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Identification of Candidate Driver Genes in Common Focal Chromosomal Aberrations of Microsatellite Stable Colorectal Cancer  [PDF]
George J. Burghel, Wei-Yu Lin, Helen Whitehouse, Ian Brock, David Hammond, Jonathan Bury, Yvonne Stephenson, Rina George, Angela Cox
PLOS ONE , 2013, DOI: 10.1371/journal.pone.0083859
Abstract: Colorectal cancer (CRC) is a leading cause of cancer deaths worldwide. Chromosomal instability (CIN) is a major driving force of microsatellite stable (MSS) sporadic CRC. CIN tumours are characterised by a large number of somatic chromosomal copy number aberrations (SCNA) that frequently affect oncogenes and tumour suppressor genes. The main aim of this work was to identify novel candidate CRC driver genes affected by recurrent and focal SCNA. High resolution genome-wide comparative genome hybridisation (CGH) arrays were used to compare tumour and normal DNA for 53 sporadic CRC cases. Context corrected common aberration (COCA) analysis and custom algorithms identified 64 deletions and 32 gains of focal minimal common regions (FMCR) at high frequency (>10%). Comparison of these FMCR with published genomic profiles from CRC revealed common overlap (42.2% of deletions and 34.4% of copy gains). Pathway analysis showed that apoptosis and p53 signalling pathways were commonly affected by deleted FMCR, and MAPK and potassium channel pathways by gains of FMCR. Candidate tumour suppressor genes in deleted FMCR included RASSF3, IFNAR1, IFNAR2 and NFKBIA and candidate oncogenes in gained FMCR included PRDM16, TNS1, RPA3 and KCNMA1. In conclusion, this study confirms some previously identified aberrations in MSS CRC and provides in silico evidence for some novel candidate driver genes.
An Evolutionary Approach for Identifying Driver Mutations in Colorectal Cancer  [PDF]
Jasmine Foo?,Lin L Liu?,Kevin Leder?,Markus Riester?,Yoh Iwasa?,Christoph Lengauer?,Franziska Michor
PLOS Computational Biology , 2015, DOI: 10.1371/journal.pcbi.1004350
Abstract: The traditional view of cancer as a genetic disease that can successfully be treated with drugs targeting mutant onco-proteins has motivated whole-genome sequencing efforts in many human cancer types. However, only a subset of mutations found within the genomic landscape of cancer is likely to provide a fitness advantage to the cell. Distinguishing such “driver” mutations from innocuous “passenger” events is critical for prioritizing the validation of candidate mutations in disease-relevant models. We design a novel statistical index, called the Hitchhiking Index, which reflects the probability that any observed candidate gene is a passenger alteration, given the frequency of alterations in a cross-sectional cancer sample set, and apply it to a mutational data set in colorectal cancer. Our methodology is based upon a population dynamics model of mutation accumulation and selection in colorectal tissue prior to cancer initiation as well as during tumorigenesis. This methodology can be used to aid in the prioritization of candidate mutations for functional validation and contributes to the process of drug discovery.
An Integrated Approach to Uncover Driver Genes in Breast Cancer Methylation Genomes  [PDF]
Xiaopei Shen, Shan Li, Lin Zhang, Hongdong Li, Guini Hong, XianXiao Zhou, Tingting Zheng, Wenjing Zhang, Chunxiang Hao, Tongwei Shi, Chunyang Liu, Zheng Guo
PLOS ONE , 2013, DOI: 10.1371/journal.pone.0061214
Abstract: Background Cancer cells typically exhibit large-scale aberrant methylation of gene promoters. Some of the genes with promoter methylation alterations play “driver” roles in tumorigenesis, whereas others are only “passengers”. Results Based on the assumption that promoter methylation alteration of a driver gene may lead to expression alternation of a set of genes associated with cancer pathways, we developed a computational framework for integrating promoter methylation and gene expression data to identify driver methylation aberrations of cancer. Applying this approach to breast cancer data, we identified many novel cancer driver genes and found that some of the identified driver genes were subtype-specific for basal-like, luminal-A and HER2+ subtypes of breast cancer. Conclusion The proposed framework proved effective in identifying cancer driver genes from genome-wide gene methylation and expression data of cancer. These results may provide new molecular targets for potential targeted and selective epigenetic therapy.
