Lenwood S. Heath
Affiliations: | Virginia Polytechnic Institute and State University, Blacksburg, VA, United States |
Area:
Computer Science, MathematicsGoogle:
"Lenwood Heath"Parents
Sign in to add mentor| Arnold L. Rosenberg | grad student | 1985 | UNC Chapel Hill | |
| (Algorithms for Embedding Graphs in Books) | ||||
Children
Sign in to add trainee| Craig A. Struble | grad student | 2000 | Virginia Tech |
| Allan A. Sioson | grad student | 2005 | Virginia Tech |
| Douglas J. Slotta | grad student | 2005 | Virginia Tech |
| Amrita Pati | grad student | 2008 | Virginia Tech |
| Nahla A. Belal | grad student | 2011 | Virginia Tech |
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Publications
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| Belal NA, Heath LS. (2023) A complete theoretical framework for inferring horizontal gene transfers using partial order sets. Plos One. 18: e0281824 |
| Arango-Argoty GA, Guron GKP, Garner E, et al. (2020) ARGminer: A web platform for crowdsourcing based curation of antibiotic resistance genes. Bioinformatics (Oxford, England) |
| Lee J, Heath LS, Grene R, et al. (2019) Comparing time series transcriptome data between plants using a network module finding algorithm. Plant Methods. 15: 61 |
| Yang Y, Robertson JA, Guo Z, et al. (2018) MCAT: Motif Combining and Association Tool. Journal of Computational Biology : a Journal of Computational Molecular Cell Biology |
| Arango-Argoty G, Garner E, Pruden A, et al. (2018) DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data. Microbiome. 6: 23 |
| Song Q, Grene R, Heath LS, et al. (2017) Identification of regulatory modules in genome scale transcription regulatory networks. Bmc Systems Biology. 11: 140 |
| Torkey H, Heath LS, ElHefnawi M. (2017) MicroTarget: MicroRNA target gene prediction approach with application to breast cancer. Journal of Bioinformatics and Computational Biology. 1750013 |
| Aghamirzaie D, Raja Velmurugan K, Wu S, et al. (2017) Expresso: A database and web server for exploring the interaction of transcription factors and their target genes in Arabidopsis thaliana using ChIP-Seq peak data. F1000research. 6: 372 |
| Altarawy D, Eid FE, Heath LS. (2017) PEAK: Integrating Curated and Noisy Prior Knowledge in Gene Regulatory Network Inference. Journal of Computational Biology : a Journal of Computational Molecular Cell Biology |
| Ni Y, Aghamirzaie D, Elmarakeby H, et al. (2016) A Machine Learning Approach to Predict Gene Regulatory Networks in Seed Development in Arabidopsis. Frontiers in Plant Science. 7: 1936 |