| 1. |
- Romagnoni, A, et al.
(författare)
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Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data
- 2019
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Ingår i: Scientific reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 9:1, s. 10351-
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Tidskriftsartikel (refereegranskat)abstract
- Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers.
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| 2. |
- Momozawa, Y, et al.
(författare)
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IBD risk loci are enriched in multigenic regulatory modules encompassing putative causative genes
- 2018
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Ingår i: Nature communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 9:1, s. 2427-
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Tidskriftsartikel (refereegranskat)abstract
- GWAS have identified >200 risk loci for Inflammatory Bowel Disease (IBD). The majority of disease associations are known to be driven by regulatory variants. To identify the putative causative genes that are perturbed by these variants, we generate a large transcriptome data set (nine disease-relevant cell types) and identify 23,650 cis-eQTL. We show that these are determined by ∼9720 regulatory modules, of which ∼3000 operate in multiple tissues and ∼970 on multiple genes. We identify regulatory modules that drive the disease association for 63 of the 200 risk loci, and show that these are enriched in multigenic modules. Based on these analyses, we resequence 45 of the corresponding 100 candidate genes in 6600 Crohn disease (CD) cases and 5500 controls, and show with burden tests that they include likely causative genes. Our analyses indicate that ≥10-fold larger sample sizes will be required to demonstrate the causality of individual genes using this approach.
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| 5. |
- Franke, Andre, et al.
(författare)
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Genome-wide meta-analysis increases to 71 the number of confirmed Crohn's disease susceptibility loci
- 2010
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Ingår i: Nature Genetics. - : Springer Science and Business Media LLC. - 1061-4036 .- 1546-1718. ; 42:12, s. 1118-1125
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Tidskriftsartikel (refereegranskat)abstract
- We undertook a meta-analysis of six Crohn's disease genome-wide association studies (GWAS) comprising 6,333 affected individuals (cases) and 15,056 controls and followed up the top association signals in 15,694 cases, 14,026 controls and 414 parent-offspring trios. We identified 30 new susceptibility loci meeting genome-wide significance (P < 5 × 10⁻⁸). A series of in silico analyses highlighted particular genes within these loci and, together with manual curation, implicated functionally interesting candidate genes including SMAD3, ERAP2, IL10, IL2RA, TYK2, FUT2, DNMT3A, DENND1B, BACH2 and TAGAP. Combined with previously confirmed loci, these results identify 71 distinct loci with genome-wide significant evidence for association with Crohn's disease.
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| 6. |
- Prescott, D R, et al.
(författare)
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A reliability analysis method using binary decision diagrams in phased mission planning
- 2009
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Ingår i: Proceedings of the Institution of Mechanical Engineers. Part O, Journal of risk and reliability. - : Sage Publications. - 1748-006X .- 1748-0078. ; 223:2, s. 133-143
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Tidskriftsartikel (refereegranskat)abstract
- The use of autonomous systems is becoming increasingly common in many fields. A significant example of this is the ambition to deploy unmanned aerial vehicles (UAVs) for both civil and military applications. In order for autonomous systems such as these to operate effectively, they must be capable of making decisions regarding the appropriate future course of their mission responding to changes in circumstance in as short a time as possible. The systems will typically perform phased missions and, owing to the uncertain nature of the environments in which the systems operate, the mission objectives may be subject to change at short notice. The ability to evaluate the different possible mission configurations is crucial in making the right decision about the mission tasks that should be performed in order to give the highest possible probability of mission success.Because binary decision diagrams (BDDs) may be quickly and accurately quantified to give measures of the system reliability it is anticipated that they are the most appropriate analysis tools to form the basis of a reliability-based prognostics methodology. The current paper presents a new BDD-based approach for phased mission analysis, which seeks to take advantage of the proven fast analysis characteristics of the BDD and enhance it in ways that are suited to the demands of a decision-making capability for autonomous systems. The BDD approach presented allows BDDs representing the failure causes in the different phases of a mission to be constructed quickly by treating component failures in different phases of the mission as separate variables. This allows flexibility when building mission phase failure BDDs because a global variable ordering scheme is not required. An alternative representation of component states in time intervals allows the dependencies to be efficiently dealt with during the quantification process. Nodes in the BDD can represent components with any number of failure modes or factors external to the system that could affect its behaviour, such as the weather. Path simplification rules and quantification rules are developed that allow the calculation of phase failure probabilities for this new BDD approach. The proposed method provides a phased mission analysis technique that allows the rapid construction of reliability models for phased missions and, with the use of BDDs, rapid quantification.
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