Käyttäjän nvthuyne profiilikuva
@nvthuyne
Maan Belgium lippu Wortegem-petegem, Belgium
Jäsen alkaen 16. kesäkuuta 2012
0 Suositukset

nvthuyne

Online-tilassa Offline-tilassa
I have 10 years of experience working with R-scripted programming and recently, the last 4 years I have been working on NGS analysis with 454, Illumina data and in the near future Solid data and MiSeq data. These analysis range from basic statistical analysis to custom developped pipelines. Next to this i am fluent in big data analysis using multiple statistical tools such as R, perl, python, matlab, excel, ... I also have programming experience in perl, php, java, c, c++ and I started recently in web scraping and data mining
$22 USD/hr
7 arvostelua
3.7
  • 50%Suoritetut työtehtävät
  • 100%Budjetin mukaisesti
  • 100%Ajallaan
  • 33%Uudelleenpalkkaamisaste

Portfolio

Viimeaikaiset arvostelut

  • käyttäjän Agnieszka K. kuva Analysis of genom micro array $222.00 USD

    “No contact with freelancer”

  • käyttäjän Agnieszka K. kuva data mining $80.00 USD

    “very good 100%”

  • käyttäjän Agnieszka K. kuva Databe querries $100.00 USD

    “very good 100% satisfaction”

  • käyttäjän Stefan B. kuva Project 4777876 has been deleted $50.00 USD

    “Very professional, reliable and intelligent freelancer. Recommended.”

  • käyttäjän Agnieszka K. kuva Statistical tasks to solve + description $155.00 USD

    “very good cooperation 100% satisfaction !”

  • käyttäjän Research C. kuva SPSS Data Analysis $30.00 USD

    “Nicky has completed a pilot testing for us. He has done a great job (even better than what we expect). He is really a statistician. We will certainly find him for our full study again.”

Kokemus

Bioinformatician/DataManager

Jul 2012

Two folded task: 1) perform data analysis generated from NGS (Solid and Miseq) 2) maintain/optimize/innovate all data flows concerning the data generated by the NGS technologies

Koulutus

Computer Science

1995 - 2002 (7 years)

Master in Statistical Data Analysis

2007 - 2012 (5 years)

Pätevyydet

Professional Certificate (2013)

Stanford University

successfully completed a free online offering of the following course provided by Stanford University through Coursera inc. MACHINE LEARNING

Julkaisut

Risk-benefit analysis regarding seafood consumption: a tool for combined intake assessment

The aim of food consumption is to provide people with the daily necessary energy, macro- and micronutrients in order to meet recommendations and to be able to execute daily tasks. What people need are the beneficial compounds that can be found in food products. Nevertheless, people risk to ingest simultaneously compounds that can have toxicological effects. These harmful compounds can on the one hand occur naturally in food, but on the other hand anthropogenic or man-made processes can lead to contamination

How to use secondary data on seafood contamination for probabilistic exposure assessment purposes? Main problems and potential solutions

nullSeafood consumption is related to both favorable health benefits of nutrients and to potential adverse health impacts of chemical contamination. To quantify the magnitude of this dilemma, probabilistic intake assessments can be performed. One step in such a procedure involves the determination of nutrient and contaminant concentrations in seafood for which data need to be collected. This article describes the process of building up a database containing previously published contaminant concentrations in

Development of a nutrient database and distributions for use in a probabilistic risk-benefit analysis of human seafood consumption

Human consumption of seafood can be promoted because of its positive health effects. Conversely, it is a source of chemical contaminants. Due to this dilemma, a probabilistic intake assessment of nutrients and contaminants via seafood is of interest to provide more detailed information. A key component of such an assessment is the selection of the most appropriate input distributions to describe the consumption and concentration data. This paper describes the construction of a nutrient database, pooling vit

Todistukset

  • Numeracy 1
    98%
  • Dutch 1
    95%
  • Python Level 1
    78%
  • US English Level 3
    78%

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