CA18131 - Statistical and machine learning techniques in human microbiomestudies (ML4Microbiome)

This COST Action network will create productive symbiosis between discovery-oriented microbiome researchers and data-driven ML experts, through regular meetings, workshops and training courses. Together, it will first optimise and then standardise the use of said techniques, following the creation of publicly available benchmark datasets. Correct usage of these approaches will allow for better identification of predictive and discriminatory ‘omics’ features, increase study repeatability, and provide mechanistic insights into possible causal or contributing roles of the microbiome.

Team member:

Naida Babić Jordamović, Research Associate 

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CA21116 - Identification of biological markers for prevention and translational medicine in pancreatic cancer (TRANSPAN)

Pancreatic cancer (PC) has a high mortality rate and is projected to become a massive public health problem in Europe. This Action will boost research on prevention of PC, particularly in the discovery of genetic risk factors, risk stratification, identification of biomarkers for early detection and patient monitoring, elucidation of biological mechanisms and functional pharmacogenomics for personalized medicine. These aims will be attained by expanding an existing interdisciplinary network.

Team member:

Merima Smajlhodžić Deljo, Senior Expert Associate

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Research on the Application of Artificial Intelligence for Sustainable Digital Transformation and Innovation in the Agricultural Sector in Bosnia and Herzegovina

The project "Research on the Application of Artificial Intelligence for Sustainable Digital Transformation and Innovation in the Agricultural Sector in Bosnia and Herzegovina" aims to explore and apply artificial intelligence (AI) concepts to encourage sustainable digital transformation and innovation within the agricultural sector in Bosnia and Herzegovina. By establishing collaboration between a scientific research institute and agricultural enterprises, an efficient and sustainable system for transferring new information, knowledge, and technologies into agricultural practice is achieved.

In the context of increasingly pronounced challenges facing the agricultural sector, such as climate change, resource constraints, and the need for increased productivity, artificial intelligence offers exceptional potential to address these issues. By combining advanced technologies such as machine learning, deep learning, and data analytics, the project will explore ways to apply these techniques to specific agricultural challenges in Bosnia and Herzegovina. Through the analysis of detailed data on climatic conditions, soil, and crop history, the project will provide farmers with necessary information to make informed decisions about optimal crops for planting in specific temporal and geographical contexts. This approach will not only significantly increase crop yields but also significantly reduce the need for resources such as water, fertilizers, and pesticides, thereby improving the sustainability and environmental awareness of agricultural production. Through the implementation of artificial intelligence in this sector, the project aims to achieve efficiency, increased productivity, and long-term improvement in agricultural practices.

Team member:

Merima Smajlhodžić Deljo, Senior Expert Associate

Project Team Lead

PM Particles and Artificial Intelligence: Unraveling the Links Between Exposure and Human Health Conditions

pAIm is a research project aimed at developing an expert decision-making system based on artificial intelligence to detect the relationship between exposure to PM particles and their health effects on humans. At the core of the proposed project's research is the prediction of DNA damage leading to the development of diseases caused by exposure to air-polluting PM particles. The developmental component of this project involves establishing the material-technical infrastructure necessary for utilizing large data structures and artificial intelligence at the Verlab Institute.

The research component of the project focuses on determining DNA damage in respiratory patients, individuals with allergic reactions, and athletes after exposure to PM particles. The project contributes to strengthening the scientific research capacity of researchers at the Verlab Institute who will be able to support the implementation and use of this and similar innovative artificial intelligence systems in the healthcare system of the Federation of Bosnia and Herzegovina. The research presented in this project is based on a connectivist approach, i.e., the methodology of developing artificial intelligence systems, which includes the analysis of data in its original form, defining the technical specifications of the artificial intelligence system, training the artificial intelligence system, and validating the performance of the developed system.

The project team consists of 3 members from the Verlab Institute (including the project leader), external collaborators: 2 representatives from the healthcare sector of Eurofarm, 1 researcher from the International University of Sarajevo (IUS), and consultants: a researcher from the University of Donja Gorica, a collaborator from the University of Napoli Federico II, Italy, and a collaborator from the University of Campus Biomedico, Italy.

Team member:

Adna Softić, Expert Associate

Project Team Lead