Supervised learning and prediction of physical interactions between human and HIV proteins
Identifieur interne : 000063 ( PascalFrancis/Corpus ); précédent : 000062; suivant : 000064Supervised learning and prediction of physical interactions between human and HIV proteins
Auteurs : Matthew D. Dyer ; T. M. Murali ; Bruno W. SobralSource :
- Infection, genetics and evolution : (Print) [ 1567-1348 ] ; 2011.
Descripteurs français
- Pascal (Inist)
English descriptors
- KwdEn :
Abstract
Background: Infectious diseases result in millions of deaths each year. Physical interactions between pathogen and host proteins often form the basis of such infections. While a number of methods have been proposed for predicting protein-protein interactions (PPIs), they have primarily focused on intra-species protein-protein interactions. Methodology: We present an application of a supervised learning method for predicting physical interactions between host and pathogen proteins, using the human-HIV system. Using a Support Vector Machine with a linear kernel, we explore the use of a number of features including domain profiles, protein sequence k-mers, and properties of human proteins in a human PPI network. We achieve the best cross-validation performance when we use a combination of all three of these features. At a precision value of 70% we obtain recall values greater than 40%, depending on the ratio of positive examples to negative examples used during training. We use a classifier trained using these features to predict new PPIs between human and HIV proteins. We focus our discussion on those predicted interactions that involve human proteins known to be critical for HIV replication and propagation. Examples of predicted interactions with support in the literature include those necessary for viral attachment to the host membrane and subsequent invasion of the host cell. Significance: Unlike intra-species PPIs, host-pathogen PPIs have not yet been experimentally detected on a large scale, though they are likely to play important roles in pathogenesis and disease outcomes. Computational methods that can robustly and accurately predict host-pathogen PPIs hold the promise of guiding future experiments and gaining insights into potential mechanisms of pathogenesis.
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Format Inist (serveur)
NO : | PASCAL 11-0292014 INIST |
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ET : | Supervised learning and prediction of physical interactions between human and HIV proteins |
AU : | DYER (Matthew D.); MURALI (T. M.); SOBRAL (Bruno W.) |
AF : | Virginia Bioinformatics Institute, Virginia Tech, 1 Washington St./Blacksburg, VA 24061/Etats-Unis (1 aut., 3 aut.); Department of Computer Science, Virginia Tech, 114 McBryde Hall/Blacksburg, VA 24061/Etats-Unis (2 aut.) |
DT : | Publication en série; Papier de recherche; Niveau analytique |
SO : | Infection, genetics and evolution : (Print); ISSN 1567-1348; Royaume-Uni; Da. 2011; Vol. 11; No. 5; Pp. 917-923; Bibl. 3/4 p. |
LA : | Anglais |
EA : | Background: Infectious diseases result in millions of deaths each year. Physical interactions between pathogen and host proteins often form the basis of such infections. While a number of methods have been proposed for predicting protein-protein interactions (PPIs), they have primarily focused on intra-species protein-protein interactions. Methodology: We present an application of a supervised learning method for predicting physical interactions between host and pathogen proteins, using the human-HIV system. Using a Support Vector Machine with a linear kernel, we explore the use of a number of features including domain profiles, protein sequence k-mers, and properties of human proteins in a human PPI network. We achieve the best cross-validation performance when we use a combination of all three of these features. At a precision value of 70% we obtain recall values greater than 40%, depending on the ratio of positive examples to negative examples used during training. We use a classifier trained using these features to predict new PPIs between human and HIV proteins. We focus our discussion on those predicted interactions that involve human proteins known to be critical for HIV replication and propagation. Examples of predicted interactions with support in the literature include those necessary for viral attachment to the host membrane and subsequent invasion of the host cell. Significance: Unlike intra-species PPIs, host-pathogen PPIs have not yet been experimentally detected on a large scale, though they are likely to play important roles in pathogenesis and disease outcomes. Computational methods that can robustly and accurately predict host-pathogen PPIs hold the promise of guiding future experiments and gaining insights into potential mechanisms of pathogenesis. |
CC : | 002B05A02; 002B05C02D; 002B06D01 |
FD : | SIDA; Facteur prédictif; Protéine; Homme; Virus immunodéficience humaine; Epidémiologie moléculaire |
FG : | Virose; Infection; Lentivirus; Retroviridae; Virus; Immunodéficit; Immunopathologie |
ED : | AIDS; Predictive factor; Protein; Human; Human immunodeficiency virus; Molecular epidemiology |
EG : | Viral disease; Infection; Lentivirus; Retroviridae; Virus; Immune deficiency; Immunopathology |
SD : | SIDA; Factor predictivo; Proteína; Hombre; Human immunodeficiency virus; Epidemiología molecular |
LO : | INIST-28039.354000190369050160 |
ID : | 11-0292014 |
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<front><div type="abstract" xml:lang="en">Background: Infectious diseases result in millions of deaths each year. Physical interactions between pathogen and host proteins often form the basis of such infections. While a number of methods have been proposed for predicting protein-protein interactions (PPIs), they have primarily focused on intra-species protein-protein interactions. Methodology: We present an application of a supervised learning method for predicting physical interactions between host and pathogen proteins, using the human-HIV system. Using a Support Vector Machine with a linear kernel, we explore the use of a number of features including domain profiles, protein sequence k-mers, and properties of human proteins in a human PPI network. We achieve the best cross-validation performance when we use a combination of all three of these features. At a precision value of 70% we obtain recall values greater than 40%, depending on the ratio of positive examples to negative examples used during training. We use a classifier trained using these features to predict new PPIs between human and HIV proteins. We focus our discussion on those predicted interactions that involve human proteins known to be critical for HIV replication and propagation. Examples of predicted interactions with support in the literature include those necessary for viral attachment to the host membrane and subsequent invasion of the host cell. Significance: Unlike intra-species PPIs, host-pathogen PPIs have not yet been experimentally detected on a large scale, though they are likely to play important roles in pathogenesis and disease outcomes. Computational methods that can robustly and accurately predict host-pathogen PPIs hold the promise of guiding future experiments and gaining insights into potential mechanisms of pathogenesis.</div>
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<ET>Supervised learning and prediction of physical interactions between human and HIV proteins</ET>
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