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W.H. Polet
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2 records found
1
Mutational Signatures for Survival Prediction
Multi Task Auto Encoder for Survival Prediction using Mutational Signatures
Motivation - Cancer remains one of the deadliest diseases worldwide and while advancements have been made in cancer treatment, cancer's heterogeneous nature makes it challenging to find a good treatment. Survival prediction for cancer patients can aid in choosing a treatment plan. Various machine learning methods have been employed to predict the survival of cancer patients, but they offer little insight into why a patient's survival is likely or not. Mutational signatures can offer an explanation on what a patient's cancer originates from, and can be linked to certain outside factors such as UV radiation. Even though mutational signatures have been employed in other problems, like predicting DNA repair pathway deficiencies, they have not been used in survival prediction. Integrating the survival problem with the extraction of mutational signatures could allow for extracting signatures that are particularly indicative of a patient's survival, providing a better prediction and more insight into why a patient's survival is predicted that way.
Results - We propose Multi-Task Auto-Encoder Cox (MTAE-Cox), which combines a non-negative auto-encoder for signature extraction with a Cox model for survival prediction and optimizes these in a multi-task manner. Our method jointly optimizes the auto-encoder's reconstruction error and the Cox loss, integrating the survival prediction problem into the signature extraction. MTAE-Cox is applied to four cancers of the TCGA dataset (GBM, HNSC, OV, SKCM) and its prediction performance is compared to Cox models using Gene Expression, Mutational Catalog, and exposures to COSMIC signatures. MTAE-Cox outperforms the generally applied gene expression (median C-index of 0.579 over 0.561 for gene expression) for GBM and outperforms Cox using non-integrated signatures derived by NMF for three of the four cancers. MTAE-Cox can extract biologically relevant signatures that are similar to COSMIC signatures that are known to be common in the specific type of cancer, for example SBS3 for ovarian cancer.
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Motivation - Cancer remains one of the deadliest diseases worldwide and while advancements have been made in cancer treatment, cancer's heterogeneous nature makes it challenging to find a good treatment. Survival prediction for cancer patients can aid in choosing a treatment plan. Various machine learning methods have been employed to predict the survival of cancer patients, but they offer little insight into why a patient's survival is likely or not. Mutational signatures can offer an explanation on what a patient's cancer originates from, and can be linked to certain outside factors such as UV radiation. Even though mutational signatures have been employed in other problems, like predicting DNA repair pathway deficiencies, they have not been used in survival prediction. Integrating the survival problem with the extraction of mutational signatures could allow for extracting signatures that are particularly indicative of a patient's survival, providing a better prediction and more insight into why a patient's survival is predicted that way.
Results - We propose Multi-Task Auto-Encoder Cox (MTAE-Cox), which combines a non-negative auto-encoder for signature extraction with a Cox model for survival prediction and optimizes these in a multi-task manner. Our method jointly optimizes the auto-encoder's reconstruction error and the Cox loss, integrating the survival prediction problem into the signature extraction. MTAE-Cox is applied to four cancers of the TCGA dataset (GBM, HNSC, OV, SKCM) and its prediction performance is compared to Cox models using Gene Expression, Mutational Catalog, and exposures to COSMIC signatures. MTAE-Cox outperforms the generally applied gene expression (median C-index of 0.579 over 0.561 for gene expression) for GBM and outperforms Cox using non-integrated signatures derived by NMF for three of the four cancers. MTAE-Cox can extract biologically relevant signatures that are similar to COSMIC signatures that are known to be common in the specific type of cancer, for example SBS3 for ovarian cancer.
FEATHER: Visual Editor for Escape Rooms
The Software behind Escape Room Games
Bachelor thesis
(2020)
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E.T. Rogers, S.J.G. Sebus, W.H. Polet, Y.Y. Angelova, Y.A.W. van den Akker, T.A.R. Overklift Vaupel Klein, Jan-Willem Manenschijn
Raccoon Serious Games develops and hosts educational activities such as escape room events and serious games. They create both physically- and digitally-based escape rooms across many different scales. These events consist of a variety of puzzles and tasks the player(s) have to solve in order to finish or `escape' the event. For their digitally hosted events, the Massive Online Reactive Serious Escape 2.0 (MORSE) system is used for creation and configuration of the needed underlying rules of the event. The system uses the `If This Then That' (IFTTT) principle for creating rules, where a trigger activated by the player/game can initiate a check about the state of the game which then results in an action by the game. In MORSE the user (usually the game host) can choose from the multiple types of triggers, conditions, and actions to create logical statements in the IFTTT format. These statements together form the rules of the game. This system, although a good improvement over the previously hard-coded procedure, has proven unintuitive to program for most of the employees at Raccoon Serious Games. The IFTTT format used is unwieldy to work with for the designers, who have little to no programming background. Furthermore this existing system provides no overview of the rules system making it challenging to visualise the whole game and its dynamics. To solve the unintuitive nature of MORSE, our team designed and developed Feather: A graph-based visual editing tool that is integrated into MORSE. It can generate rule and ruleset logic needed for the client's escape events. It uses visual components and presents the user with a graph of the whole game during the design process. The editor can be used together with all other, earlier existing, features for creating rulesets of the MORSE system. This tool has most of the functionality the current system has, with the possibility of easily extending it with new components. The product was built as an addition to MORSE over the course of 10 weeks. In the initial part of the project a thorough research was performed on the needs of the client as well as useful resources or libraries and design practices for domain specific visual languages. The second part of the project was devoted to the design and implementation of the tool. Throughout the duration of the project a number of user tests were conducted with the employees at Raccoon Serious Games to assess the understanding and usability of the product.
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Raccoon Serious Games develops and hosts educational activities such as escape room events and serious games. They create both physically- and digitally-based escape rooms across many different scales. These events consist of a variety of puzzles and tasks the player(s) have to solve in order to finish or `escape' the event. For their digitally hosted events, the Massive Online Reactive Serious Escape 2.0 (MORSE) system is used for creation and configuration of the needed underlying rules of the event. The system uses the `If This Then That' (IFTTT) principle for creating rules, where a trigger activated by the player/game can initiate a check about the state of the game which then results in an action by the game. In MORSE the user (usually the game host) can choose from the multiple types of triggers, conditions, and actions to create logical statements in the IFTTT format. These statements together form the rules of the game. This system, although a good improvement over the previously hard-coded procedure, has proven unintuitive to program for most of the employees at Raccoon Serious Games. The IFTTT format used is unwieldy to work with for the designers, who have little to no programming background. Furthermore this existing system provides no overview of the rules system making it challenging to visualise the whole game and its dynamics. To solve the unintuitive nature of MORSE, our team designed and developed Feather: A graph-based visual editing tool that is integrated into MORSE. It can generate rule and ruleset logic needed for the client's escape events. It uses visual components and presents the user with a graph of the whole game during the design process. The editor can be used together with all other, earlier existing, features for creating rulesets of the MORSE system. This tool has most of the functionality the current system has, with the possibility of easily extending it with new components. The product was built as an addition to MORSE over the course of 10 weeks. In the initial part of the project a thorough research was performed on the needs of the client as well as useful resources or libraries and design practices for domain specific visual languages. The second part of the project was devoted to the design and implementation of the tool. Throughout the duration of the project a number of user tests were conducted with the employees at Raccoon Serious Games to assess the understanding and usability of the product.