Post-Doctoral Position - Disease-Specific Models
- Employer
- Global Academy Jobs
- Location
- France
- Closing date
- May 6, 2019
View more
- Sector
- Science, Computer Science and IT, Computer Science, Life Sciences, Cell and Molecular Biology
- Hours
- Full Time
- Organization Type
- University and College
- Jobseeker Type
- Academic (e.g. 'Lecturer')
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Job Details
Supervised learning of disease-specific models to predict gene regulatory loci.
Abstract
Complex diseases are influenced by common variants. Many SNPs fall in non-coding regions and are likely gene regulatory SNPs (rSNPs). Supervised machine learning models take particular sets of rSNPs or regions and molecular data such eQTLs, transcription factor binding sites and motifs to predict functional regions [1]. In the case of rSNPs, most supervised models are trained with rare variants showinglarge effects anddo not focus on particular diseases or particular non-coding region such
as intergenic and intronic [2; 3]. However several international initiatives exist to associate genetic and phenotypic variation such as the INSERM Genomic Variability 2018, where the TAGC lab participates ( https://bit.ly/2TIfSeo ). Recently we have developed a new method to train a supervised model with common regulatory variants associated to complex diseases that we have run on intergenic and intronic regions. In the present project, we plan to make the TAGOOS method disease-specific.
For more details see http://centuri-livingsystems.org/pdp2019-06-2/
Skills and Qualifications
The Postdoc candidate should have a Doctoral degree in an area related to Mathematics, Computer Science, Biophysics, Bioinformatics or Population Genetics with interest and/or experience in statistics, machine learning, data analysis and human genetics. We expect the candidate to have at least one publication in a peer-reviewed journal relate to any of these topics. The Postdoc candidate will work under the joined supervision of a bioinformatics scientist (Aitor González, TAGC, Marseille, France), a geneticist (Pascal Rihet, TAGC, Marseille, France) and an statistician (Badih Ghattas, I2M, Marseille, France).
Abstract
Complex diseases are influenced by common variants. Many SNPs fall in non-coding regions and are likely gene regulatory SNPs (rSNPs). Supervised machine learning models take particular sets of rSNPs or regions and molecular data such eQTLs, transcription factor binding sites and motifs to predict functional regions [1]. In the case of rSNPs, most supervised models are trained with rare variants showinglarge effects anddo not focus on particular diseases or particular non-coding region such
as intergenic and intronic [2; 3]. However several international initiatives exist to associate genetic and phenotypic variation such as the INSERM Genomic Variability 2018, where the TAGC lab participates ( https://bit.ly/2TIfSeo ). Recently we have developed a new method to train a supervised model with common regulatory variants associated to complex diseases that we have run on intergenic and intronic regions. In the present project, we plan to make the TAGOOS method disease-specific.
For more details see http://centuri-livingsystems.org/pdp2019-06-2/
Skills and Qualifications
The Postdoc candidate should have a Doctoral degree in an area related to Mathematics, Computer Science, Biophysics, Bioinformatics or Population Genetics with interest and/or experience in statistics, machine learning, data analysis and human genetics. We expect the candidate to have at least one publication in a peer-reviewed journal relate to any of these topics. The Postdoc candidate will work under the joined supervision of a bioinformatics scientist (Aitor González, TAGC, Marseille, France), a geneticist (Pascal Rihet, TAGC, Marseille, France) and an statistician (Badih Ghattas, I2M, Marseille, France).
Company
Global Academy Jobs works with over 250 universities worldwide to promote academic mobility and international research collaboration. Global problems need international solutions. Our jobs board and emails reach the academics and researchers who can help.
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Company info
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