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Finding a treatment for cancer is a major challenge of our time. In the ongoing research, combination therapies (the use of several drugs together) are of high interest. In comparison with the use of a single drug, combinations of synergistic drugs (i.e. drugs that are more effective together than alone) can be as effective while allowing to overcome the drug resistance, to reduce the doses at which the drugs are used, and consequently decrease their toxic effect and multiply their targets. However, the space of all potentially effective combinations is too large to experimentally test all of them and assess their effectiveness, this is known as the combinatorial explosion problem. To overcome that, the identification of interesting combinations requires the help of computational tools. In recent years, machine learning models have been successfully used in biomedical applications. They are typically used in order to determine which combinations would be interesting to be experimentally tested. Since some models aiming at predicting the responses of pairwise combinations already exist, there are only a few machine learning models able to predict responses of higher order drug combinations (the order of a drug combination is defined as the number of drugs in the combination). In addition to the response of a drug combination (typically expressed as a growth percentage), the synergy score of this combination is of high interest. The synergy score allow to answer the question: how much are those drugs more effective together than individually? This work is a step towards the use of machine learning to predict the effect of higher order (order larger than two) cancer drug combinations. It has been made in collaboration with Aalto University (Finland), where a machine learning tool called ComboFM has been developed. ComboFM is able to efficiently predict pairwise responses of cancer drugs. The goal of this work is to extend the use of ComboFM to the predictions of higher order drug combinations. To that end, we propose to combine ComboFM with another model, called the Dose model. The Dose model computes the responses of any order drug combinations, based on all the pairwise responses existing in the combination. This work investigates how those two models can be combined together in order to predict responses of higher order drug combinations while decreasing the amount of required experimental data (pairwise responses). This combination of models gives rise to several issues that are tackled and investigated. The experiments made in this thesis showed that ComboFM and the Dose model can efficiently be combined, as long as the parameters of both models are optimized specifically for this application.

Combination therapy --- Cancer treatment --- Machine learning --- Higher order drug combination --- Ingénierie, informatique & technologie > Multidisciplinaire, généralités & autres

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Average value. --- Chemistry --- Complex reactions. --- Curve fitting. --- Geometry. --- Higher order reactions. --- Mathematics. --- Reaction rates. --- Statistics. --- Temperature dependence. --- Problems, exercises, etc.

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human higher-order cognition --- critical thinking --- problem solving --- decision-making --- creativity --- Creative ability --- Créativité --- Creatividad --- Creativeness --- Creativity --- Ability --- Creation (Literary, artistic, etc.)

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This eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contact

Science: general issues --- Psychology --- hand preference --- cerebral dominance --- brain functioning --- sensorimotor control --- higher-order processing --- skilled actions --- praxis --- laterality --- spatial discrimination --- tool affordances

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Algebra --- 514.763.5 --- 530.12 --- Calculus of tensors --- Quantum theory --- Symmetry (Physics) --- #WSCH:AAS2 --- Invariance principles (Physics) --- Symmetry (Chemistry) --- Conservation laws (Physics) --- Physics --- Quantum dynamics --- Quantum mechanics --- Quantum physics --- Mechanics --- Thermodynamics --- Absolute differential calculus --- Analysis, Tensor --- Calculus, Absolute differential --- Calculus, Tensor --- Tensor analysis --- Tensor calculus --- Geometry, Differential --- Geometry, Infinitesimal --- Vector analysis --- Spinor analysis --- Infinitesimal structures and fields of geometric objects of higher order. Tangent bundles. Jets. Tensors. Tensor fields etc. --- Relativity principle --- Calculus of tensors. --- Quantum theory. --- Symmetry (Physics). --- 530.12 Relativity principle --- 514.763.5 Infinitesimal structures and fields of geometric objects of higher order. Tangent bundles. Jets. Tensors. Tensor fields etc. --- Infinitesimal structures and fields of geometric objects of higher order. Tangent bundles. Jets. Tensors. Tensor fields etc