Project acronym BIOTENSORS
Project Biomedical Data Fusion using Tensor based Blind Source Separation
Researcher (PI) Sabine Jeanne A Van Huffel
Host Institution (HI) KATHOLIEKE UNIVERSITEIT LEUVEN
Call Details Advanced Grant (AdG), PE6, ERC-2013-ADG
Summary "Summary: the quest for a general functional tensor framework for blind source separation
Our overall objective is the development of a general functional framework for solving tensor based blind source separation (BSS) problems in biomedical data fusion, using tensor decompositions (TDs) as basic core. We claim that TDs will allow the extraction of fairly complicated sources of biomedical activity from fairly complicated sets of uni- and multimodal data. The power of the new techniques will be demonstrated for three well-chosen representative biomedical applications for which extensive expertise and fully validated datasets are available in the PI’s team, namely:
• Metabolite quantification and brain tumour tissue typing using Magnetic Resonance Spectroscopic Imaging,
• Functional monitoring including seizure detection and polysomnography,
• Cognitive brain functioning and seizure zone localization using simultaneous Electroencephalography-functional MR Imaging integration.
Solving these challenging problems requires that algorithmic progress is made in several directions:
• Algorithms need to be based on multilinear extensions of numerical linear algebra.
• New grounds for separation, such as representability in a given function class, need to be explored.
• Prior knowledge needs to be exploited via appropriate health relevant constraints.
• Biomedical data fusion requires the combination of TDs, coupled via relevant constraints.
• Algorithms for TD updating are important for continuous long-term patient monitoring.
The algorithms are eventually integrated in an easy-to-use open source software platform that is general enough for use in other BSS applications.
Having been involved in biomedical signal processing over a period of 20 years, the PI has a good overview of the field and the opportunities. By working directly at the forefront in close collaboration with the clinical scientists who actually use our software, we can have a huge impact."
Summary
"Summary: the quest for a general functional tensor framework for blind source separation
Our overall objective is the development of a general functional framework for solving tensor based blind source separation (BSS) problems in biomedical data fusion, using tensor decompositions (TDs) as basic core. We claim that TDs will allow the extraction of fairly complicated sources of biomedical activity from fairly complicated sets of uni- and multimodal data. The power of the new techniques will be demonstrated for three well-chosen representative biomedical applications for which extensive expertise and fully validated datasets are available in the PI’s team, namely:
• Metabolite quantification and brain tumour tissue typing using Magnetic Resonance Spectroscopic Imaging,
• Functional monitoring including seizure detection and polysomnography,
• Cognitive brain functioning and seizure zone localization using simultaneous Electroencephalography-functional MR Imaging integration.
Solving these challenging problems requires that algorithmic progress is made in several directions:
• Algorithms need to be based on multilinear extensions of numerical linear algebra.
• New grounds for separation, such as representability in a given function class, need to be explored.
• Prior knowledge needs to be exploited via appropriate health relevant constraints.
• Biomedical data fusion requires the combination of TDs, coupled via relevant constraints.
• Algorithms for TD updating are important for continuous long-term patient monitoring.
The algorithms are eventually integrated in an easy-to-use open source software platform that is general enough for use in other BSS applications.
Having been involved in biomedical signal processing over a period of 20 years, the PI has a good overview of the field and the opportunities. By working directly at the forefront in close collaboration with the clinical scientists who actually use our software, we can have a huge impact."
Max ERC Funding
2 500 000 €
Duration
Start date: 2014-04-01, End date: 2019-03-31
Project acronym DOUBLE-TROUBLE
Project Replaying the ‘genome duplication’ tape of life: the importance of polyploidy for adaptation in a changing environment
Researcher (PI) Yves VAN DE PEER
Host Institution (HI) VIB
Call Details Advanced Grant (AdG), LS8, ERC-2018-ADG
Summary Thousands of species are polyploid. However, the long-term establishment of organisms that have undergone ancient whole genome duplications (WGDs) has been exceedingly rare and when we analyse the genomes of plants and animals, we can, at most, find evidence for a very limited number of WGDs that survived on the longer term. The paucity of (established) ancient genome duplications and the existence of so many species that are currently polyploid provides a fascinating paradox. There is growing evidence that the majority of ancient WGDs were established at specific times in evolution, for instance during periods of environmental change and periods of mass-extinction. The reason for this ‘stress’-polyploidy relationship has been the subject of considerable speculation and several hypotheses have been put forward to explain this observation: (a) stressful conditions promote polyploid formation; (b) polyploidisation causes a niche shift allowing polyploids to grow in conditions that are unsuitable for their non-polyploid ancestors; and (c) polyploids have an increased evolvability and consequently adapt faster to a changing environment. Here, we want to unravel the mechanistic underpinnings of why and how polyploids can outcompete non-polyploids. We will address these questions by replaying the ‘genome duplication tape of life’ in two different model systems, namely Chlamydomonas and Spirodela. We will run long-term evolutionary (and resequencing) experiments. We will complement these experiments with in-silico experiments based on so-called digital organisms running on artificial genomes. Complementary modelling approaches will also be employed to study the effects of polyploidy from an eco-evolutionary dynamics perspective. By integrating the results obtained from these in vivo and in silico experiments, we will obtain important novel insights in the adaptive potential of polyploids under stressful conditions or during times of environmental and/or climate change.
Summary
Thousands of species are polyploid. However, the long-term establishment of organisms that have undergone ancient whole genome duplications (WGDs) has been exceedingly rare and when we analyse the genomes of plants and animals, we can, at most, find evidence for a very limited number of WGDs that survived on the longer term. The paucity of (established) ancient genome duplications and the existence of so many species that are currently polyploid provides a fascinating paradox. There is growing evidence that the majority of ancient WGDs were established at specific times in evolution, for instance during periods of environmental change and periods of mass-extinction. The reason for this ‘stress’-polyploidy relationship has been the subject of considerable speculation and several hypotheses have been put forward to explain this observation: (a) stressful conditions promote polyploid formation; (b) polyploidisation causes a niche shift allowing polyploids to grow in conditions that are unsuitable for their non-polyploid ancestors; and (c) polyploids have an increased evolvability and consequently adapt faster to a changing environment. Here, we want to unravel the mechanistic underpinnings of why and how polyploids can outcompete non-polyploids. We will address these questions by replaying the ‘genome duplication tape of life’ in two different model systems, namely Chlamydomonas and Spirodela. We will run long-term evolutionary (and resequencing) experiments. We will complement these experiments with in-silico experiments based on so-called digital organisms running on artificial genomes. Complementary modelling approaches will also be employed to study the effects of polyploidy from an eco-evolutionary dynamics perspective. By integrating the results obtained from these in vivo and in silico experiments, we will obtain important novel insights in the adaptive potential of polyploids under stressful conditions or during times of environmental and/or climate change.
Max ERC Funding
2 500 000 €
Duration
Start date: 2020-01-01, End date: 2024-12-31
