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:

Gene coexpression networks reveal key drivers of phenotypic divergence in porcine muscle
Xiao Zhao, Zhaoyang Liu, Qingxin Liu
BMC Genomics (2015) Vol. 16, Iss. 1
Open Access | Times Cited: 13

Showing 13 citing articles:

Spatiotemporal Transcriptome Analysis Provides Insights into Bicolor Tepal Development in Lilium “Tiny Padhye”
Leifeng Xu, Panpan Yang, Yayan Feng, et al.
Frontiers in Plant Science (2017) Vol. 8
Open Access | Times Cited: 50

Transcriptomics and targeted metabolomics profilings for elucidation of pigmentation in Lonicera japonica flowers at different developmental stages
Qiang Xue, Hang Fan, Yao Fan, et al.
Industrial Crops and Products (2019) Vol. 145, pp. 111981-111981
Closed Access | Times Cited: 46

Systematic review of genetic variants associated with cognitive impairment and depressive symptoms in Parkinson’s disease
Tyrra D’Souza, Anto P. Rajkumar
Acta Neuropsychiatrica (2019) Vol. 32, Iss. 1, pp. 10-22
Open Access | Times Cited: 36

Integrative Analysis of Metabolomic, Proteomic and Genomic Data to Reveal Functional Pathways and Candidate Genes for Drip Loss in Pigs
Julia Welzenbach, Christiane Neuhoff, Hanna Heidt, et al.
International Journal of Molecular Sciences (2016) Vol. 17, Iss. 9, pp. 1426-1426
Open Access | Times Cited: 35

Transcriptomic profiling in muscle and adipose tissue identifies genes related to growth and lipid deposition
Xuan Tao, Yan Liang, Xuemei Yang, et al.
PLoS ONE (2017) Vol. 12, Iss. 9, pp. e0184120-e0184120
Open Access | Times Cited: 31

Using Machine Learning to Measure Relatedness Between Genes: A Multi-Features Model
Yan Wang, Sen Yang, Jing Zhao, et al.
Scientific Reports (2019) Vol. 9, Iss. 1
Open Access | Times Cited: 26

Whole-genome sequence-based association analyses on an eight-breed crossed heterogeneous stock of pigs reveal the genetic basis of skeletal muscle fiber characteristics
Yizhong Huang, Liping Cai, Yanyu Duan, et al.
Meat Science (2022) Vol. 194, pp. 108974-108974
Closed Access | Times Cited: 7

eTumorMetastasis: A Network-Based Algorithm Predicts Clinical Outcomes Using Whole-Exome Sequencing Data of Cancer Patients
Jean‐Sébastien Milanese, Chabane Tibiche, Naif Zaman, et al.
Genomics Proteomics & Bioinformatics (2021) Vol. 19, Iss. 6, pp. 973-985
Open Access | Times Cited: 8

Genome-Wide Expression Profiling of mRNAs, lncRNAs and circRNAs in Skeletal Muscle of Two Different Pig Breeds
Xinhua Hou, Ligang Wang, Fuping Zhao, et al.
Animals (2021) Vol. 11, Iss. 11, pp. 3169-3169
Open Access | Times Cited: 8

Genome-wide association study for primal cut lean traits in Canadian beef cattle
Vipasha Sood, Argenis Rodas‐González, Tiago S. Valente, et al.
Meat Science (2023) Vol. 204, pp. 109274-109274
Closed Access | Times Cited: 3

Transcriptomic analysis of genes: expression and regulation
Maria Augusta Crivelente Horta, Ricardo José Gonzaga Pimenta, Déborah Aires Almeida, et al.
Elsevier eBooks (2022), pp. 1-41
Closed Access | Times Cited: 4

P5012 Integrative analysis of metabolomic, proteomic and genomic data to reveal functional pathways and candidate genes for drip loss in pigs
Julia Welzenbach, Christine Große‐Brinkhaus, Christiane Neuhoff, et al.
Journal of Animal Science (2016) Vol. 94, Iss. suppl_4, pp. 121-121
Closed Access | Times Cited: 1

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