MEDICA: Modelling and vErification of alkaptonuria and multiple sclerosis Driven by biomedICAl data PRIN 2022 Project, 2023. @misc{medica,
title = {MEDICA: Modelling and vErification of alkaptonuria and multiple sclerosis Driven by biomedICAl data},
url = {https://biomedinfo.di.unipi.it/medica-project/},
year = {2023},
date = {2023-09-29},
urldate = {2023-09-29},
abstract = {Abstract: The goal of this project is to bridge the gap between data, models and validation, integrating the data-driven and model-driven research directions, so that to develop next generation modelling and analysis approaches for biomedical investigations. We are tackling open research questions for Multiple Sclerosis (MS) and Alkaptonuria (AKU), for which we have world-leading experts in our consortium.},
howpublished = {PRIN 2022 Project},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Abstract: The goal of this project is to bridge the gap between data, models and validation, integrating the data-driven and model-driven research directions, so that to develop next generation modelling and analysis approaches for biomedical investigations. We are tackling open research questions for Multiple Sclerosis (MS) and Alkaptonuria (AKU), for which we have world-leading experts in our consortium. |
I-POTERI: Innovation in the telemonitoring of the rehabilitation of post-operative cardiac patients Collaboration with the IFC CNR Pisa and several SME Tuscan companies, 2021. @misc{I-POTERI,
title = {I-POTERI: Innovation in the telemonitoring of the rehabilitation of post-operative cardiac patients},
year = {2021},
date = {2021-03-01},
abstract = {Abstract: Cardiac rehabilitation in the post-surgical phase has an important impact on the patient as it speeds up his psychophysical recovery, and improves the clinical picture by reducing the sense of fatigue, dyspnea, and increases survival. In this context, compliance with the rehabilitation program is a key element for the therapeutic benefits and, at the same time, for providing elements for strengthening the patient, in terms of increasing awareness of their own state of health. To achieve these results, the I-POTERI project aims to evolve current telemonitoring systems by developing and industrializing a SW-HW platform based on the integration of home kits (consisting of wearable and low-invasive medical devices), on the some monitoring microservices, and on innovative AI technology, which will allow the continuous monitoring of numerous parameters both during the patient's normal home activities and during rehabilitation activities. The ultimate goal is to customize and optimize the post-surgical rehabilitation process and to allow personalized intervention by the doctor given the parameters of the rehabilitation process and any anomalies of the monitored vital parameters. The project also aims to carry out a retrospective analysis in terms of the effectiveness of the performance and adequacy of the program, thanks also to the application of data analytics algorithms and artificial intelligence techniques.},
howpublished = {Collaboration with the IFC CNR Pisa and several SME Tuscan companies},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Abstract: Cardiac rehabilitation in the post-surgical phase has an important impact on the patient as it speeds up his psychophysical recovery, and improves the clinical picture by reducing the sense of fatigue, dyspnea, and increases survival. In this context, compliance with the rehabilitation program is a key element for the therapeutic benefits and, at the same time, for providing elements for strengthening the patient, in terms of increasing awareness of their own state of health. To achieve these results, the I-POTERI project aims to evolve current telemonitoring systems by developing and industrializing a SW-HW platform based on the integration of home kits (consisting of wearable and low-invasive medical devices), on the some monitoring microservices, and on innovative AI technology, which will allow the continuous monitoring of numerous parameters both during the patient's normal home activities and during rehabilitation activities. The ultimate goal is to customize and optimize the post-surgical rehabilitation process and to allow personalized intervention by the doctor given the parameters of the rehabilitation process and any anomalies of the monitored vital parameters. The project also aims to carry out a retrospective analysis in terms of the effectiveness of the performance and adequacy of the program, thanks also to the application of data analytics algorithms and artificial intelligence techniques. |
SoBigData++ 2021. @misc{SOBIGDATA++,
title = {SoBigData++},
url = {https://cordis.europa.eu/project/id/871042/it},
year = {2021},
date = {2021-02-01},
