Toggle 1
Toggle 2
Toggle 1
Toggle 2
Funded by the Horizon 2020 Framework Programme of the European Union.
Grant holder institution:
(GH Manager: Dr Chloe Huseyin)
Biosciences Institute,
University College Cork,
Western road,
Cork, Ireland,
T12 YT20.
This site uses cookies. By continuing to browse the site, you are agreeing to our use of cookies.
Accept settingsHideWe may request cookies to be set on your device. We use cookies to let us know when you visit our websites, how you interact with us, to enrich your user experience, and to customize your relationship with our website.
Click on the different category headings to find out more. You can also change some of your preferences. Note that blocking some types of cookies may impact your experience on our websites and the services we are able to offer.
These cookies are strictly necessary to provide you with services available through our website and to use some of its features.
Because these cookies are strictly necessary to deliver the website, refuseing them will have impact how our site functions. You always can block or delete cookies by changing your browser settings and force blocking all cookies on this website. But this will always prompt you to accept/refuse cookies when revisiting our site.
We fully respect if you want to refuse cookies but to avoid asking you again and again kindly allow us to store a cookie for that. You are free to opt out any time or opt in for other cookies to get a better experience. If you refuse cookies we will remove all set cookies in our domain.
We provide you with a list of stored cookies on your computer in our domain so you can check what we stored. Due to security reasons we are not able to show or modify cookies from other domains. You can check these in your browser security settings.
We also use different external services like Google Webfonts, Google Maps, and external Video providers. Since these providers may collect personal data like your IP address we allow you to block them here. Please be aware that this might heavily reduce the functionality and appearance of our site. Changes will take effect once you reload the page.
Google Webfont Settings:
Google Map Settings:
Google reCaptcha Settings:
Vimeo and Youtube video embeds:
You can read about our cookies and privacy settings in detail on our Privacy Policy Page.
Bioinformatician job opportunity
We are looking for a bioinformatician with experience in biostatistics and analyses of microbiome data (deadline for application is Sep 30, 2020), to work on a research project for 9 months. The position is funded by Portuguese Funds and it will be at MyBiome, a spin-off of NOVA Medical School https://www.unl.pt/en/entrepreneurship/mybiome. There will be the possibility for employees to work remotely.
For further information and to apply please contact: Cláudia Marques and Diogo Pestana.
Frontiers Research Topic -Microbiome and Machine Learning
Submission deadline November 11th 2020
In recent years, the human microbiome has been paid great attention. Several large-scale studies have pointed out the microbiome as a key player in intestinal and non-intestinal diseases. High expectations have been put on the use of microbiome data in clinical use for diagnostics, prognostics and therapeutics, as well as to focus on its causality role in diseases. However, these promising applications are still in their infancy.
Machine Learning (ML) methods offer great potential to continue growing microbiome science. ML algorithms are developed to process high dimensional data and to deal with uncertainty and noise, while the aims of the algorithms are multiple: classification, prediction, etc. However, to maximize the combinatory potential of these emerging fields (microbiome and ML), several challenges must first be overcome. One of the reasons is that microbiome data are inherently convoluted, noisy and highly variable, and non-standard analytical methodologies are therefore required to unlock its clinical and scientific potential. In this manner, although there are available a wide range of statistical modelling and ML methods, sub-optimal implementation often leads to errors, over-fitting and misleading results, due to a lack of good analytical practices and ML expertise in the microbiome community.
Considering these facts, our Research Topic is based on the idea that microbiome science has not benefited enough from the interaction with available machine learning methods. This Research Topic will allow the field to advance in this mission, through its dissemination as well as the participation of experts over the world in the discussion.
The aim of this Research Topic is to collect all article types (reviews, research papers, methods papers, …) related to machine learning in relation to microbiome data. We encourage manuscripts about review, evaluation or application of ML-based models, software packages and web servers for specific prediction problems in microbiome data, as well as the development on novel ones. We welcome the submission of research articles that use machine learning as an underlying modeling strategy and/or primary data analysis tool in any kind of microbiome data. Biomedical data is a must-have, but we are open to any kind of field, from plants to environmental, the goal is to advance in the use of ML techniques in microbiome data and gain insight into current problems and methods in ML on gut microbiome data. Thus, we expect submissions from different expertise fields: Biological Sciences, Computer and Information Sciences, Mathematics and Applied Statistics, Translational Medicine, Environmental Biotechnology as well as Physical Sciences including Network Science.
Thus, specific topics may include, but are not limited to:
• Optimization of data preparation of microbiome data to use ML techniques
• Tools and pipelines to analyze microbiome data with ML methods, for experts and non-experts
• Latest ML algorithms with applications in taxa and gene function prediction
• ML models to extract potential microbiome-biomarkers
• ML approaches and applications for integrating multi-level omics data
• ML models for early disease prediction and prevention
• Studies based on dynamical and prospective models of microbiome
• Studies based on multi-omics data, e.g. the combination of genomic, transcriptomic, epigenomic, or proteomic data
Submit your manuscript now
CLosed Postdoctoral fellowship (Norway)
Postdoctoral Research Fellow in Microbiome and Lung Research
At the Faculty of Medicine, Department of Clinical Science, a full-time (100 %) position as Postdoctoral Research Fellow is available for a period of three (3) years. The position is part of the project ”Oral and Environmental Microbiome, Endotoxin and Lung Health; the United Airways concept extended”, financed by the Research Council of Norway.
Apply here