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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:

iRecSpot-EF: Effective sequence based features for recombination hotspot prediction
Rafsanjani Muhammod, Md Toha Khan Mozlish, Sajid Ahmed, et al.
Computers in Biology and Medicine (2018) Vol. 103, pp. 17-23
Closed Access | Times Cited: 21

Showing 21 citing articles:

PyFeat: a Python-based effective feature generation tool for DNA, RNA and protein sequences
Rafsanjani Muhammod, Sajid Ahmed, Dewan Md. Farid, et al.
Bioinformatics (2019) Vol. 35, Iss. 19, pp. 3831-3833
Open Access | Times Cited: 104
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: 68
iRSpot-SPI: Deep learning-based recombination spots prediction by incorporating secondary sequence information coupled with physio-chemical properties via Chou's 5-step rule and pseudo components
Zaheer Ullah Khan, Farman Ali, Izhar Ahmed Khan, et al.
Chemometrics and Intelligent Laboratory Systems (2019) Vol. 189, pp. 169-180
Closed Access | Times Cited: 67
Locate-R: Subcellular localization of long non-coding RNAs using nucleotide compositions
Ahsan Ahmad, Hao Lin, Swakkhar Shatabda
Genomics (2020) Vol. 112, Iss. 3, pp. 2583-2589
Open Access | Times Cited: 48
iRSpot-DTS: Predict recombination spots by incorporating the dinucleotide-based spare-cross covariance information into Chou's pseudo components
Shengli Zhang, Kaiwen Yang, Yuqing Lei, et al.
Genomics (2018) Vol. 111, Iss. 6, pp. 1760-1770
Open Access | Times Cited: 32
Multi-feature fusion for deep learning to predict plant lncRNA-protein interaction
Jael Sanyanda Wekesa, Jun Meng, Yushi Luan
Genomics (2020) Vol. 112, Iss. 5, pp. 2928-2936
Open Access | Times Cited: 31
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
Sentiment Analysis of Emirati Dialect
Arwa A. Al Shamsi, Sherief Abdallah
Big Data and Cognitive Computing (2022) Vol. 6, Iss. 2, pp. 57-57
Open Access | Times Cited: 17
A novel lncRNA–protein interaction prediction method based on deep forest with cascade forest structure
Xiongfei Tian, Ling Shen, Zhenwu Wang, et al.
Scientific Reports (2021) Vol. 11, Iss. 1
Open Access | Times Cited: 22
The prediction of Recombination Hotspot Based on Automated Machine Learning
Dong-Xin Ye, Jun-Wen Yu, Rui Li, et al.
Journal of Molecular Biology (2024), pp. 168653-168653
Closed Access | Times Cited: 2
Automated feature engineering improves prediction of protein–protein interactions
Neven Šumonja, Branislava Gemović, Nevena Veljković, et al.
Amino Acids (2019) Vol. 51, Iss. 8, pp. 1187-1200
Closed Access | Times Cited: 20
CluSem: Accurate clustering-based ensemble method to predict motor imagery tasks from multi-channel EEG data
Md. Ochiuddin Miah, Rafsanjani Muhammod, Khondaker A. Mamun, et al.
Journal of Neuroscience Methods (2021) Vol. 364, pp. 109373-109373
Open Access | Times Cited: 13
RNAI-FRID: novel feature representation method with information enhancement and dimension reduction for RNA–RNA interaction
Qiang Kang, Jun Meng, Yushi Luan
Briefings in Bioinformatics (2022) Vol. 23, Iss. 3
Closed Access | Times Cited: 7
SubFeat: Feature subspacing ensemble classifier for function prediction of DNA, RNA and protein sequences
H. M. Fazlul Haque, Rafsanjani Muhammod, Fariha Arifin, et al.
Computational Biology and Chemistry (2021) Vol. 92, pp. 107489-107489
Open Access | Times Cited: 6
DeepSSPred: A Deep Learning Based Sulfenylation Site Predictor Via a Novel nSegmented Optimize Federated Feature Encoder
Zaheer Ullah Khan, Dechang Pi
Protein and Peptide Letters (2020) Vol. 28, Iss. 6, pp. 708-721
Closed Access | Times Cited: 5
X-Ray Image Enhancer
Mrs. K. Sowndharya, R. Arun, N. Deepak, et al.
International Journal of Advanced Research in Science Communication and Technology (2024), pp. 279-284
Open Access
SubFeat: Feature Subspacing Ensemble Classifier for Function Prediction of DNA, RNA and Protein Sequences
H. M. Fazlul Haque, Fariha Arifin, Sheikh Adilina, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2020)
Open Access | Times Cited: 1
DeepEnzyPred: A Bi-Layered Deep Learning Framework for prediction of Bacteriophage Enzymes and their Sub-Hydrolases Enzymes via Novel Multi Level- Multi Thresholds Feature Selection technique
Yu Wang, Zaheer Ullah Khan, Shaukat Ali, et al.
Research Square (Research Square) (2020)
Open Access | Times Cited: 1
Epigenetic Marks and Variation of Sequence-Based Information Along Genomic Regions Are Predictive of Recombination Hot/Cold Spots in Saccharomyces cerevisiae
Guoqing Liu, Shuangjian Song, Qiguo Zhang, et al.
Frontiers in Genetics (2021) Vol. 12
Open Access | Times Cited: 1
Prediction model for IcRNA aubcellular localization using machine learning
Zareen Kalim, Amna Arshad
2021 International Conference on Innovative Computing (ICIC) (2021), pp. 1-10
Closed Access | Times Cited: 1
CluSem: Accurate Clustering-based Ensemble Method to Predict Motor Imagery Tasks from Multi-channel EEG Data
Md Ochiuddin Miah, Rafsanjani Muhammod, Khondaker A. Mamun, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2021)
Open Access
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