Bridging Artificial Intelligence and Machine Learning with bioimage analysis
The AI4Life project launched today, marks an exciting chapter in the computational and life science research communities. The €4 million Horizon Europe funded project aims to create accessible, harmonized, and interoperable AI tools and methods for solving today’s microscopy image analysis problems.
The gap
Machine learning (ML) has accelerated frontier research in the life sciences, but democratized access to such methods is not a given. Limited access to necessary hardware/software and expertise combined with insufficiently documented methods hinder life science researchers from harnessing the power of such tools. Furthermore, while modern Artificial Intelligence (AI)-based methods typically generalize well to unseen data, no standard exists for sharing and fine-tuning pretrained models between different analysis tools. Compounding the issue, existing user-facing platforms operate entirely independently, often failing to comply with FAIR1 data and Open Science standards. Furthermore, the staggering pace of AI and ML development make it impossible for the non- specialist to stay up to date. Hence, urgent services and infrastructures to solve such problems are required to expand cutting edge life science research.
1 FAIR: Findable, Accessible, Interoperable, Reusable. https://doi.org/10.1038/sdata.2016.18
The bridge
The 10-partner consortium will build an open, accessible, community-driven repository of FAIR pre- trained AI models and develop services to deliver these models to life scientists, including those without substantial computational expertise. AI4Life will provide direct support and ample trainingactivities to prepare life scientists for responsible use of AI methods. Additionally, AI4Life will drive community contributions of new models and interoperability between analysis tools. AI4Life will also facilitate Open calls and public Challenges aimed at providing state-of-the-art solutions to unsolved image analysis problems in life science research.
AI4Life brings together AI/ML researchers, developers of open-source image analysis tools, providers of European-scale storage and compute services, and European life science Research Infrastructures – all united behind the common goal to enable life scientists to benefit from the untapped, tremendous power of AI-based analysis methods.
The core objectives
- Democratize availability of AI-based image analysis methods
- Establish standards for the submission, storage, and FAIR access
- Simple model deployment, sharing and dissemination through a new developer-facing service
- Organize Open calls and Challenges for image analysis problems
- Empower common image analysis platforms with AI integration
- Organizing outreach and training events
The team
Our multidisciplinary team of experts in computational and life sciences as well as 3 European Research Infrastructures.
Follow the work of AI4Life on twitter “@AI4LifeTeam and check-out the AI4Life website for more information.
Share:
You could also like:
-
Italy at the centre of the Genome of Europe project
The Human Technopole, ELIXIR Italia, the national node of the European life sciences research infrastructure coordinated by the National Research Council (CNR), and the Centro Cardiologico Monzino, as the Italian coordinating centre, have been selected as the Italian partners of Genome of Europe (GoE), the largest EU-funded genomic project, whose ultimate goal is to make […]
-
Human Technopole wins 2024 Oscar di Bilancio
On Friday 13 December, at Palazzo Mezzanotte in Milan, the Human Technopole Foundation’s ‘Integrated Report 2023’ received the Oscar di Bilancio in the social enterprises and non-profit organisations category. The award was presented to President Gianmario Verona, Elena Trovesi, Head of Administration, as well as the project leaders Giovanni Selmi, Head of Finance, and Alessandro […]
-
Mosaic Organoids to Study the Brains of Multiple Patients Simultaneously
An international team of scientists from Human Technopole and the University of Milan has developed and validated an innovative approach to studying human brain development across multiple individuals simultaneously using single organoids—laboratory models that replicate key cellular processes of human neurodevelopment. The research paves the way for in vitro population studies. Additionally, the scientists have developed a novel computational method to more accurately quantify the genetic identity of individual cells profiled from multiple individuals concurrently. The findings have been published in Nature Methods.
-
Cancer stem cell morphology predicts glioblastoma growth and resistance
Human Technopole researchers have identified adducin-γ (ADD3) as a crucial regulator of glioblastoma cancer stem cell morphology and intercellular bridges between tumour cells. These connections facilitate communication and allow tumour cells to share resources, evade chemotherapy, and survive in challenging conditions. The study has been funded by AIRC and the findings are published in Life Science Alliance.
-
Colorectal Cancer: ML Enhances Cetuximab Response Predictions
An international collaborative study led by Human Technopole, Candiolo Cancer Institute IRCCS in Turin, the University of Turin, and the Wellcome Sanger Institute in Cambridge (UK) has identified new factors associated with therapeutic response in colorectal cancer. The research has led to the development of a machine-learning model capable of accurately predicting the effects of cetuximab, a drug in clinical use, on different colorectal tumour subtypes. Funded by the AIRC Foundation, the study paves the way to identifying molecular features that could serve as biomarkers for predicting treatment response in patients with this type of cancer.