abstract = {SoBigData++ strives to deliver a distributed, Pan-European, multi-disciplinary research infrastructure for big social data analytics, coupled with the consolidation of a cross-disciplinary European research community, aimed at using social mining and big data to understand the complexity of our contemporary, globally-interconnected society. SoBigData++ will move forward from a starting community of pioneers to a wide and diverse scientific movement, capable of empowering the next generation of responsible social data scientists, engaged in the grand societal challenges laid out in its exploratories: Societal Debates and Online Misinformation, Sustainable Cities for Citizens, Demography, Economics & Finance 2.0, Migration Studies, Sports Data Science, Social Impact of Artificial Intelligence and Explainable Machine Learning. In particular, the exploratory on Sports Data Science focuses on research related to sports and health analytics. },
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
SoBigData++ strives to deliver a distributed, Pan-European, multi-disciplinary research infrastructure for big social data analytics, coupled with the consolidation of a cross-disciplinary European research community, aimed at using social mining and big data to understand the complexity of our contemporary, globally-interconnected society. SoBigData++ will move forward from a starting community of pioneers to a wide and diverse scientific movement, capable of empowering the next generation of responsible social data scientists, engaged in the grand societal challenges laid out in its exploratories: Societal Debates and Online Misinformation, Sustainable Cities for Citizens, Demography, Economics & Finance 2.0, Migration Studies, Sports Data Science, Social Impact of Artificial Intelligence and Explainable Machine Learning. In particular, the exploratory on Sports Data Science focuses on research related to sports and health analytics. |
High-Tech House Garden (HTHG) 2021. @misc{HTHG,
title = {High-Tech House Garden (HTHG)},
url = {https://www.serra-hthg.it/},
year = {2021},
date = {2021-02-01},
abstract = {The project aims to respond to the need to increase the technological level through the construction of a high-tech greenhouse integrated with sensor and home automation technologies, controlled through an ICT (information and communication technology) approach, for the controlled agronomic development of horticultural and horticultural crops. , supported by innovative technologies to stimulate the growth and development of plants by increasing the efficiency of use of agrochemicals without further use of synthetic products.
In particular, the HT greenhouse will be designed to be able to manage in a controlled and effective, efficient and functional way, different types of crops with different cultivation needs (eg in soil and soilless). The HT greenhouse will be a versatile and multifunctional environment, equipped with sensors and monitoring systems that will allow the acquisition of data and information that a specially prepared "computer brain" will process in order to perform effective active controls (eg in retro-action), thus optimizing crop management and providing a useful decision support tool.},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
The project aims to respond to the need to increase the technological level through the construction of a high-tech greenhouse integrated with sensor and home automation technologies, controlled through an ICT (information and communication technology) approach, for the controlled agronomic development of horticultural and horticultural crops. , supported by innovative technologies to stimulate the growth and development of plants by increasing the efficiency of use of agrochemicals without further use of synthetic products.
In particular, the HT greenhouse will be designed to be able to manage in a controlled and effective, efficient and functional way, different types of crops with different cultivation needs (eg in soil and soilless). The HT greenhouse will be a versatile and multifunctional environment, equipped with sensors and monitoring systems that will allow the acquisition of data and information that a specially prepared "computer brain" will process in order to perform effective active controls (eg in retro-action), thus optimizing crop management and providing a useful decision support tool. |
ALPACA (ALgorithms for PAngenome Computational Analysis) EU funded Innovative Training Network (ITN), 2021. @misc{ALPACA_21,
title = {ALPACA (ALgorithms for PAngenome Computational Analysis)},
url = {https://alpaca-itn.eu},
year = {2021},
date = {2021-01-01},
abstract = {ALPACA (ALgorithms for PAngenome Computational Analysis) is an EU funded Innovative Training Network for talented PhD Students.
In view of ultra-large amounts of genome sequence data emerging from rapidly advancing genome sequencing devices the driving, urgent question is: How can we arrange and analyze these data masses in a formally rigorous, computationally efficient and biomedically rewarding manner?
