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  • 1
    Online Resource
    Online Resource
    Informa UK Limited ; 2017
    In:  Journal of the American Statistical Association Vol. 112, No. 520 ( 2017-10-02), p. 1598-1611
    In: Journal of the American Statistical Association, Informa UK Limited, Vol. 112, No. 520 ( 2017-10-02), p. 1598-1611
    Type of Medium: Online Resource
    ISSN: 0162-1459 , 1537-274X
    RVK:
    RVK:
    Language: English
    Publisher: Informa UK Limited
    Publication Date: 2017
    detail.hit.zdb_id: 2064981-2
    detail.hit.zdb_id: 207602-0
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  • 2
    Online Resource
    Online Resource
    MIT Press ; 2002
    In:  Neural Computation Vol. 14, No. 9 ( 2002-09-01), p. 2221-2244
    In: Neural Computation, MIT Press, Vol. 14, No. 9 ( 2002-09-01), p. 2221-2244
    Abstract: A three-level hierarchical mixture model for classification is presented that models the following data generation process: (1) the data are generated by a finite number of sources (clusters), and (2) the generation mechanism of each source assumes the existence of individual internal class-labeled sources (subclusters of the external cluster). The model estimates the posterior probability of class membership similar to a mixture of experts classifier. In order to learn the parameters of the model, we have developed a general training approach based on maximum likelihood that results in two efficient training algorithms. Compared to other classification mixture models, the proposed hierarchical model exhibits several advantages and provides improved classification performance as indicated by the experimental results.
    Type of Medium: Online Resource
    ISSN: 0899-7667 , 1530-888X
    Language: English
    Publisher: MIT Press
    Publication Date: 2002
    detail.hit.zdb_id: 1025692-1
    detail.hit.zdb_id: 1498403-9
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  • 3
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2021
    In:  Machine Learning Vol. 110, No. 5 ( 2021-05), p. 965-987
    In: Machine Learning, Springer Science and Business Media LLC, Vol. 110, No. 5 ( 2021-05), p. 965-987
    Abstract: We introduce a Gaussian process latent factor model for multi-label classification that can capture correlations among class labels by using a small set of latent Gaussian process functions. To address computational challenges, when the number of training instances is very large, we introduce several techniques based on variational sparse Gaussian process approximations and stochastic optimization. Specifically, we apply doubly stochastic variational inference that sub-samples data instances and classes which allows us to cope with Big Data. Furthermore, we show it is possible and beneficial to optimize over inducing points, using gradient-based methods, even in very high dimensional input spaces involving up to hundreds of thousands of dimensions. We demonstrate the usefulness of our approach on several real-world large-scale multi-label learning problems.
    Type of Medium: Online Resource
    ISSN: 0885-6125 , 1573-0565
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2021
    detail.hit.zdb_id: 1475529-4
    detail.hit.zdb_id: 54638-0
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  • 4
    Online Resource
    Online Resource
    Springer Science and Business Media LLC ; 2012
    In:  BMC Systems Biology Vol. 6, No. 1 ( 2012), p. 53-
    In: BMC Systems Biology, Springer Science and Business Media LLC, Vol. 6, No. 1 ( 2012), p. 53-
    Type of Medium: Online Resource
    ISSN: 1752-0509
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2012
    detail.hit.zdb_id: 2265490-2
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  • 5
    Online Resource
    Online Resource
    Elsevier BV ; 2023
    In:  Journal of Econometrics Vol. 232, No. 2 ( 2023-02), p. 501-520
    In: Journal of Econometrics, Elsevier BV, Vol. 232, No. 2 ( 2023-02), p. 501-520
    Type of Medium: Online Resource
    ISSN: 0304-4076
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2023
    detail.hit.zdb_id: 1460617-3
    detail.hit.zdb_id: 184861-6
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  • 6
    In: Blood, American Society of Hematology, Vol. 124, No. 21 ( 2014-12-06), p. 1942-1942
