The analysis of the human microbiome has recently attracted the attention of several research communities, due to its potential diagnostics, prognostics, and therapeutics role for several diseases, including diabetes, liver cirrhosis, some types of cancer (e.g., colorectal cancer) as well as for disorders like the autism spectrum disorder. The adoption of statistical and Machine Learning approaches appears very promising to elucidate existing (or identify novel) relationships between microbiome conditions and diseases or to build descriptive and predictive models that can be adopted to improve existing therapeutic procedures. The workshop “Advanced machine learning approaches for the analysis of microbiome data” will focus on advanced machine learning approaches and their (potential or actual) application to microbiome data, including semi-supervised learning methods, multi-view learning methods, and transfer learning approaches.

Submission

Submission to the workshop is enabled via the BITS2023 conference submission form. Please, choose Session "Machine Learning applications for microbiome data analysis (Workshop)". 
The deadline and notification dates are the same as the key dates of the abstract submission to the main conference.