Graph based data structures have been pointed out to have disruptive benefits over traditional sequence based structures when representing pan-genomes. This paradigm shift from sequences to graphs requires to make substantial advances in terms of algorithms and data structures.},
howpublished = {EU funded Innovative Training Network (ITN)},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
ALPACA (ALgorithms for PAngenome Computational Analysis) is an EU funded Innovative Training Network for talented PhD Students.
In view of ultra-large amounts of genome sequence data emerging from rapidly advancing genome sequencing devices the driving, urgent question is: How can we arrange and analyze these data masses in a formally rigorous, computationally efficient and biomedically rewarding manner?
Graph based data structures have been pointed out to have disruptive benefits over traditional sequence based structures when representing pan-genomes. This paradigm shift from sequences to graphs requires to make substantial advances in terms of algorithms and data structures. |
TEACHING: A computing toolkit for building efficient autonomous applications leveraging humanistic intelligence (2020-2022) Funder: European Community (HORIZON 2020), 2020. @misc{TEACHING,
title = {TEACHING: A computing toolkit for building efficient autonomous applications leveraging humanistic intelligence (2020-2022)},
year = {2020},
date = {2020-12-31},
abstract = {A computing Toolkit for building Efficient Autonomous appliCations leveraging Humanistic INtelliGence is an EU-funded project that designs a computing platform and the associated software toolkit supporting the development and deployment of autonomous, adaptive and dependable CPSoS applications, allowing them to exploit a sustainable human feedback to drive, optimize and personalize the provisioning of their services.
Involves Machine Learning for Human Activity Recognition, Human State Monitoring and stress detection},
howpublished = {Funder: European Community (HORIZON 2020)},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
A computing Toolkit for building Efficient Autonomous appliCations leveraging Humanistic INtelliGence is an EU-funded project that designs a computing platform and the associated software toolkit supporting the development and deployment of autonomous, adaptive and dependable CPSoS applications, allowing them to exploit a sustainable human feedback to drive, optimize and personalize the provisioning of their services.
Involves Machine Learning for Human Activity Recognition, Human State Monitoring and stress detection |
PreMed2 Funder: Regione Toscana, Bando Salute, 2020. @misc{PreMed2,
title = {PreMed2},
year = {2020},
date = {2020-12-29},
abstract = {The main purpose of this project is to identify the population of subjects at risk of developing type 2 diabetes based on eating habits (using validated questionnaires), physical activity (using wearable devices) and metabolic profile.
The BioSystems Modeling group is involved in data analysis with the application of systems biology approaches.
},
howpublished = {Funder: Regione Toscana, Bando Salute},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
The main purpose of this project is to identify the population of subjects at risk of developing type 2 diabetes based on eating habits (using validated questionnaires), physical activity (using wearable devices) and metabolic profile.
The BioSystems Modeling group is involved in data analysis with the application of systems biology approaches.
