Year |
Citation |
Score |
2020 |
Thrash A, Hoffmann F, Perkins A. Toward a more holistic method of genome assembly assessment. Bmc Bioinformatics. 21: 249. PMID 32631298 DOI: 10.1186/S12859-020-3382-4 |
0.352 |
|
2020 |
Ranathunge C, Wheeler GL, Chimahusky ME, Perkins AD, Pramod S, Welch ME. Transcribed microsatellite allele lengths are often correlated with gene expression in natural sunflower populations. Molecular Ecology. PMID 32285554 DOI: 10.1111/Mec.15440 |
0.314 |
|
2020 |
Scott MA, Woolums AR, Swiderski CE, Perkins AD, Nanduri B, Smith DR, Karisch BB, Epperson WB, Blanton JR. Whole blood transcriptomic analysis of beef cattle at arrival identifies potential predictive molecules and mechanisms that indicate animals that naturally resist bovine respiratory disease. Plos One. 15: e0227507. PMID 31929561 DOI: 10.1371/Journal.Pone.0227507 |
0.308 |
|
2018 |
Ranathunge C, Wheeler GL, Chimahusky ME, Kennedy MM, Morrison JI, Baldwin BS, Perkins AD, Welch ME. Transcriptome profiles of sunflower reveal the potential role of microsatellites in gene expression divergence. Molecular Ecology. PMID 29419922 DOI: 10.1111/Mec.14522 |
0.301 |
|
2015 |
Tang JD, Perkins A, Williams WP, Warburton ML. Using genome-wide associations to identify metabolic pathways involved in maize aflatoxin accumulation resistance. Bmc Genomics. 16: 673. PMID 26334534 DOI: 10.1186/S12864-015-1874-9 |
0.309 |
|
2015 |
Warburton ML, Tang JD, Windham GL, Hawkins LK, Murray SC, Xu W, Boykin D, Perkins A, Williams WP. Genome-wide association mapping of aspergillus flavus and aflatoxin accumulation resistance in maize Crop Science. 55: 1857-1867. DOI: 10.2135/Cropsci2014.06.0424 |
0.374 |
|
2015 |
Rice J, Dees K, Perkins A, Thrash A. Investigating genome similarity through cross mapping percentage Bcb 2015 - 6th Acm Conference On Bioinformatics, Computational Biology, and Health Informatics. 543-544. DOI: 10.1145/2808719.2811456 |
0.312 |
|
2014 |
Pramod S, Perkins AD, Welch ME. Patterns of microsatellite evolution inferred from the Helianthus annuus (Asteraceae) transcriptome. Journal of Genetics. 93: 431-42. PMID 25189238 DOI: 10.1007/S12041-014-0402-Z |
0.339 |
|
2014 |
Asters MC, Williams WP, Perkins AD, Mylroie JE, Windham GL, Shan X. Relating significance and relations of differentially expressed genes in response to Aspergillus flavus infection in maize. Scientific Reports. 4: 4815. PMID 24770700 DOI: 10.1038/Srep04815 |
0.361 |
|
2013 |
Tang JD, Parker LA, Perkins AD, Sonstegard TS, Schroeder SG, Nicholas DD, Diehl SV. Gene expression analysis of copper tolerance and wood decay in the brown rot fungus Fibroporia radiculosa. Applied and Environmental Microbiology. 79: 1523-33. PMID 23263965 DOI: 10.1128/Aem.02916-12 |
0.304 |
|
2013 |
Aldwairi T, Nanduri B, Ramkumar M, Gautam D, Johnson M, Perkins A. Statistical methods for ambiguous sequence mappings 2013 Acm Conference On Bioinformatics, Computational Biology and Biomedical Informatics, Acm-Bcb 2013. 674-675. DOI: 10.1145/2506583.2506678 |
0.46 |
|
2012 |
Pirim H, Ekşioğlu B, Perkins A, Yüceer C. Clustering of High Throughput Gene Expression Data. Computers & Operations Research. 39: 3046-3061. PMID 23144527 DOI: 10.1016/J.Cor.2012.03.008 |
0.319 |
|
2012 |
Jay JJ, Eblen JD, Zhang Y, Benson M, Perkins AD, Saxton AM, Voy BH, Chesler EJ, Langston MA. A systematic comparison of genome-scale clustering algorithms. Bmc Bioinformatics. 13: S7. PMID 22759431 DOI: 10.1186/1471-2105-13-S10-S7 |
0.303 |
|
2012 |
Tang JD, Perkins AD, Sonstegard TS, Schroeder SG, Burgess SC, Diehl SV. Short-read sequencing for genomic analysis of the brown rot fungus Fibroporia radiculosa. Applied and Environmental Microbiology. 78: 2272-81. PMID 22247176 DOI: 10.1128/Aem.06745-11 |
0.368 |
|
2009 |
Perkins AD, Langston MA. Threshold selection in gene co-expression networks using spectral graph theory techniques. Bmc Bioinformatics. 10: S4. PMID 19811688 DOI: 10.1186/1471-2105-10-S11-S4 |
0.336 |
|
2009 |
Malone BM, Perkins AD, Bridges SM. Integrating phenotype and gene expression data for predicting gene function. Bmc Bioinformatics. 10: S20. PMID 19811686 DOI: 10.1186/1471-2105-10-S11-S20 |
0.325 |
|
2007 |
Gatti D, Maki A, Chesler EJ, Kirova R, Kosyk O, Lu L, Manly KF, Williams RW, Perkins A, Langston MA, Threadgill DW, Rusyn I. Genome-level analysis of genetic regulation of liver gene expression networks. Hepatology (Baltimore, Md.). 46: 548-57. PMID 17542012 DOI: 10.1002/Hep.21682 |
0.348 |
|
2007 |
Langston MA, Perkins AD, Saxton AM, Scharff JA, Voy BH. Innovative Computational Methods for Transcriptomic Data Analysis: A Case Study in the Use of FPT for Practical Algorithm Design and Implementation The Computer Journal. 51: 26-38. DOI: 10.1093/Comjnl/Bxm003 |
0.311 |
|
2006 |
Voy BH, Scharff JA, Perkins AD, Saxton AM, Borate B, Chesler EJ, Branstetter LK, Langston MA. Extracting gene networks for low-dose radiation using graph theoretical algorithms. Plos Computational Biology. 2: e89. PMID 16854212 DOI: 10.1371/Journal.Pcbi.0020089 |
0.314 |
|
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