Gianvito Pio, PhD, Early Career Investigator (ECI), is an Assistant Professor at the Department of Computer Science, University of Bari, Italy. He published 36 papers, including 18 in journals with high impact factors, such as Machine Learning, Data Mining and Knowledge Discovery, Bioinformatics, BMC Bioinformatics, Information Sciences, and IEEE Transaction on Knowledge and Data Engineering. He is on the editorial board of Springer Medical & Biological Engineering & Computing, served as a reviewer for several international journals and participated in the scientific committee of several international conferences.  His research interests include Machine Learning, Big Data Analytics, Bioinformatics and Blockchain.

STSM Title:

Standardizing the pipeline for the analysis of high dimensional noisy Microbiome data

Host: Prof. Sašo Džeroski, Jozef Stefan Institute, Ljubljana (Slovenia)

Dates: 01/03/2020 to 31/03/2020

Microbiome studies make use of different types of data that may show different characteristics. However, they usually have one common characteristic, i.e., they are high-dimensional and noisy. Identifying a standard process of analysis of such kind of data is fundamental since it would enable the design of tools that could allow performing clinical analysis without owning specific machine learning expertise. In this context, the purpose of this STSM was to study existing works for the analysis of microbiome data to identify commonalities in the followed workflows. A specific ontology tailored for defining and reasoning on Data Mining tasks (OntoDM) has been considered during such an analysis, aiming at defining a set of guidelines and possibly a limited set of pipelines according to the classification provided by the ontology. Particular attention has been put on possible strategies for handling the high dimensionality of data and the presence of noise, considering the introduction, in the pipelines, of specific pre-processing approaches for feature reduction/extraction as well as the adoption of specific learning methods that are inherently able to work with high-dimensional noisy data.
The obtained results confirmed that we can handle microbiome data similarly as we handle high-dimensional noisy data in other application domains, without specific steps (except for the CLR normalization). However, it is noteworthy that these conclusions apply to the specific task considered during the pilot study, namely multi-target regression in the specific case of the prediction of Type II diabetes.

Vladimir Trajkovik was born in Skopje, R.N. Macedonia in 1971. He received a PhD degree in 2003 from Ss. Cyril and Methodius University in Skopje. His current position is a professor at the Faculty of Computer Science and Engineering. He has published 4 books as author or editor, more than 60 papers in respectable journals, and more than 160 conference papers. He has more than 1400 citations, with an h-index of 21.

His research interests include: Information Systems Analyses and Design, Distributed Systems, ICT based Collaboration Systems and Mobile services with a special focus on Connected Health.

STSM Title:

Evaluation of state-of-the-art research on the application of machine learning in human microbiome studies

Host: Prof. Tatjana Loncar Turukalo, University of Novi Sad, Serbia

Dates: 01/03/2020 – 07/03/2020

Thanks to sequencing technology development, microbiome studies with a large number of samples allow more sophisticated modelling using machine learning approaches to study relationships between microbiome and various health-related traits. In this STSM, we analysed the application of machine learning in analyses of the human microbiome from the perspective of possible application of machine learning paradigms.

We were interested in the preprocessing methodology pipeline from 16s rRNA data to OTU, the origin of human-based data sources (e.g. skin, gut, saliva), their sample size, the main purpose for using machine learning techniques from a microbiological point of view, the way feature reduction is made, description of machine learning methods, results and obtained performances, as well as their combination with statistical methods.

Microbiome profiling is typically conducted using 16S rRNA amplicon sequencing or shotgun sequencing. The 16S rRNA gene sequences are clustered into groups, namely operational taxonomic unit (OTU) using workflows such as QIIME or mothur. Once OTU clusters are determined, taxonomic information is assigned for the representative sequences of each OTU. Although analytical methods have recently been introduced using representative sequences of 16S rRNA, the OTU cluster-based variables are most frequently used as input features in microbiomes analysis.

Supervised learning methods are typically used to build a model to predict reported categorical outcomes, such as disease affection status. The most popular supervised learning methods used on OTU tables are support vector machine, Naïve Bayes, random forest, and k nearest neighbour methods.

Paper published thanks to the contribution of the work made during this STSM:

1. Tonkovic, Petar, Slobodan Kalajdziski, Eftim Zdravevski, Petre Lameski, Roberto Corizzo, Ivan Miguel Pires, Nuno M. Garcia, Tatjana Loncar-Turukalo, and Vladimir Trajkovik. “Literature on Applied Machine Learning in Metagenomic Classification: A Scoping Review.” Biology 9, no. 12 (2020): 453. https://doi.org/10.3390/biology9120453
2. Marcos-Zambrano, Laura Judith, Kanita Karaduzovic-Hadziabdic, Tatjana Loncar Turukalo, Piotr Przymus, Vladimir Trajkovik, Oliver Aasmets, Magali Berland et al. “Applications of machine learning in human microbiome studies: a review on feature selection, biomarker identification, disease prediction and treatment.” Frontiers in Microbiology 12 (2021): 313. https://doi.org/10.3389/fmicb.2021.634511

Applicant short Bio: Vladimir Trajkovik was born in Skopje, R.N. Macedonia in 1971. He received a PhD degree in 2003 from Ss. Cyril and Methodius University in Skopje. His current position is a professor at the Faculty of Computer Science and Engineering. He has published 4 books as author or editor, more than 60 papers in respectable journals, and more than 160 conference papers. He has more than 1400 citations, with an h-index of 21.

His research interests include: Information Systems Analyses and Design, Distributed Systems, ICT based Collaboration Systems and Mobile services with a special focus on Connected Health.

Unfortunately cancelled.