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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:

Construction of precise support vector machine based models for predicting promoter strength
Hailin Meng, Yingfei Ma, Guoqin Mai, et al.
Quantitative Biology (2017) Vol. 5, Iss. 1, pp. 90-98
Open Access | Times Cited: 36

Showing 1-25 of 36 citing articles:

Synthetic promoter design in Escherichia coli based on a deep generative network
Ye Wang, Haochen Wang, Lei Wei, et al.
Nucleic Acids Research (2020) Vol. 48, Iss. 12, pp. 6403-6412
Open Access | Times Cited: 144
Biosystems Design by Machine Learning
Michael Volk, Ismini Lourentzou, Shekhar Mishra, et al.
ACS Synthetic Biology (2020) Vol. 9, Iss. 7, pp. 1514-1533
Closed Access | Times Cited: 104
Machine Learning of Designed Translational Control Allows Predictive Pathway Optimization in Escherichia coli
Adrian J. Jervis, Pablo Carbonell, María Vinaixa, et al.
ACS Synthetic Biology (2018) Vol. 8, Iss. 1, pp. 127-136
Open Access | Times Cited: 96
Recent advances in machine learning applications in metabolic engineering
Pradipta Patra, Disha B.R., Pritam Kundu, et al.
Biotechnology Advances (2022) Vol. 62, pp. 108069-108069
Closed Access | Times Cited: 46
Toward improved terpenoids biosynthesis: strategies to enhance the capabilities of cell factories
Eric Fordjour, Emmanuel Osei Mensah, Yunpeng Hao, et al.
Bioresources and Bioprocessing (2022) Vol. 9, Iss. 1
Open Access | Times Cited: 38
Standardization in synthetic biology: an engineering discipline coming of age
Thomas Decoene, Brecht De Paepe, Jo Maertens, et al.
Critical Reviews in Biotechnology (2017) Vol. 38, Iss. 5, pp. 647-656
Open Access | Times Cited: 70
Precise Prediction of Promoter Strength Based on a De Novo Synthetic Promoter Library Coupled with Machine Learning
Mei Zhao, Zhenqi Yuan, Longtao Wu, et al.
ACS Synthetic Biology (2021) Vol. 11, Iss. 1, pp. 92-102
Closed Access | Times Cited: 43
Predictive design of sigma factor-specific promoters
Maarten Van Brempt, Jim Clauwaert, Friederike Mey, et al.
Nature Communications (2020) Vol. 11, Iss. 1
Open Access | Times Cited: 41
Future trends in synthetic biology in Asia
Ning Mao, Nikhil Aggarwal, Chueh Loo Poh, et al.
Advanced Genetics (2021) Vol. 2, Iss. 1
Open Access | Times Cited: 24
PromoterPredict: sequence-based modelling ofEscherichia coliσ70promoter strength yields logarithmic dependence between promoter strength and sequence
Ramit Bharanikumar, Keshav Aditya R. Premkumar, Ashok Palaniappan
PeerJ (2018) Vol. 6, pp. e5862-e5862
Open Access | Times Cited: 24
Computational design of biological circuits: putting parts into context
Eleni Karamasioti, Claude Lormeau, Jörg Stelling
Molecular Systems Design & Engineering (2017) Vol. 2, Iss. 4, pp. 410-421
Closed Access | Times Cited: 23
Improving the performance of machine learning models for biotechnology: The quest for deus ex machina
Friederike Mey, Jim Clauwaert, Kirsten Van Huffel, et al.
Biotechnology Advances (2021) Vol. 53, pp. 107858-107858
Closed Access | Times Cited: 13
Microbial cell factories for bioeconomy: From discovery to creation
Xiongying Yan, Qiaoning He, Binan Geng, et al.
BioDesign Research (2024) Vol. 6
Open Access | Times Cited: 1
Controlling protein expression by using intron-aided promoters in Saccharomyces cerevisiae
Xiaoyi Cui, Xiaoqiang Ma, Kristala L. J. Prather, et al.
Biochemical Engineering Journal (2021) Vol. 176, pp. 108197-108197
Closed Access | Times Cited: 9
Insight to Gene Expression From Promoter Libraries With the Machine Learning Workflow Exp2Ipynb
Ulf W. Liebal, Sebastian Köbbing, Linus Netze, et al.
Frontiers in Bioinformatics (2021) Vol. 1
Open Access | Times Cited: 8
Model-driven promoter strength prediction based on a fine-tuned synthetic promoter library inEscherichia coli
Mei Zhao, Shenghu Zhou, Longtao Wu, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2020)
Open Access | Times Cited: 7
Variable-Length Promoter Strength Prediction Based on Graph Convolution
Tianqi Teng, Feng Yang, Qiang Zhang, et al.
Lecture notes in computer science (2024), pp. 310-321
Closed Access
Artificial design of the genome: from sequences to the 3D structure of chromosomes
Junyi Wang, Ze‐Xiong Xie, You‐Zhi Cui, et al.
Trends in biotechnology (2024)
Closed Access
Construction of a universal biotransformation platform for producing aromatic alcohols including phenylethanol, tyrosol, hydroxytyrosol and tryptophol from corresponding aromatic amino acids
Yi Yan, Wenjing Liu, Xiaoxiang Hu, et al.
Biochemical Engineering Journal (2024) Vol. 215, pp. 109616-109616
Closed Access
Detection of minimum biomarker features via bi-level optimization framework by nested hybrid differential evolution
Kai-Cheng Hsu, Feng‐Sheng Wang
Journal of the Taiwan Institute of Chemical Engineers (2017) Vol. 81, pp. 31-39
Closed Access | Times Cited: 3
Deep learning and support vector machines for transcription start site identification
José A. Barbero‐Aparicio, Alicia Olivares‐Gil, José-Francisco Díez-Pastor, et al.
PeerJ Computer Science (2023) Vol. 9, pp. e1340-e1340
Open Access | Times Cited: 1
Peer Review #1 of "Deep learning and support vector machines for transcription start site identification (v0.1)"
Barbero-Aparicio Corresp, Alicia Olivares‐Gil, José Raúl Fernández del Castillo Díez, et al.
(2023)
Open Access
Peer Review #2 of "Deep learning and support vector machines for transcription start site identification (v0.2)"
Barbero-Aparicio Corresp, Alicia Olivares‐Gil, José Raúl Fernández del Castillo Díez, et al.
(2023)
Open Access
Peer Review #2 of "Deep learning and support vector machines for transcription start site identification (v0.1)"
Barbero-Aparicio Corresp, Alicia Olivares‐Gil, José Raúl Fernández del Castillo Díez, et al.
(2023)
Open Access
Physicochemical Properties for Promoter Classification
Lauro Moraes, Eduardo Luz, Gladston Moreira
Lecture notes in computer science (2023), pp. 368-382
Closed Access
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