Inmunología in silico: diseno e implementación
-
1
Universidad Pablo de Olavide
info
ISSN: 2173-0903
Año de publicación: 2020
Número: 37
Tipo: Artículo
Otras publicaciones en: MoleQla: revista de Ciencias de la Universidad Pablo de Olavide
Resumen
Los métodos basados en ´ Machine Learning se presentan como los mas prometedores en diversos campos, como la inmunología, para solucionar problemas que hasta ahora dependen de pasos de modelización complejos. Una de las áreas a explorar es el diseño, fabricación y seguimiento de vacunas, partiendo de fuentes de datos biológicos a gran escala
Referencias bibliográficas
- Fumiya Watanabe y col. ((Day-ahead Strategic Marketing of Energy Prosumption: A Machine Learning Approach Based on Neural Networks)). En: 2019 18th European Control Conference (ECC). IEEE, jun. de 2019.
- Edgar P. Torres P. y col. ((Stock Market Data Prediction Using Machine Learning Techniques)). En: Advances in Intelligent Systems and Computing. Springer International Publishing, 2019, pags. 539-547. ´
- Li Shen y col. ((Deep Learning to Improve Breast Cancer Detection on Screening Mammography)). En: Scientic Reports 9.1 (ago. de 2019).
- Jonathan Schmidt y col. ((Recent advances and applications of machine learning in solid-state materials science)). En: npj Computational Materials 5.1 (ago. de 2019).
- Adi L. Tarca y col. ((Machine Learning and Its Applications to Biology)). En: PLoS Computational Biology 3.6 (2007), e116.
- Stephen F. Altschul y col. ((Basic local alignment search tool)). En: Journal of Molecular Biology 215.3 (oct. de 1990), pags. 403-410. ´
- Jesus Ferrero Bermejo y col. ´ ((A Review of the Use of Artificial Neural Network Models for Energy and Reliability Prediction. A Study of the Solar PV, Hydraulic and Wind Energy Sources)). En: Applied Sciences 9.9 (mayo de 2019), pag. 1844. ´
- Ke-Lin Du y col. ((Recurrent Neural Networks)). En: Neural Networks and Statistical Learning. Springer London, dic. de 2013, pags. 337-353. ´
- Barak A. Pearlmutter. ((Learning State Space Trajectories in Recurrent Neural Networks)). En: Neural Computation 1.2 (jun. de 1989), pags. 263-269. ´
- Amr El-Desoky Mousa y col. ((Deep Bidirectional Long Short-Term Memory Recurrent Neural Networks for Grapheme-to-Phoneme Conversion Utilizing Complex Many-to-Many Alignments)). En: Interspeech 2016. ISCA, sep. de 2016.
- Mariagrazia Belfiore y col. ((In Silico Modeling of the Immune System: Cellular and Molecular Scale Approaches)). En: BioMed Research International 2014 (2014), pags. 1-7. ´
- Catalin Buiu y col. ((Learning the Relationship between the Primary Structure of HIV Envelope Glycoproteins and Neutralization Activity of Particular Antibodies by Using Artificial Neural Networks)). En: International Journal of Molecular Sciences 17.10 (oct. de 2016), pag. 1710. ´
- Yi Yang y col. ((In silicodesign of a DNA-based HIV-1 multi-epitope vaccine for Chinese populations)). En: Human Vaccines & Immunotherapeutics 11.3 (mar. de 2015), pags. 795-805. ´
- Sebastian Suerbaum y col. ((Helicobacter pyloriInfection)). En: New England Journal of Medicine 347.15 (oct. de 2002), pags. 1175-1186. ´
- Songhua Zhang y col. ((H. pylori vaccines)). En: Human Vaccines 7.11 (nov. de 2011), pags. 1153-1157. ´
- Mazhar Khan y col. ((Immunoinformatics approaches to explore Helicobacter Pylori proteome (Virulence Factors) to design B and T cell multi-epitope subunit vaccine)). En: Scientic Reports 9.1 (sep. de 2019).
