Bacterial Immunogenicity Prediction by Machine Learning Methods
The identification of protective immunogens is the most important and vigorous initial step in the long-lasting and expensive process of vaccine design and development. Machine learning (ML) methods are very effective in data mining and in the analysis of big data such as microbial proteomes. They a...
Main Authors: | Ivan Dimitrov, Nevena Zaharieva, Irini Doytchinova |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2020-11-01
|
Series: | Vaccines |
Subjects: | |
Online Access: | https://www.mdpi.com/2076-393X/8/4/709 |
Similar Items
-
Predicting Immunogenicity Risk in Biopharmaceuticals
by: Nikolet Doneva, et al.
Published: (2021-02-01) -
In silico prediction of cancer immunogens: current state of the art
by: Irini A. Doytchinova, et al.
Published: (2018-03-01) -
A Machine Learning Approach for High-Dimensional Time-to-Event Prediction With Application to Immunogenicity of Biotherapies in the ABIRISK Cohort
by: Julianne Duhazé, et al.
Published: (2020-04-01) -
OVERVIEW OF PRE-CLINICAL TECHNIQUES FOR PREDICTING THE IMMUNOGENICITY OF THERAPEUTICS IN DRUG DEVELOPEMENT
by: Clarisse Musanabaganwa, et al.
Published: (2014-03-01) -
Predicting HLA CD4 Immunogenicity in Human Populations
by: Sandeep Kumar Dhanda, et al.
Published: (2018-06-01)