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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:

Bastion3: a two-layer ensemble predictor of type III secreted effectors
Jiawei Wang, Jiahui Li, Bingjiao Yang, et al.
Bioinformatics (2018) Vol. 35, Iss. 12, pp. 2017-2028
Open Access | Times Cited: 86

Showing 1-25 of 86 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: 254
HLPpred-Fuse: improved and robust prediction of hemolytic peptide and its activity by fusing multiple feature representation
Md Mehedi Hasan, Nalini Schaduangrat, Shaherin Basith, et al.
Bioinformatics (2020) Vol. 36, Iss. 11, pp. 3350-3356
Closed Access | Times Cited: 182
mACPpred: A Support Vector Machine-Based Meta-Predictor for Identification of Anticancer Peptides
Vinothini Boopathi, Sathiyamoorthy Subramaniyam, Adeel Malik, et al.
International Journal of Molecular Sciences (2019) Vol. 20, Iss. 8, pp. 1964-1964
Open Access | Times Cited: 167
SDM6A: A Web-Based Integrative Machine-Learning Framework for Predicting 6mA Sites in the Rice Genome
Shaherin Basith, Balachandran Manavalan, Tae Hwan Shin, et al.
Molecular Therapy — Nucleic Acids (2019) Vol. 18, pp. 131-141
Open Access | Times Cited: 151
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
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
AtbPpred: A Robust Sequence-Based Prediction of Anti-Tubercular Peptides Using Extremely Randomized Trees
Balachandran Manavalan, Shaherin Basith, Tae Hwan Shin, et al.
Computational and Structural Biotechnology Journal (2019) Vol. 17, pp. 972-981
Open Access | Times Cited: 94
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
4mCpred-EL: An Ensemble Learning Framework for Identification of DNA N4-Methylcytosine Sites in the Mouse Genome
Balachandran Manavalan, Shaherin Basith, Tae Hwan Shin, et al.
Cells (2019) Vol. 8, Iss. 11, pp. 1332-1332
Open Access | Times Cited: 91
STALLION: a stacking-based ensemble learning framework for prokaryotic lysine acetylation site prediction
Shaherin Basith, Gwang Lee, Balachandran Manavalan
Briefings in Bioinformatics (2021) Vol. 23, Iss. 1
Open Access | Times Cited: 72
H2Opred: a robust and efficient hybrid deep learning model for predicting 2’-O-methylation sites in human RNA
Nhat Truong Pham, Rajan Rakkiyapan, Jongsun Park, et al.
Briefings in Bioinformatics (2023) Vol. 25, Iss. 1
Open Access | Times Cited: 24
Prediction of Amyloid Proteins using Embedded Evolutionary & Ensemble Feature Selection based Descriptors with eXtreme Gradient Boosting Model
Shahid Akbar, Hashim Ali, Ashfaq Ahmad, et al.
IEEE Access (2023) Vol. 11, pp. 39024-39036
Open Access | Times Cited: 22
iNR-2L: A two-level sequence-based predictor developed via Chou's 5-steps rule and general PseAAC for identifying nuclear receptors and their families
Muhammad Kabir, Saeed Ahmad, Muhammad Iqbal, et al.
Genomics (2019) Vol. 112, Iss. 1, pp. 276-285
Open Access | Times Cited: 66
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
DeepVF: a deep learning-based hybrid framework for identifying virulence factors using the stacking strategy
Ruopeng Xie, Jiahui Li, Jiawei Wang, et al.
Briefings in Bioinformatics (2020) Vol. 22, Iss. 3
Open Access | Times Cited: 60
iHyd-PseAAC (EPSV): Identifying Hydroxylation Sites in Proteins by Extracting Enhanced Position and Sequence Variant Feature via Chou's 5- Step Rule and General Pseudo Amino Acid Composition
Asma Ehsan, Muhammad Khalid Mahmood, Yaser Daanial Khan, et al.
Current Genomics (2019) Vol. 20, Iss. 2, pp. 124-133
Open Access | Times Cited: 56
Integrative machine learning framework for the identification of cell-specific enhancers from the human genome
Shaherin Basith, Md Mehedi Hasan, Gwang Lee, et al.
Briefings in Bioinformatics (2021) Vol. 22, Iss. 6
Closed Access | Times Cited: 54
PaCRISPR: a server for predicting and visualizing anti-CRISPR proteins
Jiawei Wang, Wei Dai, Jiahui Li, et al.
Nucleic Acids Research (2020) Vol. 48, Iss. W1, pp. W348-W357
Open Access | Times Cited: 52
iPhosS(Deep)-PseAAC: Identify Phosphoserine Sites in Proteins using Deep Learning on General Pseudo Amino Acid Compositions via Modified 5-Steps Rule
Sheraz Naseer, Waqar Hussain, Yaser Daanial Khan, et al.
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2020) Vol. 19, Iss. 3, pp. 1703-1714
Closed Access | Times Cited: 51
PeNGaRoo, a combined gradient boosting and ensemble learning framework for predicting non-classical secreted proteins
Yanju Zhang, Sha Yu, Ruopeng Xie, et al.
Bioinformatics (2019) Vol. 36, Iss. 3, pp. 704-712
Closed Access | Times Cited: 46
BastionHub: a universal platform for integrating and analyzing substrates secreted by Gram-negative bacteria
Jiawei Wang, Jiahui Li, Yi Hou, et al.
Nucleic Acids Research (2020) Vol. 49, Iss. D1, pp. D651-D659
Open Access | Times Cited: 46
Empirical Comparison and Analysis of Web-Based DNA N4-Methylcytosine Site Prediction Tools
Balachandran Manavalan, Md Mehedi Hasan, Shaherin Basith, et al.
Molecular Therapy — Nucleic Acids (2020) Vol. 22, pp. 406-420
Open Access | Times Cited: 44
Effectidor: an automated machine-learning-based web server for the prediction of type-III secretion system effectors
Naama Wagner, Oren Avram, Dafna Gold-Binshtok, et al.
Bioinformatics (2022) Vol. 38, Iss. 8, pp. 2341-2343
Closed Access | Times Cited: 27
BBPpredict: A Web Service for Identifying Blood-Brain Barrier Penetrating Peptides
Xue Chen, Qianyue Zhang, Bowen Li, et al.
Frontiers in Genetics (2022) Vol. 13
Open Access | Times Cited: 23
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