|
BrAID - Brugada syndrome and Artificial Intelligence applications to Diagnosis (2020-2023) Funder: Regione Toscana, 2020. @misc{braid20,
title = {BrAID - Brugada syndrome and Artificial Intelligence applications to Diagnosis (2020-2023)},
year = {2020},
date = {2020-12-29},
abstract = {Developoment of Machine Learning models for the identification/detection of patterns related to the BrS diagnosis from the ECG},
howpublished = {Funder: Regione Toscana},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Developoment of Machine Learning models for the identification/detection of patterns related to the BrS diagnosis from the ECG |
CLAIRE Taskforce on COVID-19 2020. @misc{CLAIRE_covid19,
title = {CLAIRE Taskforce on COVID-19},
url = {https://covid19.claire-ai.org/},
year = {2020},
date = {2020-12-29},
abstract = {CLAIRE Taskforce on COVID-19},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
CLAIRE Taskforce on COVID-19 |
Metodi Informatici Integrati per la Biomedica PRA – Progetti di Ricerca di Ateneo (Institutional Research Grants) - Project no. PRA_2020-2021_26 , 2020. @misc{PRA_2020,
title = {Metodi Informatici Integrati per la Biomedica},
year = {2020},
date = {2020-12-29},
abstract = {This project aims at integrating different computer methodologies that contribute to interdisciplinary research in bioinformatics and, in particular, to its applications in the biomedical field. This in order to experiment the co-existence of algorithmic, modeling, and machine learning methods in the analysis of biomedical data and, at the same time, to refine the synergy of the many research groups that have been active in the Department of Computer Science for decades.},
howpublished = {PRA – Progetti di Ricerca di Ateneo (Institutional Research Grants) - Project no. PRA_2020-2021_26 },
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
This project aims at integrating different computer methodologies that contribute to interdisciplinary research in bioinformatics and, in particular, to its applications in the biomedical field. This in order to experiment the co-existence of algorithmic, modeling, and machine learning methods in the analysis of biomedical data and, at the same time, to refine the synergy of the many research groups that have been active in the Department of Computer Science for decades. |
MOTIF-RAPTOR Collab. with Massachusetts General Hospital, Harvard Medical School, Broad Institute of MIT and Harvard, 2020. @misc{MOTIF-RAPTOR,
title = {MOTIF-RAPTOR},
year = {2020},
date = {2020-12-28},
abstract = {This project aims at designing a computational tool which hinges on Transcription Factors (TF) and integrates sequence-based predictive models, chromatin accessibility, gene expression datasets and GWAS summary statistics to systematically investigate how TF function is affected by genetic variants. We have successfully tested Motif-Raptor on complex traits such as rheumatoid arthritis and red blood cell count and demonstrated its ability to prioritize relevant cell types, potential regulatory TFs and non-coding SNPs which have been previously characterized and validated.},
howpublished = {Collab. with Massachusetts General Hospital, Harvard Medical School, Broad Institute of MIT and Harvard},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
This project aims at designing a computational tool which hinges on Transcription Factors (TF) and integrates sequence-based predictive models, chromatin accessibility, gene expression datasets and GWAS summary statistics to systematically investigate how TF function is affected by genetic variants. We have successfully tested Motif-Raptor on complex traits such as rheumatoid arthritis and red blood cell count and demonstrated its ability to prioritize relevant cell types, potential regulatory TFs and non-coding SNPs which have been previously characterized and validated. |
PANS Collaboration with Stanford Medical School, 2020. @misc{PANS,
title = {PANS},
year = {2020},
date = {2020-12-28},
abstract = {This project aims to uncover the mechanisms involved in the development and remission of symptoms for PANS (pediatric acute-onset neuropsychiatric syndrome). It involves analysis of proteomic and metabolomic data for a set of PANS patients at different disease stages and under different treatments.},
howpublished = {Collaboration with Stanford Medical School},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
This project aims to uncover the mechanisms involved in the development and remission of symptoms for PANS (pediatric acute-onset neuropsychiatric syndrome). It involves analysis of proteomic and metabolomic data for a set of PANS patients at different disease stages and under different treatments. |
Activity and Sleep monitoring Collaboration with Biobeats and UNIPI Pharmacy Department, 2020. @misc{sleep_monitoring,
title = {Activity and Sleep monitoring},
year = {2020},
date = {2020-12-28},
