T of various solvents and other conditions on periodate oxidation of
T of various solvents and other conditions on periodate oxidation of carbohydrates in the PAS reaction. Ann Histochim. 1962,7:57?.doi:10.1186/1741-7007-11-86 Cite this article as: Yang et al.: Integration-deficient lentivectors: an effective strategy to purify and differentiate human embryonic stem cell-derived hepatic progenitors. BMC Biology 2013 11:86.Submit your next manuscript to BioMed Central and take full advantage of:?Convenient online submission ?Thorough peer review ?No space constraints or color PubMed ID:https://www.ncbi.nlm.nih.gov/pubmed/28607003 figure charges ?Immediate publication on acceptance ?Inclusion in PubMed, CAS, Scopus and Google Scholar ?Research which is freely available for redistributionSubmit your manuscript at www.biomedcentral.com/submit
Cilia et al. BMC Bioinformatics 2014, 15:309 PubMed ID:https://www.ncbi.nlm.nih.gov/pubmed/28494239 http://www.biomedcentral.com/1471-2105/15/RESEARCH ARTICLEOpen AccessPredicting virus mutations through statistical relational learningElisa Cilia1,2 , Stefano Teso3 , Sergio Ammendola4 , Tom Lenaerts1,2,5 and Andrea Passerini3*Abstract Background: Viruses are typically characterized by high mutation rates, which allow them to quickly develop drug-resistant mutations. Mining relevant rules from mutation data can be extremely useful to understand the virus adaptation mechanism and to design drugs that effectively counter potentially resistant mutants. Results: We propose a simple statistical relational learning approach for mutant prediction where the input consists of mutation data with drug-resistance information, either as sets of mutations conferring resistance to a certain drug, or as sets of mutants with information on their susceptibility to the drug. The algorithm learns a set of relational rules characterizing drug-resistance and uses them to generate a set of potentially resistant mutants. Learning a weighted combination of rules allows to attach generated mutants with a resistance score as predicted by the statistical relational model and select only the highest scoring ones. Conclusions: Promising results were obtained in generating resistant mutations for both nucleoside and non-nucleoside HIV reverse transcriptase inhibitors. The approach can be generalized quite easily to learning mutants characterized by more complex rules correlating multiple mutations. BackgroundHIV is a pandemic cause of lethal pathologies in more than 33 million people. Its horizontal transmission trough mucosae is difficult to control and treat because the virus has a high virulence and it infects several type of immune surveillance cells, such as those characterized by CD4 receptor (CD4+ cells). The major problem in treating the human virus infection is the drug selectivity since the virus penetrates in the cell where it releases its genetic material to replicate itself by using the cell mechanisms. A drug target is the replicating apparatus of the cell. HIV antiviral molecules will be directed against several cells such as macrophages or lymphocytes T to interfere with viral replication. The HIV releases a single-strand RNA particle, a reverse transcriptase and an integrase into the cell cytoplasm. Quickly the RNA molecule is retrotranscribed in a DNA double strand molecule, which is integrated into the host genome. The integration events induce a cellular response, which begins with the transcription of the Tat gene by the RNA polymerase II. Tat*Correspondence: [email protected] 3 Department of Computer Science and Information Engineering, University of Trento, via Thonzonium (bromide) molecular weight Sommarive 5, I-38123.