Wei Li
Orcid: 0000-0002-2163-7903Affiliations:
- George Washington University, Genomics and Precision Medicine, Washington, DC, USA
- Children's National Medical Center, Washington, DC, USA
- Harvard School of Public Health, Boston, MA, USA (2012 - 2018)
- Dana-Farber Cancer Institute, Boston, MA, USA (2012 - 2018)
- University of California Riverside, Algorithm and Computational Biology Lab, CA, USA (PhD 2012)
According to our database1,
Wei Li
authored at least 16 papers
between 2008 and 2023.
Collaborative distances:
Collaborative distances:
Timeline
Legend:
Book In proceedings Article PhD thesis Dataset OtherLinks
Online presence:
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on weililab.org
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on orcid.org
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on cs.ucr.edu
On csauthors.net:
Bibliography
2023
Feature augmentation and semi-supervised conditional transfer learning for early detection of sepsis.
Comput. Biol. Medicine, October, 2023
2021
Nucleic Acids Res., 2021
2019
Genome-wide identification of the essential protein-coding genes and long non-coding RNAs for human pan-cancer.
Bioinform., 2019
2018
2016
2015
2012
RNA-Seq Based Transcriptome Assembly: Sparsity, Bias Correction and Multiple Sample Comparison.
PhD thesis, 2012
Transcriptome assembly and isoform expression level estimation from biased RNA-Seq reads.
Bioinform., 2012
Workshop: Transcriptome assembly and isoform expression level estimation from biased RNA-Seq reads.
Proceedings of the IEEE 2nd International Conference on Computational Advances in Bio and Medical Sciences, 2012
2011
J. Comput. Biol., 2011
IsoLasso: A LASSO Regression Approach to RNA-Seq Based Transcriptome Assembly - (Extended Abstract).
Proceedings of the Research in Computational Molecular Biology, 2011
Workshop: Transcriptome assembly from RNA-Seq data: Objectives, algorithms and challenges.
Proceedings of the IEEE 1st International Conference on Computational Advances in Bio and Medical Sciences, 2011
2010
Proceedings of the 10th IEEE International Conference on Bioinformatics and Bioengineering, 2010
2008
COX-2 activity prediction in Chinese medicine using neural network based ensemble learning methods.
Proceedings of the International Joint Conference on Neural Networks, 2008