abstract = {This project explores the interaction between physical and psychological characteristics of healthy individuals, by monitoring various parameters in a set of volunteers, leading to a multi-level analysis. The parameters include accelerometer data, sleep quality, hormone levels, heart rate, stress, emotions, and others, and can provide a wide view of how different processes interact with each other during daily life.},
howpublished = {Collaboration with Biobeats and UNIPI Pharmacy Department},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
This project explores the interaction between physical and psychological characteristics of healthy individuals, by monitoring various parameters in a set of volunteers, leading to a multi-level analysis. The parameters include accelerometer data, sleep quality, hormone levels, heart rate, stress, emotions, and others, and can provide a wide view of how different processes interact with each other during daily life. |
PROMET (SPARK Project) Collaboration with the Pisa University Hospital Oncology Unit , 2020. @misc{PROMET,
title = {PROMET (SPARK Project)},
year = {2020},
date = {2020-12-28},
abstract = {Circulating tumor DNA can be detected in cancer patients after tumor resection. This project aims to evaluate whether sequencing of circulating tumor DNA can be employed in prognosis for cancer patients, concentrating on patients with colorectal cancer liver metastasis. This consists of both survival prediction and selection of prognosis biomarkers.},
howpublished = {Collaboration with the Pisa University Hospital Oncology Unit },
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Circulating tumor DNA can be detected in cancer patients after tumor resection. This project aims to evaluate whether sequencing of circulating tumor DNA can be employed in prognosis for cancer patients, concentrating on patients with colorectal cancer liver metastasis. This consists of both survival prediction and selection of prognosis biomarkers. |
COVID-19 Collaboration with the Pisa University Hospital and CNR Pisa, 2020. @misc{covid19,
title = {COVID-19},
year = {2020},
date = {2020-12-28},
abstract = {Clinical data for COVID-19 patients can be employed to predict disease progression and final outcomes. However, the quantity of clinical variables and missing data may pose challenges. We are developing a method to select the most important clinical variables and to predict mortality at hospitalisation time.},
howpublished = {Collaboration with the Pisa University Hospital and CNR Pisa},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Clinical data for COVID-19 patients can be employed to predict disease progression and final outcomes. However, the quantity of clinical variables and missing data may pose challenges. We are developing a method to select the most important clinical variables and to predict mortality at hospitalisation time. |
DROP (SPARK project) Collaboration with the University of Pisa Medical School, 2020. @misc{DROP,
title = {DROP (SPARK project)},
year = {2020},
date = {2020-12-28},
abstract = {While whole slide imaging is providing detailed information to pathologists, diagnosis and therapeutic decisions are still mostly based on expert evaluation of the digital images, which is subjective introducing errors and tampering with reproducibility. This project aims to develop a prototype tool that can aid the evaluation of biomarkers in breast tumour tissues, which employs image analysis and machine learning. },
howpublished = {Collaboration with the University of Pisa Medical School},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
While whole slide imaging is providing detailed information to pathologists, diagnosis and therapeutic decisions are still mostly based on expert evaluation of the digital images, which is subjective introducing errors and tampering with reproducibility. This project aims to develop a prototype tool that can aid the evaluation of biomarkers in breast tumour tissues, which employs image analysis and machine learning. |
Vaccination coverage prediction for Meningitis B Collaboration with GlaxoSmithKlein, 2020. @misc{meningitisB,
title = {Vaccination coverage prediction for Meningitis B},
year = {2020},
date = {2020-12-28},
abstract = {The project aims to identify genomic characteristics of Meningococcus B strains that predict whether an existing vaccine is able to protect against new isolates of the bacterium. While the state of the art uses pure statistical tools to predict efficiency, this project aims to employ machine learning tools based on Deep Learning. The hope is that these methods, being based on complete genetic sequences, will be more generic and translatable to other vaccines.},
howpublished = {Collaboration with GlaxoSmithKlein},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
The project aims to identify genomic characteristics of Meningococcus B strains that predict whether an existing vaccine is able to protect against new isolates of the bacterium. While the state of the art uses pure statistical tools to predict efficiency, this project aims to employ machine learning tools based on Deep Learning. The hope is that these methods, being based on complete genetic sequences, will be more generic and translatable to other vaccines. |