    Abstract: Background: Major progress has been made in understanding disease biology and therapeutic options for patients with chronic lymphocytic leukaemia (CLL). Recurrent mutations have been discovered using next generation sequencing, but with the exception of TP53 disruption their potential impact on response to treatment is unknown. In order to address this question, we characterised the genomic landscape of 250 first-line chemo-immunotherapy treated CLL patients within UK clinical trials using targeted resequencing and whole-genome SNP array. Methods: We studied patients from two UK-based Phase II randomised controlled trials (AdMIRe and ARCTIC) receiving FCR-based treatment in a first-line treatment setting. A TruSeq Custom Amplicon panel (TSCA, Illumina) was designed targeting 10 genes recurrently mutated in CLL based on recent publications.Average sequencing depth was 2260X. The cumulated length of targets sequenced was 7.87 kb from 330 amplicons covering 160 exons. Alignment and variant calling included a combination of three pipelines to confidently detect SNVs, indels and low level frequency mutations. SNP array testing was performed using HumanOmni2.5-8 BeadChips, (Illumina) and data analysed using Nexus 6.1 Discovery Edition, Biodiscovery. We performed targeted resequencing and genome-wide SNP arrays using selected samples’ germline material to confirm somatic mutations (n=40). Univariate and multivariate analyses using minimal residual disease (MRD) as the outcome measure were performed for 220 of the 250 patients. Results: Pathogenic mutations were identified in 165 (66%) patients, totalling 268 mutations in 10 genes. ATM was the most frequently mutated gene affecting 67 patients (29%) followed by SF3B1 (n=56, 24%), NOTCH1 (n= 32, 14%), TP53 (n= 21, 9%), BIRC3 (n= 17, 7%) and XPO1 (n=14, 6%). Less frequently recurrent mutations were seen in SAMHD1 (n=8, 3%), MYD88 (n= 4, 2%), MED12 (n=7, 3%) and ZFPM2 (n=5, 2%). Integrating sequencing and array results increased the patients with one or more CLL driver mutation from 66% to 94%. As previously reported del17p and TP53 mutations are co-occurring and associate with MRD positivity in all cases (n=15, p=0.0002). We report on minor TP53 subclones in 11 patients (VAF 1-5%), 8 of whom have MRD data available and were also associated with MRD positivity. Deletions of 11q were present in 44 patients. These lesions always included ATM but not always BIRC3. Bialleleic disruption was present in ATM for 27 patients (significantly associated with MRD positivity) and in BIRC3 for 4 patients. Rather surprisingly, trisomy 12 (n=33) and NOTCH1 mutations (n=28) were associated with MRD negativity (p=0.006 and 0.097, respectively). Analysing clonal and subclonal mutations per gene revealed the majority of mutations in SF3B1 and BIRC3 were subclonal (65% and 87% respectively). In contrast almost all SAMHD1 and MYD88 mutations were clonally distributed. There was an association between NOTCH1 subclonal mutations and MRD negativity, compared to clonal mutations, but this difference was not seen in the remaining mutated genes. From our copy number data, the presence of subclones was associated with MRD positivity (p=0.05). Combining important lesions in a multiple logistic regression analysis to predict MRD positivity, bialleleic ATM disruption, together with TP53 disruption, were the strongest predictors, followed by SAMHD1, whereas BIRC3 monoalleleic mutations were a medium predictor for MRD negativity. Conclusion: This is the first integrated genome-wide analysis of the distribution and associations of CLL drivers, using targeted deep resequencing and whole genome SNP arrays in an FCR-based first-line treatment setting. We have shown subclonal and clonal mutation profiles in all patients. For patients with two or more CLL-associated mutations we have begun to unravel clonal hierarchies. We have developed a comprehensive model using MRD as an outcome measure and have found bialleleic ATM mutations and SAMHD1 disruption to strongly predict for MRD positivity. Using MRD status as a robust proxy for PFS not only enables us to confirm results of previous studies, but is advantageous also in considerably reducing the timeframe for results. Indeed, we suggest that MRD status should be assessed routinely in future studies to complement modern integrated genomics approaches. Disclosures Hillmen: Pharmacyclics, Janssen, Gilead, Roche: Honoraria, Research Funding.
    Type of Medium: Online Resource
    ISSN: 0006-4971 , 1528-0020
    RVK:
    RVK:
    Language: English
    Publisher: American Society of Hematology
    Publication Date: 2014
    detail.hit.zdb_id: 1468538-3
    detail.hit.zdb_id: 80069-7
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  • 7
    Online Resource
    Online Resource
    MIT Press ; 2004
    In:  Neural Computation Vol. 16, No. 5 ( 2004-05-01), p. 1039-1062
    In: Neural Computation, MIT Press, Vol. 16, No. 5 ( 2004-05-01), p. 1039-1062
    Abstract: We consider data that are images containing views of multiple objects. Our task is to learn about each of the objects present in the images. This task can be approached as a factorial learning problem, where each image must be explained by instantiating a model for each of the objects present with the correct instantiation parameters. A major problem with learning a factorial model is that as the number of objects increases, there is a combinatorial explosion of the number of configurations that need to be considered. We develop a method to extract object models sequentially from the data by making use of a robust statistical method, thus avoiding the combinatorial explosion, and present results showing successful extraction of objects from real images.
