NEST projects
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Some research projects within WASP aim to build broad ground theory, while others are narrow and breaching the very frontier of our knowledge, and yet others are somewhere in between. Many projects are collaborative efforts, as seen below.
On this page you can read more about the various WASP-funded projects.
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During 2022, WASP and the SciLifeLab and Wallenberg National Program on Data-Driven Life Science (DDLS), the two largest research programs in Sweden, launched a second joint call with the aim of solving ground-breaking research questions across their different scientific disciplines. In total, 13 applications were awarded grants for two-year projects.
Researchers
Nina Linder (UU), Claes Lundström (LiU)
Cervical cancer is the fourth most common cancer among women worldwide, and about 90% of the new cases and deaths occur in low- and middle-income countries. Linder et al have previously developed an AI-based point-of-care (POC) diagnostic system for cervical cancer screening in resource-limited settings. However, domain shift poses challenges for large-scale implementation of AI-based diagnostic methods, as AI systems may yield unreliable predictions when encountering data that differs from the training data.
To develop computational methods based on uncertainty estimation, to tackle domain shift challenges in AI applications for digital cytology and to integrate the methods into a workflow in a prospective study for improved human-and-machine interplay in the diagnostic process.
The project will explore methods to mitigate the domain shift challenges, focusing on uncertainty estimation to increase prediction robustness or detect mispredictions. A prototype clinical viewer, including uncertainty estimations will be evaluated within a prospective clinical study. The assumption is that uncertainty predictions will allow human experts to focus on challenging cases, while high-confidence AI predictions require minimal human intervention.
The project has significant societal and industrial impact potential by enabling more accurate, efficient, and accessible diagnostics for cervical cancer, also in resource-limited settings.
Researchers
Volker Lauschke (KI), Ming Xiao (KTH)
Chemotherapy is essential in the treatment of several cancers; however, drug resistance remains a substantial problem. To predict chemotherapy response, certain genetic variations in the drug transporter genes MDR1, MRP1 and BCRP are currently used as clinically established biomarkers. In a recent study, genomic data from >130,000 individuals were analyzed, revealing more than 3000 novel genetic variations in these transporters with unclear functional consequences.
Develop a machine learning based tool for pharmacogenetic predictions of chemotherapy response in cancer treatment.
A substrate-specific functionality profile of all genetic variants of human drug transporters will be generated using deep mutational scanning followed by phenotypic selection in cancer cells. The resulting data set will be used for the development of graph neural network (GNN)-based learning schemes for pharmacogenetic predictions of drug response.
This project will facilitate the translation of an individual’s genomic information into chemotherapeutic sensitivity profiles, providing an important mechanistic link between rare genetic variations, transporter function and clinical outcomes. The long-term goal is to allow for more individualized therapy strategies to increase patient survival, but also to decrease the risk of severe side-effects treatments.
During 2021, WASP and the SciLifeLab and Wallenberg National Program on Data-Driven Life Science (DDLS), the two largest research programs in Sweden, launched a joint call with the aim of solving ground-breaking research questions across their different scientific disciplines. In total, 15 applications were awarded grants for two-year projects.
During 2022, WASP and the SciLifeLab and Wallenberg National Program on Data-Driven Life Science (DDLS), the two largest research programs in Sweden, launched a second joint call with the aim of solving ground-breaking research questions across their different scientific disciplines. In total, 13 applications were awarded grants for two-year projects.
Researchers
Nina Linder (UU), Claes Lundström (LiU)
Cervical cancer is the fourth most common cancer among women worldwide, and about 90% of the new cases and deaths occur in low- and middle-income countries. Linder et al have previously developed an AI-based point-of-care (POC) diagnostic system for cervical cancer screening in resource-limited settings. However, domain shift poses challenges for large-scale implementation of AI-based diagnostic methods, as AI systems may yield unreliable predictions when encountering data that differs from the training data.
To develop computational methods based on uncertainty estimation, to tackle domain shift challenges in AI applications for digital cytology and to integrate the methods into a workflow in a prospective study for improved human-and-machine interplay in the diagnostic process.
The project will explore methods to mitigate the domain shift challenges, focusing on uncertainty estimation to increase prediction robustness or detect mispredictions. A prototype clinical viewer, including uncertainty estimations will be evaluated within a prospective clinical study. The assumption is that uncertainty predictions will allow human experts to focus on challenging cases, while high-confidence AI predictions require minimal human intervention.
The project has significant societal and industrial impact potential by enabling more accurate, efficient, and accessible diagnostics for cervical cancer, also in resource-limited settings.
Researchers
Volker Lauschke (KI), Ming Xiao (KTH)
Chemotherapy is essential in the treatment of several cancers; however, drug resistance remains a substantial problem. To predict chemotherapy response, certain genetic variations in the drug transporter genes MDR1, MRP1 and BCRP are currently used as clinically established biomarkers. In a recent study, genomic data from >130,000 individuals were analyzed, revealing more than 3000 novel genetic variations in these transporters with unclear functional consequences.
Develop a machine learning based tool for pharmacogenetic predictions of chemotherapy response in cancer treatment.
A substrate-specific functionality profile of all genetic variants of human drug transporters will be generated using deep mutational scanning followed by phenotypic selection in cancer cells. The resulting data set will be used for the development of graph neural network (GNN)-based learning schemes for pharmacogenetic predictions of drug response.
This project will facilitate the translation of an individual’s genomic information into chemotherapeutic sensitivity profiles, providing an important mechanistic link between rare genetic variations, transporter function and clinical outcomes. The long-term goal is to allow for more individualized therapy strategies to increase patient survival, but also to decrease the risk of severe side-effects treatments.
During 2021, WASP and the SciLifeLab and Wallenberg National Program on Data-Driven Life Science (DDLS), the two largest research programs in Sweden, launched a joint call with the aim of solving ground-breaking research questions across their different scientific disciplines. In total, 15 applications were awarded grants for two-year projects.
Researchers
Nina Linder (UU), Claes Lundström (LiU)
Background
Cervical cancer is the fourth most common cancer among women worldwide, and about 90% of the new cases and deaths occur in low- and middle-income countries. Linder et al have previously developed an AI-based point-of-care (POC) diagnostic system for cervical cancer screening in resource-limited settings. However, domain shift poses challenges for large-scale implementation of AI-based diagnostic methods, as AI systems may yield unreliable predictions when encountering data that differs from the training data.
Aim
To develop computational methods based on uncertainty estimation, to tackle domain shift challenges in AI applications for digital cytology and to integrate the methods into a workflow in a prospective study for improved human-and-machine interplay in the diagnostic process.
Methods
The project will explore methods to mitigate the domain shift challenges, focusing on uncertainty estimation to increase prediction robustness or detect mispredictions. A prototype clinical viewer, including uncertainty estimations will be evaluated within a prospective clinical study. The assumption is that uncertainty predictions will allow human experts to focus on challenging cases, while high-confidence AI predictions require minimal human intervention.
Significance
The project has significant societal and industrial impact potential by enabling more accurate, efficient, and accessible diagnostics for cervical cancer, also in resource-limited settings.
