Biology and Chemistry (2023) Vol. 104, pp. 107874-107874
Open Access | Times Cited: 5

ESM-BBB-Pred: a fine-tuned ESM 2.0 and deep neural networks for the identification of blood–brain barrier peptides
Ansar Naseem, Fahad Alturise, Tamim Alkhalifah, et al.
Briefings in Bioinformatics (2024) Vol. 26, Iss. 1
Open Access

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