Simultaneous Identification of Multiple Driver Pathways in Cancer  [PDF]
Mark D. M. Leiserson,Dima Blokh,Roded Sharan ,Benjamin J. Raphael
PLOS Computational Biology , 2013, DOI: 10.1371/journal.pcbi.1003054
Abstract: Distinguishing the somatic mutations responsible for cancer (driver mutations) from random, passenger mutations is a key challenge in cancer genomics. Driver mutations generally target cellular signaling and regulatory pathways consisting of multiple genes. This heterogeneity complicates the identification of driver mutations by their recurrence across samples, as different combinations of mutations in driver pathways are observed in different samples. We introduce the Multi-Dendrix algorithm for the simultaneous identification of multiple driver pathways de novo in somatic mutation data from a cohort of cancer samples. The algorithm relies on two combinatorial properties of mutations in a driver pathway: high coverage and mutual exclusivity. We derive an integer linear program that finds set of mutations exhibiting these properties. We apply Multi-Dendrix to somatic mutations from glioblastoma, breast cancer, and lung cancer samples. Multi-Dendrix identifies sets of mutations in genes that overlap with known pathways – including Rb, p53, PI(3)K, and cell cycle pathways – and also novel sets of mutually exclusive mutations, including mutations in several transcription factors or other genes involved in transcriptional regulation. These sets are discovered directly from mutation data with no prior knowledge of pathways or gene interactions. We show that Multi-Dendrix outperforms other algorithms for identifying combinations of mutations and is also orders of magnitude faster on genome-scale data. Software available at: http://compbio.cs.brown.edu/software.
DomainRBF: a Bayesian regression approach to the prioritization of candidate domains for complex diseases
Wangshu Zhang, Yong Chen, Fengzhu Sun, Rui Jiang
BMC Systems Biology , 2011, DOI: 10.1186/1752-0509-5-55
Abstract: Using a compiled dataset containing 1,614 associations between 671 domains and 1,145 disease phenotypes, we demonstrate the effectiveness of the proposed approach through three large-scale leave-one-out cross-validation experiments (random control, simulated linkage interval, and genome-wide scan), and we do so in terms of three criteria (precision, mean rank ratio, and AUC score). We further show that the proposed approach is robust to the parameters involved and the underlying domain-domain interaction network through a series of permutation tests. Once having assessed the validity of this approach, we show the possibility of ab initio inference of domain-disease associations and gene-disease associations, and we illustrate the strong agreement between our inferences and the evidences from genome-wide association studies for four common diseases (type 1 diabetes, type 2 diabetes, Crohn's disease, and breast cancer). Finally, we provide a pre-calculated genome-wide landscape of associations between 5,490 protein domains and 5,080 human diseases and offer free access to this resource.The proposed approach effectively ranks susceptible domains among the top of the candidates, and it is robust to the parameters involved. The ab initio inference of domain-disease associations shows strong agreement with the evidence provided by genome-wide association studies. The predicted landscape provides a comprehensive understanding of associations between domains and human diseases.Over the past few decades, remarkable success has been achieved for such traditional gene-mapping approaches as family-based linkage analysis [1,2] and population-based association studies [3,4] in pinpointing genes that are responsible for human inherited diseases [5,6]. Nevertheless, these traditional methods are either only capable of linking diseases with genetic regions that typically contain dozens to hundreds of genes, or usually require carefully selected candidate genes that are biologically
Physics at a Fermilab Proton Driver  [PDF]
M. G. Albrow,S. Antusch,K. S. Babu,T. Barnes,A. O. Bazarko,R. H. Bernstein,T. J. Bowles,S. J. Brice,A. Ceccucci,F. Cei,H. W. KCheung,D. C. Christian,J. I. Collar,J. Cooper,P. S. Cooper,A. Curioni,A. deGouvea,F. DeJongh,P. F. Derwent,M. V. Diwan,B. A. Dobrescu,G. J. Feldman,D. A. Finley,B. T. Fleming,S. Geer,G. L. Greene,Y. Grossman,D. A. Harris,C. J. Horowitz,D. W. Hertzog,P. Huber,J. Imazato,A. Jansson,K. P. Jungmann,P. A. Kasper,J. Kersten,S. H. Kettell,Y. Kuno,M. Lindner,M. Mandelkern,W. J. Marciano,W. Melnitchouk,O. Mena,D. G. Michael,J. P. Miller,G. B. Mills,J. G. Morfin,H. Nguyen
Physics , 2005,
Abstract: This report documents the physics case for building a 2 MW, 8 GeV superconducting linac proton driver at Fermilab.
