| 1. |
- Campbell, PJ, et al.
(författare)
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Pan-cancer analysis of whole genomes
- 2020
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Ingår i: Nature. - : Springer Science and Business Media LLC. - 1476-4687 .- 0028-0836. ; 578:7793, s. 82-
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Tidskriftsartikel (refereegranskat)abstract
- Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale1–3. Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4–5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter4; identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation5,6; analyses timings and patterns of tumour evolution7; describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity8,9; and evaluates a range of more-specialized features of cancer genomes8,10–18.
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| 2. |
- Chen, DeJiu, et al.
(författare)
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DySCAS System Architecture
- 2007
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Rapport (refereegranskat)abstract
- This deliverable provides an architectural strategy and overall system design forthe DySCAS middleware system as a first step towards a complete systemspecification. The DySCAS Basic Architecture captures both the systemconceptualization and an initial function-level outline without consideringimplementation and technology details. It defines the middleware system in termsof its application and operational contexts, expected features, middleware servicesthat group functions, behaviours, as well as the structuring and implementationdecisions that are most crucial to satisfy the given set of requirements. In thisdocument, we also outline some fundamental strategies for binding themiddleware components to the target platforms and infrastructures and forhandling potential errors at both initialization-time and rum-time, while taking thetechnology support and domain needs into consideration.This deliverable also covers the rationale behind the architecture solutions andprovides a generic framework that relates architecture principles and styles, wellknownmechanisms, and reference models to the expected functionalities andqualities of DySCAS. The aim is to consolidate the proposed solutions and also tofacilitate the communications between DySCAS partners and to third-parties.
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| 3. |
- Dahl, Oskar, et al.
(författare)
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Understanding Association Between Logged Vehicle Data and Vehicle Marketing Parameters : Using Clustering and Rule-Based Machine Learning
- 2020
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Ingår i: Proceedings of the 2020 3rd International Conference on Information Management and Management Science, IMMS 2020. - New York : Association for Computing Machinery (ACM). - 9781450375467 ; , s. 13-22
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Konferensbidrag (refereegranskat)abstract
- Trucks are designed, configured and marketed for various working environments. There lies a concern whether trucks are used as intended by the manufacturer, as usage may impact the longevity, efficiency and productivity of the trucks. In this paper we propose a framework that aims to extract costumers' vehicle behaviours from Logged Vehicle Data (LVD) in order to evaluate whether they align with vehicle configurations, so-called Global Transport Application (GTA) parameters. Gaussian mixture model (GMM)s are employed to cluster and classify various vehicle behaviors from the LVD. Rule-based machine learning (RBML) was applied on the clusters to examine whether vehicle behaviors follow the GTA configuration. Particularly, we propose an approach based on studying associations that is able to extract insights on whether the trucks are used as intended. Experimental results shown that while for the vast majority of the trucks' behaviors seemingly follows their GTA configuration, there are also interesting outliers that warrant further analysis. © 2020 ACM.
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