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Research projects from mens X machina.
Causal AI for Data-Driven Insights and Optimal Decision Making
Development of ETIA, an automated causal discovery software platform embedded within a Causal AI Agent. Given a user's data, ETIA automatically discovers, explains and visualizes the plausible causal relationships, optimizes decisions and performs root-cause analysis, and can converse with the user in natural language.
View project →Machine Learning for Human Microbiome Studies
A COST Action network focusing on the application of machine learning methods to human microbiome research, covering feature selection, biomarker identification, disease prediction, and treatment.
Early Detection of Lung Cancer and Mesothelioma
An international discovery study seeking the earliest molecular signals of lung cancer and mesothelioma in blood serum. The consortium brings together five universities in five countries, aiming to develop a blood test for screening smokers and people exposed to asbestos.
View project →ERC Consolidator Grant — Next Generation Causal Analysis
Advancing causal discovery theory, methods, and tools for robust analysis of real-world data. The project studies how to infer quantitative models of biological pathways from cytometry, other omics data, and prior knowledge, with a focus on human T-cell differentiation.
View project →Uniform Preprocessing of Public Genomic Datasets
Creation of a large database of uniformly preprocessed and disease-annotated genomic and epigenomic data. Currently includes 7,345 datasets spanning 847 diseases, enabling large-scale meta-analysis.
View project →Predictive Explanation of Data Anomalies
Research and development of methods for predictive explanation of data anomalies, resulting in a best paper award at BDA 2021. Produced the DIADEx software tool.
Machine learning for Type 2 Diabetes diagnosis
Application of AutoML to identify circulating cell-free DNA methylation signatures for accurate diagnosis and monitoring of Type 2 Diabetes Mellitus, achieving AUC of 0.927.
Causal-Based Variable Selection for Omics Data
Research on causal-based variable selection for omics data at the Institute of Computer Science, FORTH. Supported through ARISTEIA II under the Education and Lifelong Learning programme, with co-financing from the European Social Fund and national resources.
View project →Multidisciplinary Structural Biology Research in Crete
Strengthening the capacity for multidisciplinary research driven by structural biology in Crete. Coordinated by FORTH, the project supports research excellence and innovation.
View project →An Interactive Learning Environment for Creativity
Developing an interactive learning environment that supports creative problem solving. The project explores visual information mash-ups and collaborative tools for creative work and learning.
View project →Modeling and Analysis of Spectrum Markets
Developing models and simulation tools to study wireless access and spectrum markets. The research examines network services and user experience from the perspectives of providers, customers, and regulators.
View project →Collaborative Research Project
A research project in which members of the mens X machina group participated.
Remote Diabetes Management and Therapy
Developing an integrated platform for diabetes monitoring, management, and therapy across healthcare settings. The project combines glucose monitoring, clinical decision support, and tools for patient self-management.
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