منابع مشابه
Correction: Transplanting Supersites of HIV-1 Vulnerability
Citation: The PLOS ONE Staff (2014) Correction: Transplanting Supersites of HIV-1 Vulnerability. PLoS ONE 9(8): e105659. doi:10.1371/journal.pone.0105659 Published August 11, 2014 Copyright: 2014 The PLOS ONE Staff. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium,...
متن کاملTransplanting Supersites of HIV-1 Vulnerability
One strategy for isolating or eliciting antibodies against a specific target region on the envelope glycoprotein trimer (Env) of the human immunodeficiency virus type 1 (HIV-1) involves the creation of site transplants, which present the target region on a heterologous protein scaffold with preserved antibody-binding properties. If the target region is a supersite of HIV-1 vulnerability, recogn...
متن کاملSource apportionment: findings from the U.S. Supersites Program.
Receptor models are used to identify and quantify source contributions to particulate matter and volatile organic compounds based on measurements of many chemical components at receptor sites. These components are selected based on their consistent appearance in some source types and their absence in others. UNMIX, positive matrix factorization (PMF), and effective variance are different soluti...
متن کاملCombiMotif: A new algorithm for network motifs discovery in proteinprotein interaction networks
Discovering motifs in protein–protein interaction networks is becoming a current major challenge in computational biology, since the distribution of the number of network motifs can reveal significant systemic differences among species. However, this task can be computationally expensive because of the involvement of graph isomorphic detection. In this paper, we present a new algorithm (CombiMo...
متن کاملMining Gene-Disease Relationships from Biomedical Literature: Weighting Proteinprotein Interactions and Connectivity
Motivation: The promises of the post-genome era disease-related discoveries and advances have yet to be fully realized, with many opportunities for discovery hiding in the millions of biomedical papers published since. Public databases give access to data extracted from the literature by teams of experts, but their coverage is often limited and lags behind recent discoveries. We present a compu...
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ژورنال
عنوان ژورنال: PLOS Computational Biology
سال: 2019
ISSN: 1553-7358
DOI: 10.1371/journal.pcbi.1006704