    Type of Medium: Online Resource
    ISSN: 0899-7667 , 1530-888X
    Language: English
    Publisher: MIT Press
    Publication Date: 2004
    detail.hit.zdb_id: 1025692-1
    detail.hit.zdb_id: 1498403-9
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  • 8
    In: Blood, American Society of Hematology, Vol. 124, No. 21 ( 2014-12-06), p. 3315-3315
    Abstract: Background:Historically, the identification of minimal deleted regions (MDRs) has been a useful approach for pinpointing genes involved in the pathogenesis of human malignancies and constitutional disorders. Microarray technology has offered increased capability for newly identifying or refining existing MDRs and minimal overlapping regions (MORs) in cancer. Despite this, in chronic lymphocytic leukemia (CLL), published MORs that pinpoint only a few candidate genes have been limited and with the advent of NGS, the utility of high resolution array work as a discovery tool has become uncertain. Here, we show that profiling copy number abnormalities (CNAs) and cnLOH using arrays in a large patient series can still be a valuable approach for the identification of genes that are disrupted or mutated in CLL and have a role in CLL development and/or progression. Methods: 250 CLL patient DNAs from individuals enrolled in two UK-based Phase II randomised controlled trials (AdMIRe and ARCTIC trials) were tested using Infinium HumanOmni2.5-8 v1.1 according to manufacturer’s guidelines (Illumina Inc, San Diego, CA). Data were processed using GenomeStudioV2009.2 (Illumina Inc.) and analysed using Nexus Discovery Edition v6.1 (BioDiscovery, Hawthorne, CA). All Nexus plots were inspected visually to verify calls made, identify uncalled events and exclude likely false positives. To exclude common germline CNVs, the Database of Genomic Variants (DGV), a comprehensive catalog of structural variation in control data, was used. Copy number (CN) changes that encompassed fully changes noted in the DGV were excluded from further analysis. Regions of copy neutral loss of heterozygosity (cnLOH) were recorded if 〉 1Mb in size, but were not used to define or refine MORs. Data from 1275 age-appropriate control samples minimised the reporting of common cnLOH events. All genomic coordinates were noted with reference to the GRCh37, hg19 assembly. MORs were investigated using Microsoft Excel filtering functions. A subset of genes (n=91) selected from MORs mainly on the basis of event frequency and/or number of genes within the MOR and/or literature interest were taken forward for targeted sequencing (exons only) of appropriate samples with/without CN Losses or cnLOH (Set 1 n=124; Set 2 n=126). These were tested using custom designed TruSeq Custom Amplicon panels (Illumina Inc) and processed according to manufacturer’s instructions. SAMHD1 was excluded from these panels since it had been studied separately within our laboratory. The data were analysed using an in-house bioinformatics pipeline that uses the sequence aligners MSR and Stampy and the variant callers GATK and Platypus, followed by stringent filtering. Results: Using our datasets we have identified 〉 50 MORs previously unreported in the literature. Six of these showed copy number (CN) losses in 〉 3% of patients studied. Furthermore, we have refined 14 MORs that overlapped with regions described previously and that had also a CN loss frequency of 〉 3%. Thirteen MORs involved only a single reference gene, often a gene implicated previously in cancer (eg. SAMHD1, MTSS1, DCC and RFC1). Of the 91 genes taken forward for targeted sequencing, stringent data filtering led to a subset of 19 genes of interest harbouring exonic mutations. Genes with mutations identified include DCC, BAP1 and FBXW7, also implicated previously in cancer. Conclusion: We have generated high resolution CNA and cnLOH profiles for 250 first-line chemo-immunotherapy treated CLL patients and used this information to document newly identified MORs, to refine MORs reported previously and to identify mutation harbouring genes using targeted NGS. Functional knowledge supports our hypothesis that these genes may have a contributory role in CLL. For two genes, SAMHD1 and FBXW7, relevance in CLL has been established already. Taken together, our data validate the utility of high resolution arrays studies for the identification of candidate genes that may be involved in CLL development or progression when disrupted. Further studies are required to confirm a role for these genes in CLL and to elucidate the nature of the underlying biological mechanisms. Disclosures No relevant conflicts of interest to declare.
    Type of Medium: Online Resource
    ISSN: 0006-4971 , 1528-0020
    RVK:
    RVK:
    Language: English
    Publisher: American Society of Hematology
    Publication Date: 2014
    detail.hit.zdb_id: 1468538-3
    detail.hit.zdb_id: 80069-7
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  • 9
    Online Resource
    Online Resource
    Institute of Electrical and Electronics Engineers (IEEE) ; 2014
    In:  IEEE Geoscience and Remote Sensing Letters Vol. 11, No. 4 ( 2014-04), p. 838-842
    In: IEEE Geoscience and Remote Sensing Letters, Institute of Electrical and Electronics Engineers (IEEE), Vol. 11, No. 4 ( 2014-04), p. 838-842
    Type of Medium: Online Resource
    ISSN: 1545-598X , 1558-0571
    Language: Unknown
    Publisher: Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2014
    detail.hit.zdb_id: 2138738-2
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  • 10
    Online Resource
    Online Resource
    Informa UK Limited ; 2016
    In:  Journal of the American Statistical Association Vol. 111, No. 513 ( 2016-01-02), p. 200-215
    In: Journal of the American Statistical Association, Informa UK Limited, Vol. 111, No. 513 ( 2016-01-02), p. 200-215
    Type of Medium: Online Resource
    ISSN: 0162-1459 , 1537-274X
    RVK:
    RVK:
    Language: English
    Publisher: Informa UK Limited
    Publication Date: 2016
    detail.hit.zdb_id: 2064981-2
    detail.hit.zdb_id: 207602-0
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