- Pietro Sormanni y col. ((Third generation antibody discovery methods:in silicorational design)). En: Chemical Society Reviews 47.24 (2018), pags. 9137-9157. ´
- Edgar Liberis y col. ((Parapred: antibody paratope prediction using convolutional and recurrent neural networks)). En: Bioinformatics 34.17 (abr. de 2018). Ed. por John Hancock, pags. 2944-2950. ´
- Mario Abdel Messih y col. ✭ ✭Improving the accuracy of the structure prediction of the third hypervariable loop of the heavy chains of antibodies✮ ✮ . En: Bioinformatics 30.19 (jun. de 2014), pags. 2733-2740. ´
- Yee Siew Choong y col. ✭ ✭ Computer-Aided Antibody Design: An Overview✮ ✮ . En: Recombinant Antibodies for Infectious Diseases. Springer International Publishing, 2017, pags. 221-243. ´
- Lenka Potocnakova y col. ✭ ✭An Introduction to BCell Epitope Mapping and In Silico Epitope Prediction✮ ✮ . En: Journal of Immunology Research 2016 (2016), pags. 1-11. ´
- Martin J. Blythe y col. ✭ ✭Benchmarking B cell epitope prediction: Underperformance of existing methods✮ ✮ . En: Protein Science 14.1 (ene. de 2009), pags. 246-248. ´
- Sudipto Saha y col. ✭ ✭BcePred: Prediction of Continuous B-Cell Epitopes in Antigenic Sequences Using Physico-chemical Properties✮ ✮ . En: Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2004, pags. 197-204. ´
- Sudipto Saha y col. En: BMC Genomics 6.1 (2005), pag. 79. ´
- Randi Vita y col. ✭ ✭The Immune Epitope Database (IEDB): 2018 update✮ ✮ . En: Nucleic Acids Research 47.D1 (oct. de 2018), pags. D339-D343. ´
- Sandeep Kumar Dhanda y col. ✭ ✭IEDB-AR: immune epitope database—analysis resource in 2019✮ ✮ . En: Nucleic Acids Research 47.W1 (mayo de 2019), W502-W506.
- Michael Schantz Klausen y col. ✭ ✭LYRA, a webserver for lymphocyte receptor structural modeling✮ ✮ . En: Nucleic Acids Research 43.W1 (mayo de 2015), W349-W355.
- Swapnil Mahajan y col. ✭ ✭Benchmark datasets of immune receptor-epitope structural complexes✮ ✮ . En: BMC Bioinformatics 20.1 (oct. de 2019).
- Vanessa Jurtz y col. ✭ ✭ NetMHCpan-4.0: Improved Peptide–MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data✮ ✮ . En: The Journal of Immunology 199.9 (oct. de 2017), pags. 3360-3368. ´
- Yohan Kim y col. ✭ ✭Dataset size and composition impact the reliability of performance benchmarks for peptide-MHC binding predictions✮ ✮ . En: BMC Bioinformatics 15.1 (2014), pag. 241. ´
- Darius Moradpour y col. ✭ ✭ Hepatitis C Virus Proteins: From Structure to Function✮ ✮ . En: Current Topics in Microbiology and Immunology. Springer Berlin Heidelberg, 2013, pags. 113-142. ´
- Ping Xu y col. ✭ ✭Effects of glutamine and asparagine on recombinant antibody production using CHO-GS cell lines✮ ✮ . En: Biotechnology Progress 30.6 (ago. de 2014), pags. 1457-1468. ´
- Si Nga Sou y col. ✭ ✭ How does mild hypothermia affect monoclonal antibody glycosylation?✮ ✮ En: Biotechnology and Bioengineering 112.6 (abr. de 2015), pags. 1165-1176. ´
- Fatemeh Bashokouh y col. ✭ ✭Optimization of cultivation conditions for monoclonal IgM antibody production by M1A2 hybridoma using artificial neural network✮ ✮ . En: Cytotechnology 71.4 (jul. de 2019), pags. 849-860. ´
- [ LiMin Fu y col. ✭ ✭Incremental backpropagation learning networks✮ ✮ . En: IEEE Transactions on Neural Networks 7.3 (mayo de 1996), pags. 757-761. ´
- Ashish Prabhu A. y col. ✭ ✭Reverse micellar extraction of papain with cationic detergent based system: An optimization approach✮ ✮ . En: Preparative Biochemistry and Biotechnology 47.3 (jun. de 2016), pags. 236-244. ´
- Haruna Chiroma y col. ✭ ✭ Neural Networks Optimization through Genetic Algorithm Searches: A Review✮ ✮ . En: Applied Mathematics & Information Sciences 11.6 (nov. de 2017), pags. 1543-1564. ´
- Hossein Jelvehgaran Esfahani y col. ✭ ✭Big data and social media: A scientometrics analysis✮ ✮ . En: International Journal of Data and Network Science (2019), pags. 145-164. ´
- Junxiang Wang y col. ✭ ✭Adverse event detection by integrating twitter data and VAERS✮ ✮ . En: Journal of Biomedical Semantics 9.1 (jun. de 2018).
- Karen Smith y col. ✭ ✭Methods to Compare Adverse Events in Twitter to FAERS, Drug Information Databases, and Systematic Reviews: Proof of Concept with Adalimumab✮ ✮ . En: Drug Safety 41.12 (ago. de 2018), pags. 1397-1410. ´
- Jingcheng Du y col. ✭ ✭Optimization on machine learning based approaches for sentiment analysis on HPV vaccines related tweets✮ ✮ . En: Journal of Biomedical Semantics 8.1 (mar. de 2017)