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:

A Novel Hybrid Sequence-Based Model for Identifying Anticancer Peptides
Lei Xu, Guangmin Liang, Longjie Wang, et al.
Genes (2018) Vol. 9, Iss. 3, pp. 158-158
Open Access | Times Cited: 107

Showing 1-25 of 107 citing articles:

Distance-based Support Vector Machine to Predict DNA N6- methyladenine Modification
Haoyu Zhang, Quan Zou, Ying Ju, et al.
Current Bioinformatics (2022) Vol. 17, Iss. 5, pp. 473-482
Closed Access | Times Cited: 293
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: 254
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: 215
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: 176
Application of Machine Learning in Microbiology
Kaiyang Qu, Fei Guo, Xiangrong Liu, et al.
Frontiers in Microbiology (2019) Vol. 10
Open Access | Times Cited: 169
sgRNACNN: identifying sgRNA on-target activity in four crops using ensembles of convolutional neural networks
Mengting Niu, Yuan Lin, Quan Zou
Plant Molecular Biology (2021) Vol. 105, Iss. 4-5, pp. 483-495
Closed Access | Times Cited: 103
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
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
A Random Forest Sub-Golgi Protein Classifier Optimized via Dipeptide and Amino Acid Composition Features
Zhibin Lv, Shunshan Jin, Hui Ding, et al.
Frontiers in Bioengineering and Biotechnology (2019) Vol. 7
Open Access | Times Cited: 106
An Efficient Classifier for Alzheimer’s Disease Genes Identification
Lei Xu, Guangmin Liang, Changrui Liao, et al.
Molecules (2018) Vol. 23, Iss. 12, pp. 3140-3140
Open Access | Times Cited: 102
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: 99
MRMD2.0: A Python Tool for Machine Learning with Feature Ranking and Reduction
Shida He, Fei Guo, Quan Zou, et al.
Current Bioinformatics (2020) Vol. 15, Iss. 10, pp. 1213-1221
Closed Access | Times Cited: 98
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: 96
SeqSVM: A Sequence-Based Support Vector Machine Method for Identifying Antioxidant Proteins
Lei Xu, Guangmin Liang, Shuhua Shi, et al.
International Journal of Molecular Sciences (2018) Vol. 19, Iss. 6, pp. 1773-1773
Open Access | Times Cited: 90
An in silico approach to identification, categorization and prediction of nucleic acid binding proteins
Lei Xu, Shanshan Jiang, Jin Wu, et al.
Briefings in Bioinformatics (2020) Vol. 22, Iss. 3
Open Access | Times Cited: 85
Gradient Boosting Decision Tree-Based Method for Predicting Interactions Between Target Genes and Drugs
Ping Xuan, Chang Sun, Tiangang Zhang, et al.
Frontiers in Genetics (2019) Vol. 10
Open Access | Times Cited: 80
Incorporating Distance-Based Top-n-gram and Random Forest To Identify Electron Transport Proteins
Xiaoqing Ru, Lihong Li, Quan Zou
Journal of Proteome Research (2019) Vol. 18, Iss. 7, pp. 2931-2939
Closed Access | Times Cited: 79
RF-PseU: A Random Forest Predictor for RNA Pseudouridine Sites
Zhibin Lv, Jun Zhang, Hui Ding, et al.
Frontiers in Bioengineering and Biotechnology (2020) Vol. 8
Open Access | Times Cited: 79
k-Skip-n-Gram-RF: A Random Forest Based Method for Alzheimer's Disease Protein Identification
Lei Xu, Guangmin Liang, Changrui Liao, et al.
Frontiers in Genetics (2019) Vol. 10
Open 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: 70
ACP-MHCNN: an accurate multi-headed deep-convolutional neural network to predict anticancer peptides
Sajid Ahmed, Rafsanjani Muhammod, Zahid Khan, et al.
Scientific Reports (2021) Vol. 11, Iss. 1
Open Access | Times Cited: 67
Improved prediction and characterization of anticancer activities of peptides using a novel flexible scoring card method
Phasit Charoenkwan, Wararat Chiangjong, Vannajan Sanghiran Lee, et al.
Scientific Reports (2021) Vol. 11, Iss. 1
Open Access | Times Cited: 66
cACP: Classifying anticancer peptides using discriminative intelligent model via Chou’s 5-step rules and general pseudo components
Shahid Akbar, Ateeq Ur Rahman, Maqsood Hayat, et al.
Chemometrics and Intelligent Laboratory Systems (2019) Vol. 196, pp. 103912-103912
Closed Access | Times Cited: 75
Large-scale comparative review and assessment of computational methods for anti-cancer peptide identification
Xiao Liang, Fuyi Li, Jin-Xiang Chen, et al.
Briefings in Bioinformatics (2020) Vol. 22, Iss. 4
Open Access | Times Cited: 68
Machine learning and its applications in plant molecular studies
Shanwen Sun, Chunyu Wang, Hui Ding, et al.
Briefings in Functional Genomics (2019) Vol. 19, Iss. 1, pp. 40-48
Closed Access | Times Cited: 67
Page 1 - Next Page

Scroll to top