Articles published in Journal of Data Mining in Genomics & Proteomics have been cited by esteemed scholars and scientists all around the world. Journal of Data Mining in Genomics & Proteomics has got h-index 16, which means every article in Journal of Data Mining in Genomics & Proteomics has got 16 average citations.

Following are the list of articles that have cited the articles published in Journal of Data Mining in Genomics & Proteomics.

  2022 2021 2020 2019 2018

Year wise published articles

30 30 23 2 4

Year wise citations received

95 110 132 99 114
Journal total citations count 1039
Journal Impact Factor 2.41
Journal 5 years Impact Factor 3.5
Journal CiteScore 7.71
Journal h-index 16
Important citations

Complexity and integration. A philosophical analysis of how cancer complexity can be faced in the era of precision medicine

Mou X, Jamil HM, Rinker R (2016) Visual orchestration and autonomous execution of distributed and heterogeneous computational biology pipelines. In Bioinfo Biomed 15: 752-757.

Yazdani A, Yazdani A, Samiei A, Boerwinkle E (2016) Identification, analysis, and interpretation of a human serum metabolomics causal network in an observational study. J Biomed Informatics 63: 337-343.

Yazdani A, Yazdani A, Boerwinkle E (2016) A causal network analysis of the fatty acid metabolome in African-Americans reveals a critical role for palmitoleate and margarate. OMICS: A J Integrative Biol 20: 480-484.

Yazdani A, Yazdani A, Saniei A, Boerwinkle E (2016) A causal network analysis in an observational study identifies metabolomics pathways influencing plasma triglyceride levels. Metabolomics 12: 1-7.

Yazdani A, Yazdani A, Boerwinkle E (2016) Conceptual aspects of causal networks in an applied context. J Data Mining Geno Proteo 7: 2153-0602.

Yazdani A, Yazdani A, Samiei A, Boerwinkle E (2016) Generating a robust statistical causal structure over 13 cardiovascular disease risk factors using genomics data. J Biomed Informatics 60: 114-119.

Ch�¡vez-Galarza J, Garnery L, Henriques D, Neves CJ, Loucif-Ayad W, et al. (2016) Mitochondrial DNA variation of Apis mellifera iberiensis: further insights from a large-scale study using sequence data of the tRNAleu-cox2 intergenic region. Apidologie 1-2.

The role of MicroRNAs in defense against viral phytopathogens

Computational Identification of MicroRNAs and Their Transcript Target(s) in Field Mustard (Brassica rapa L.).

Validation and quality assessment of macromolecular structures using complex network analysis.

Heinke F, Bittrich S, Kaiser F, Labudde D (2016) SequenceCEROSENE: a computational method and web server to visualize spatial residue neighborhoods at the sequence level. BioData mining 9: 6.

Usman T, Hadlich F, Demasius W, Weikard R, K�¼hn C (2017) Unmapped reads from cattle RNAseq data: A source for missing and misassembled sequences in the reference assemblies and for detection of pathogens in the host. Genomics 109: 36-42.

Fu YB, Yang MH (2017) Genotyping-by-Sequencing and Its Application to Oat Genomic Research. Oat: Methods and Protocols 169-187.

Genomic Characterization of Lactobacillus delbrueckii TUA4408L and Evaluation of the Antiviral Activities of its Extracellular Polysaccharides in Porcine Intestinal Epithelial Cells.

A Survey on Machine Learning Techniques for Cyber Security in the Last Decade

Performance Comparison and Current Challenges of Using Machine Learning Techniques in Cybersecurity

Riccadonna S, Jurman G, Visintainer R, Filosi M, Furlanello C (2016) DTW-MIC coexpression networks from time-course data. PloS one 11: e0152648.

Poljak M (2016) Next-generation sequencing: a diagnostic one-stop shop for hepatitis C?. J Clin Microbiol. 54: 2427-2430.

Posada-Cespedes S, Seifert D, Beerenwinkel N (2016) Recent advances in inferring viral diversity from high-throughput sequencing data. Virus Research.