Brown, D. ., & Owen, M. . (2020). Mean and Variance of Phylogenetic Trees. Systematic Biology, 69. https://doi.org/https://doi.org/10.1093/sysbio/syz041 (Original work published 2020)
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Mansfield, M. ., Wentz, T. ., Zhang, S. ., Lee, E. ., Dong, M. ., Sharma, S. ., & Doxey, A. . (2019). Bioinformatic discovery of a toxin family in Chryseobacterium piperi with sequence similarity to botulinum neurotoxins. Scientific Reports. 9: 1634. Retrieved from https://www.nature.com/articles/s41598-018-37647-8
Bergstrand, L. ., Neufeld, J. ., & Doxey, A. . (2019). Pygenprop: a python library for interactive programmatic exploration and comparison of organism Genome Properties. Bioinformatics, btz522. https://doi.org/https://doi.org/10.1093/bioinformatics/btz522
Mendler, K. ., Chen, H. ., Parks, D. ., Lobb, B. ., Hug, L. ., & Doxey, A. . (2019). AnnoTree: visualization and exploration of a functionally annotated microbial tree of life. Nucleic Acids Research. 47: 4442-4448. Retrieved from https://academic.oup.com/nar/article/47/9/4442/5432638
Zhang, W. ., Chen, X. ., Xin, L. ., Shan, P. ., Luo, Z. ., Li, M. ., & . (2019). ChimST: An efficient spectral library search tool for peptide identification for chimeric spectra in data-dependent acquisition. ACM/IEEE/Trans./on/Comput./Biol./And/Bioinform. Retrieved from https://www.ncbi.nlm.nih.gov/pubmed/31603795
Randhawa, G. ., Hill, K. ., Kari, L. ., & Genomics, B. . (2019). ML-DSP: Machine learning with Digital Signal Processing for ultrafast, accurate and scalable DNA sequence classification at all taxonomic levels. BMC Genomics, 20:267. Retrieved from https://cs.uwaterloo.ca/~lila/pdfs/ML-DSP.pdf
Doxey, A. ., Mansfield, M. ., & Lobb, B. . (2019). Exploring the evolution of virulence factors through bioinformatic data mining. MSystems. 4:E00162-19. Retrieved from https://msystems.asm.org/content/4/3/e00162-19
Tran, N. ., Qiao, R. ., Xin, L. ., Chen, X. ., Lui, C. ., Zhang, X. ., Shan, B. ., Ghodsi, A. ., & Li, M. . (2019). Deep learning enables de novo peptide sequencing from data-independent acquisition mass spectrometry. Nature Methods, Vol 16.
Guan, S. ., Moran, M. ., & Ma, B. . (2019). Prediction of LC-MS/MS Properties of Peptides from Sequence by Deep Learning. Molecular Cellular Proteomics. Retrieved from https://www.mcponline.org/content/early/2019/06/27/mcp.TIR119.001412
Zohora, F. ., Tran, N. ., Zhang, X. ., Xin, L. ., Shan, B. ., & Li, M. . (2019). Deeplso: a deep learning model for peptide feature detection. Nature, Scientific Report.