A Functional Driver Analyzing Concept  [PDF]
Tobias Islinger,Thorsten K?hler,Christian Wolff
Advances in Human-Computer Interaction , 2011, DOI: 10.1155/2011/413964
Abstract: It is evident that a lot of accidents occur because of drowsiness or inattentiveness of the driver. The logical consequence is that we have to find methods to better analyze the driver. A lot of research has been spent on camera-based systems which focus on the driver's eye gaze or his head movement. But there are few systems that provide camera-free driver analyzing. This is the main goal of the work presented here which is structured in three phases, with the operational goal of having a working driver analyzer implemented in a car. The main question is: is it possible to make statements concerning the driver and his state by using vehicle data from the CAN Bus only? This paper describes the current state of driver analyzing, our overall system architecture, as well as future work. At the moment, we focus on detecting the driving style of a person. 1. Introduction Driver analysis (DA) has been an active field of research for years. For example, [1] published an article about driver monitoring already in 2005. Among others, DA can be divided in the following subtopics: driver monitoring, driving style analysis, and merging vehicle data to derive conclusions concerning the driver (The word driver means both, female as well as male drivers. This is also relevant for words like “his” or “him” which reflect also both, female as well as male persons.) and his environment. For our research work, we focus on the following aspects. (i)How can the state of the driver be detected without using a camera or realtime biosensor data like a electrocardiogram (ecd)? (ii)How can we support the driver, depending on his actual driving situation, based on the results of the driver state detection? Driver monitoring is usually performed by cameras installed in the car for detecting the driver's behavior or state, mostly by using infrared cameras ([2, 3], or [1]). There are also first results for noncamera based research on driver analysis: By analyzing analog speed graphs, Rygula [4] makes conclusions about the driving style, speed profile and, depending on driving time and course, aggressiveness of the driver. Therefore, he evaluated ten analog speed graphs for two drivers by comparing their speed profile, their profile referring to the distance, or referring to route and direction. Rygula states that “Even a brake of 45 minutes reduce aggressivity of driving style” ([4, page 79]). A different approach is the research on context recognition in vehicles and the development of a driver model. Ferscha and Riener [5] describe this process of in-car context recognition and
Some Notes on the Musical Landscape
Theo Van Leeuwen
Transforming Cultures , 2009,
Abstract: Some Notes on the Musical Landscape
Characterization of Driver Nodes of Anti-Stable Networks  [PDF]
Ram Niwash Mahia,Deepak Fulwani,Mahaveer Singh
Computer Science , 2014,
Abstract: A controllable network can be driven from any initial state to any desired state using driver nodes. A set of driver nodes to control a network is not unique. It is important to characterize these driver nodes and select the right driver nodes. The work discusses theory and algorithms to select driver node such that largest region of attraction can be obtained considering limited capacity of driver node and with unstable eigenvalues of adjacency matrix. A network which can be controllable using one driver node is considered. Nonuniqueness of driver node poses a challenge to select right driver node when multiple possibilities exist. The work addresses this issue.
Analysis and Diversion of Duqu's Driver  [PDF]
Guillaume Bonfante,Jean-Yves Marion,Fabrice Sabatier,Aurélien Thierry
Computer Science , 2014,
Abstract: The propagation techniques and the payload of Duqu have been closely studied over the past year and it has been said that Duqu shared functionalities with Stuxnet. We focused on the driver used by Duqu during the infection, our contribution consists in reverse-engineering the driver: we rebuilt its source code and analyzed the mechanisms it uses to execute the payload while avoiding detection. Then we diverted the driver into a defensive version capable of detecting injections in Windows binaries, thus preventing further attacks. We specifically show how Duqu's modified driver would have detected Duqu.
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