Protein selection for vaccine development Collaboration with GlaxoSmithKlein, 2020. @misc{vaccine,
title = {Protein selection for vaccine development},
year = {2020},
date = {2020-12-28},
abstract = {Vaccine development typically includes a preliminary in silico selection of proteins that can be promising candidates to be included in the vaccine. Current approaches start by annotating proteins with various parameters and then selecting based on the annotations. This project aims to eliminate the intermediate phase of annotating the sequences, by considering the protein sequences directly as input to a classifier.},
howpublished = {Collaboration with GlaxoSmithKlein},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Vaccine development typically includes a preliminary in silico selection of proteins that can be promising candidates to be included in the vaccine. Current approaches start by annotating proteins with various parameters and then selecting based on the annotations. This project aims to eliminate the intermediate phase of annotating the sequences, by considering the protein sequences directly as input to a classifier. |
Covid-19 morbidity and mortality in type 2 diabetes mellitus: possible roles of macrophages and of glucose lowering drugs Collaboration with University of Parma, 2020. @misc{covid-diabetes,
title = {Covid-19 morbidity and mortality in type 2 diabetes mellitus: possible roles of macrophages and of glucose lowering drugs},
year = {2020},
date = {2020-12-28},
abstract = {The Severe Acute Respiratory Syndrome-Coronavirus-2 (SARS-CoV-2) causes COVID-19, which, if severe, features interstitial pneumonia, multiple organ damage, macrophage (MΦ) activation syndrome and vascular thrombosis. People with type 2 diabetes (T2DM) may be more vulnerable to severe COVID-19.
AIMS:
1. To assess the independent impact of T2DM on the risk of death or transfer to intensive care unit (ICU) of patients admitted to the Covid-1 macro-unit of Ospedale Maggiore.
2. To assess, in a SARS-CoV-dependent model of inflamed human MΦ:
a. the role of T2DM and of some anti-diabetes agents (ADA) in modulating MΦ inflammation;
b. the effects of the MΦ secretome on lung microvascular endothelium.},
howpublished = {Collaboration with University of Parma},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
The Severe Acute Respiratory Syndrome-Coronavirus-2 (SARS-CoV-2) causes COVID-19, which, if severe, features interstitial pneumonia, multiple organ damage, macrophage (MΦ) activation syndrome and vascular thrombosis. People with type 2 diabetes (T2DM) may be more vulnerable to severe COVID-19.
AIMS:
1. To assess the independent impact of T2DM on the risk of death or transfer to intensive care unit (ICU) of patients admitted to the Covid-1 macro-unit of Ospedale Maggiore.
2. To assess, in a SARS-CoV-dependent model of inflamed human MΦ:
a. the role of T2DM and of some anti-diabetes agents (ADA) in modulating MΦ inflammation;
b. the effects of the MΦ secretome on lung microvascular endothelium. |
Multicriteria Data Structures and Algorithms: from compressed to learned indexes, and beyond. PRIN no. 2017WR7SHH, 2019. @misc{PRIN2017,
title = {Multicriteria Data Structures and Algorithms: from compressed to learned indexes, and beyond.},
url = {http://learned.di.unipi.it/},
year = {2019},
date = {2019-09-01},
abstract = {The ever growing need to efficiently store, retrieve and analyze massive datasets, is currently made more complex by the different requirements posed by users, devices and applications. Such a new level of complexity cannot be handled properly by current data structures for big data problems. This project lays down the theoretical and algorithmic-engineering foundations of a new generation of Multicriteria Data Structures and Algorithms that will seamlessly integrate, via a principled optimization approach, modern compressed data structures with new, revolutionary, data structures learned from the input data by using proper machine-learning tools, with applications to key-value stores and bioinformatics tools.},
howpublished = {PRIN no. 2017WR7SHH},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
The ever growing need to efficiently store, retrieve and analyze massive datasets, is currently made more complex by the different requirements posed by users, devices and applications. Such a new level of complexity cannot be handled properly by current data structures for big data problems. This project lays down the theoretical and algorithmic-engineering foundations of a new generation of Multicriteria Data Structures and Algorithms that will seamlessly integrate, via a principled optimization approach, modern compressed data structures with new, revolutionary, data structures learned from the input data by using proper machine-learning tools, with applications to key-value stores and bioinformatics tools. |
