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Wyszukujesz frazę "Badie, Christophe" wg kryterium: Wszystkie pola


Wyświetlanie 1-2 z 2
Tytuł:
Potential protein activity modifications of amino acid variants in the human transcriptome
Autorzy:
Zyla, Joanna
Bulman, Robert
Badie, Christophe
Bouffler, Simon
Powiązania:
https://bibliotekanauki.pl/articles/1039132.pdf
Data publikacji:
2015
Wydawca:
Polskie Towarzystwo Biochemiczne
Tematy:
RNA editing
amino acid variants
Opis:
Background: The occurrence of widespread RNA and DNA sequence differences in the human transcriptome was reported in 2011. Similar findings were described in a second independent publication on personal omics profiling investigating the occurrence of dynamic molecular and related medical phenotypes. The suggestion that the RNA sequence variation was likely to affect disease susceptibility prompted us to investigate with a range of algorithms the amino acid variants reported to be present in the identified peptides to determine if they might be disease-causing. Results: The predictive qualities of the different algorithms were first evaluated by using nonsynonymous single-base nucleotide polymorphism (nsSNP) datasets, using independently established data on amino acid variants in several proteins as well as data obtained by mutational mapping and modelling of binding sites in the human serotonin transporter protein (hSERT). Validation of the used predictive algorithms was at a 75% level. Using the same algorithms, we found that widespread RNA and DNA sequence differences were predicted to impair the function of the peptides in over 57% of cases. Conclusions: Our findings suggest that a proportion of edited RNAs which serve as templates for protein synthesis is likely to modify protein function, possibly as an adaptive survival mechanism in response to environmental modifications.
Źródło:
Acta Biochimica Polonica; 2015, 62, 1; 57-61
0001-527X
Pojawia się w:
Acta Biochimica Polonica
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Machine learning techniques combined with dose profiles indicate radiation response biomarkers
Autorzy:
Papiez, Anna
Badie, Christophe
Polanska, Joanna
Powiązania:
https://bibliotekanauki.pl/articles/331077.pdf
Data publikacji:
2019
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
machine learning
gene profiling
radiation response
multiple random validation
transcription
uczenie maszynowe
profilowanie genów
odpowiedź radiacyjna
transkrypcja
Opis:
The focus of this research is to combine statistical and machine learning tools in application to a high-throughput biological data set on ionizing radiation response. The analyzed data consist of two gene expression sets obtained in studies of radiosensitive and radioresistant breast cancer patients undergoing radiotherapy. The data sets were similar in principle; however, the treatment dose differed. It is shown that introducing mathematical adjustments in data preprocessing, differentiation and trend testing, and classification, coupled with current biological knowledge, allows efficient data analysis and obtaining accurate results. The tools used to customize the analysis workflow were batch effect filtration with empirical Bayes models, identifying gene trends through the Jonckheere–Terpstra test and linear interpolation adjustment according to specific gene profiles for multiple random validation. The application of non-standard techniques enabled successful sample classification at the rate of 93.5% and the identification of potential biomarkers of radiation response in breast cancer, which were confirmed with an independent Monte Carlo feature selection approach and by literature references. This study shows that using customized analysis workflows is a necessary step towards novel discoveries in complex fields such as personalized individual therapy.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2019, 29, 1; 169-178
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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