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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:

Recombination spot identification Based on gapped k-mers
Rong Wang, Yong Xu, Bin Liu
Scientific Reports (2016) Vol. 6, Iss. 1
Open Access | Times Cited: 29

Showing 1-25 of 29 citing articles:

BioSeq-Analysis: a platform for DNA, RNA and protein sequence analysis based on machine learning approaches
Bin Liu
Briefings in Bioinformatics (2017) Vol. 20, Iss. 4, pp. 1280-1294
Closed Access | Times Cited: 300
Pretata: predicting TATA binding proteins with novel features and dimensionality reduction strategy
Quan Zou, Shixiang Wan, Ying Ju, et al.
BMC Systems Biology (2016) Vol. 10, Iss. S4
Open Access | Times Cited: 155
PseKRAAC: a flexible web server for generating pseudo K-tuple reduced amino acids composition
Yongchun Zuo, Yuan Li, Chen Ying-li, et al.
Bioinformatics (2016) Vol. 33, Iss. 1, pp. 122-124
Open Access | Times Cited: 134
Identification of Secretory Proteins inMycobacterium tuberculosisUsing Pseudo Amino Acid Composition
Huan Yang, Hua Tang, Xinxin Chen, et al.
BioMed Research International (2016) Vol. 2016, pp. 1-7
Open Access | Times Cited: 126
A Brief Survey of Machine Learning Methods in Protein Sub-Golgi Localization
Wuritu Yang, Xiaojuan Zhu, Jian Huang, et al.
Current Bioinformatics (2018) Vol. 14, Iss. 3, pp. 234-240
Closed Access | Times Cited: 125
Identification of Bacterial Cell Wall Lyases via Pseudo Amino Acid Composition
Xinxin Chen, Hua Tang, Wenchao Li, et al.
BioMed Research International (2016) Vol. 2016, pp. 1-8
Open Access | Times Cited: 121
A Review of DNA-binding Proteins Prediction Methods
Kaiyang Qu, Leyi Wei, Quan Zou
Current Bioinformatics (2018) Vol. 14, Iss. 3, pp. 246-254
Closed Access | Times Cited: 87
Identification of DNA-Binding Proteins by Combining Auto-Cross Covariance Transformation and Ensemble Learning
Bin Liu, Shanyi Wang, Qiwen Dong, et al.
IEEE Transactions on NanoBioscience (2016) Vol. 15, Iss. 4, pp. 328-334
Closed Access | Times Cited: 81
Prediction of G Protein-Coupled Receptors with SVM-Prot Features and Random Forest
Zhijun Liao, Ying Ju, Quan Zou
Scientifica (2016) Vol. 2016, pp. 1-10
Open Access | Times Cited: 53
iRSpot-SF: Prediction of recombination hotspots by incorporating sequence based features into Chou's Pseudo components
M. Maruf, Swakkhar Shatabda
Genomics (2018) Vol. 111, Iss. 4, pp. 966-972
Open Access | Times Cited: 50
RAANMF: An adaptive sequence feature representation method for predictions of protein thermostability, PPI, and drug-target interaction
Qian Yan, Shuyi Pan, Zhixing Cheng, et al.
Future Generation Computer Systems (2025), pp. 107819-107819
Closed Access
DNABP: Identification of DNA-Binding Proteins Based on Feature Selection Using a Random Forest and Predicting Binding Residues
Xin Ma, Jing Guo, Xiao Sun
PLoS ONE (2016) Vol. 11, Iss. 12, pp. e0167345-e0167345
Open Access | Times Cited: 45
Identification of apolipoprotein using feature selection technique
Hua Tang, Ping Zou, Chunmei Zhang, et al.
Scientific Reports (2016) Vol. 6, Iss. 1
Open Access | Times Cited: 44
BP Neural Network Could Help Improve Pre-miRNA Identification in Various Species
Limin Jiang, Jingjun Zhang, Ping Xuan, et al.
BioMed Research International (2016) Vol. 2016, pp. 1-11
Open Access | Times Cited: 42
Krein support vector machine classification of antimicrobial peptides
Joseph Redshaw, Darren Shu Jeng Ting, Alex Brown, et al.
Digital Discovery (2023) Vol. 2, Iss. 2, pp. 502-511
Open Access | Times Cited: 12
iRSpot-DACC: a computational predictor for recombination hot/cold spots identification based on dinucleotide-based auto-cross covariance
Bingquan Liu, Yumeng Liu, Xiaopeng Jin, et al.
Scientific Reports (2016) Vol. 6, Iss. 1
Open Access | Times Cited: 37
iRSpot-PDI: Identification of recombination spots by incorporating dinucleotide property diversity information into Chou's pseudo components
Lichao Zhang, Liang Kong
Genomics (2018) Vol. 111, Iss. 3, pp. 457-464
Closed Access | Times Cited: 35
In SilicoPrediction of Gamma-Aminobutyric Acid Type-A Receptors Using Novel Machine-Learning-Based SVM and GBDT Approaches
Zhijun Liao, Yong Huang, Xiaodong Yue, et al.
BioMed Research International (2016) Vol. 2016, pp. 1-12
Open Access | Times Cited: 34
Prediction of Recombination Spots Using Novel Hybrid Feature Extraction Method via Deep Learning Approach
Fatima Khan, Mukhtaj Khan, Nadeem Iqbal, et al.
Frontiers in Genetics (2020) Vol. 11
Open Access | Times Cited: 29
Accurate Identification of Cancerlectins through Hybrid Machine Learning Technology
Jieru Zhang, Ying Ju, Huijuan Lu, et al.
International Journal of Genomics (2016) Vol. 2016, pp. 1-11
Open Access | Times Cited: 28
Combining pseudo dinucleotide composition with the Z curve method to improve the accuracy of predicting DNA elements: a case study in recombination spots
Chuan Dong, Yazhou Yuan, Fa-Zhan Zhang, et al.
Molecular BioSystems (2016) Vol. 12, Iss. 9, pp. 2893-2900
Closed Access | Times Cited: 22
Enhancing the prediction of protein coding regions in biological sequence via a deep learning framework with hybrid encoding
Chao Wei, Junying Zhang, Xiguo Yuan
Digital Signal Processing (2022) Vol. 123, pp. 103430-103430
Open Access | Times Cited: 11
Identifying the Types of Ion Channel-Targeted Conotoxins by Incorporating New Properties of Residues into Pseudo Amino Acid Composition
Yun Wu, Yufei Zheng, Hua Tang
BioMed Research International (2016) Vol. 2016, pp. 1-5
Open Access | Times Cited: 15
A Computational Method for Optimizing Experimental Environments forPhellinus igniariusvia Genetic Algorithm and BP Neural Network
Zhongwei Li, Beibei Sun, Yuezhen Xin, et al.
BioMed Research International (2016) Vol. 2016, pp. 1-6
Open Access | Times Cited: 7
An efficient strategy using k- mers to analyse 16S rRNA sequences
Marcel Martínez‐Porchas, Francisco Vargas‐Albores
Heliyon (2017) Vol. 3, Iss. 7, pp. e00370-e00370
Open Access | Times Cited: 5
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