Cargando…
Hidden neural networks for transmembrane protein topology prediction
Hidden Markov Models (HMMs) are amongst the most successful methods for predicting protein features in biological sequence analysis. However, there are biological problems where the Markovian assumption is not sufficient since the sequence context can provide useful information for prediction purpos...
Autores principales: | Tamposis, Ioannis A., Sarantopoulou, Dimitra, Theodoropoulou, Margarita C., Stasi, Evangelia A., Kontou, Panagiota I., Tsirigos, Konstantinos D., Bagos, Pantelis G. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Research Network of Computational and Structural Biotechnology
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8606341/ https://www.ncbi.nlm.nih.gov/pubmed/34849210 http://dx.doi.org/10.1016/j.csbj.2021.11.006 |
Ejemplares similares
-
OMPdb: A Global Hub of Beta-Barrel Outer Membrane Proteins
por: Roumia, Ahmed F., et al.
Publicado: (2021) -
Drug genetic associations with COVID-19 manifestations: a data mining and network biology approach
por: Charitou, Theodosia, et al.
Publicado: (2022) -
MAGE: An Open-Source Tool for Meta-Analysis of Gene Expression Studies
por: Tamposis, Ioannis A., et al.
Publicado: (2022) -
Algorithms for incorporating prior topological information in HMMs: application to transmembrane proteins
por: Bagos, Pantelis G, et al.
Publicado: (2006) -
Immune-based treatment re-challenge in renal cell carcinoma: A systematic review and meta-analysis
por: Papathanassiou, Maria, et al.
Publicado: (2022)