Cargando…
Cross-Predicting Essential Genes between Two Model Eukaryotic Species Using Machine Learning
Experimental studies of Caenorhabditis elegans and Drosophila melanogaster have contributed substantially to our understanding of molecular and cellular processes in metazoans at large. Since the publication of their genomes, functional genomic investigations have identified genes that are essential...
Autores principales: | Campos, Tulio L., Korhonen, Pasi K., Young, Neil D. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8150380/ https://www.ncbi.nlm.nih.gov/pubmed/34064595 http://dx.doi.org/10.3390/ijms22105056 |
Ejemplares similares
-
An Evaluation of Machine Learning Approaches for the Prediction of Essential Genes in Eukaryotes Using Protein Sequence-Derived Features()
por: Campos, Tulio L., et al.
Publicado: (2019) -
Predicting gene essentiality in Caenorhabditis elegans by feature engineering and machine-learning
por: Campos, Tulio L., et al.
Publicado: (2020) -
Combined use of feature engineering and machine-learning to predict essential genes in Drosophila melanogaster
por: Campos, Tulio L, et al.
Publicado: (2020) -
Identifying essential genes across eukaryotes by machine learning
por: Beder, Thomas, et al.
Publicado: (2021) -
OGEE v3: Online GEne Essentiality database with increased coverage of organisms and human cell lines
por: Gurumayum, Sanathoi, et al.
Publicado: (2020)