Lodewyk F A Wessels

Centrum Wiskunde & Informatica, Amsterdamo, North Holland, Netherlands

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Publications (145)1182.69 Total impact

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    ABSTRACT: Genomic information is encoded on a wide range of distance scales, ranging from tens of bases to megabases. We developed a multiscale framework to analyze and visualize the information content of genomic signals. Different types of signals, such as G+C content or DNA methylation, are characterized by distinct patterns of signal enrichment or depletion across scales spanning several orders of magnitude. These patterns are associated with a variety of genomic annotations. By integrating the information across all scales, we demonstrated improved prediction of gene expression from polymerase II chromatin immunoprecipitation sequencing (ChIP-seq) measurements, and we observed that gene expression differences in colorectal cancer are related to methylation patterns that extend beyond the single-gene scale. Our software is available at https://github.com/tknijnen/msr/.
    Nature Methods 04/2014; · 23.57 Impact Factor
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    ABSTRACT: Retroviral insertional mutagenesis is a powerful tool for identifying putative cancer genes in mice. To uncover the regulatory mechanisms by which common insertion loci affect downstream processes, we supplemented genotyping data with genome-wide mRNA expression profiling data for 97 tumors induced by retroviral insertional mutagenesis. We developed locus expression signature analysis, an algorithm to construct and interpret the differential gene expression signature associated with each common insertion locus. Comparing locus expression signatures to promoter affinity profiles allowed us to build a detailed map of transcription factors whose protein-level regulatory activity is modulated by a particular locus. We also predicted a large set of drugs that might mitigate the effect of the insertion on tumorigenesis. Taken together, our results demonstrate the potential of a locus-specific signature approach for identifying mammalian regulatory mechanisms in a cancer context.
    Proceedings of the National Academy of Sciences 04/2014; · 9.74 Impact Factor
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    ABSTRACT: The ability of retroviruses and transposons to insert their genetic material into host DNA makes them widely used tools in molecular biology, cancer research and gene therapy. However, these systems have biases that may strongly affect research outcomes. To address this issue, we generated very large datasets consisting of [Formula: see text] to [Formula: see text] unselected integrations in the mouse genome for the Sleeping Beauty (SB) and piggyBac (PB) transposons, and the Mouse Mammary Tumor Virus (MMTV). We analyzed [Formula: see text] (epi)genomic features to generate bias maps at both local and genome-wide scales. MMTV showed a remarkably uniform distribution of integrations across the genome. More distinct preferences were observed for the two transposons, with PB showing remarkable resemblance to bias profiles of the Murine Leukemia Virus. Furthermore, we present a model where target site selection is directed at multiple scales. At a large scale, target site selection is similar across systems, and defined by domain-oriented features, namely expression of proximal genes, proximity to CpG islands and to genic features, chromatin compaction and replication timing. Notable differences between the systems are mainly observed at smaller scales, and are directed by a diverse range of features. To study the effect of these biases on integration sites occupied under selective pressure, we turned to insertional mutagenesis (IM) screens. In IM screens, putative cancer genes are identified by finding frequently targeted genomic regions, or Common Integration Sites (CISs). Within three recently completed IM screens, we identified 7%-33% putative false positive CISs, which are likely not the result of the oncogenic selection process. Moreover, results indicate that PB, compared to SB, is more suited to tag oncogenes.
    PLoS Genetics 04/2014; 10(4):e1004250. · 8.52 Impact Factor
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    ABSTRACT: There are no effective therapies for the ∼30% of human malignancies with mutant RAS oncogenes. Using a kinome-centered synthetic lethality screen, we find that suppression of the ERBB3 receptor tyrosine kinase sensitizes KRAS mutant lung and colon cancer cells to MEK inhibitors. We show that MEK inhibition results in MYC-dependent transcriptional upregulation of ERBB3, which is responsible for intrinsic drug resistance. Drugs targeting both EGFR and ERBB2, each capable of forming heterodimers with ERBB3, can reverse unresponsiveness to MEK inhibition by decreasing inhibitory phosphorylation of the proapoptotic proteins BAD and BIM. Moreover, ERBB3 protein level is a biomarker of response to combinatorial treatment. These data suggest a combination strategy for treating KRAS mutant colon and lung cancers and a way to identify the tumors that are most likely to benefit from such combinatorial treatment.
    Cell reports. 03/2014;
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    ABSTRACT: Treatment of BRAF(V600E) mutant melanoma by small molecule drugs that target the BRAF or MEK kinases can be effective, but resistance develops invariably1, 2. In contrast, colon cancers that harbour the same BRAF(V600E) mutation are intrinsically resistant to BRAF inhibitors, due to feedback activation of the epidermal growth factor receptor (EGFR)3, 4. Here we show that 6 out of 16 melanoma tumours analysed acquired EGFR expression after the development of resistance to BRAF or MEK inhibitors. Using a chromatin-regulator-focused short hairpin RNA (shRNA) library, we find that suppression of sex determining region Y-box 10 (SOX10) in melanoma causes activation of TGF-β signalling, thus leading to upregulation of EGFR and platelet-derived growth factor receptor-β (PDGFRB), which confer resistance to BRAF and MEK inhibitors. Expression of EGFR in melanoma or treatment with TGF-β results in a slow-growth phenotype with cells displaying hallmarks of oncogene-induced senescence. However, EGFR expression or exposure to TGF-β becomes beneficial for proliferation in the presence of BRAF or MEK inhibitors. In a heterogeneous population of melanoma cells having varying levels of SOX10 suppression, cells with low SOX10 and consequently high EGFR expression are rapidly enriched in the presence of drug, but this is reversed when the drug treatment is discontinued. We find evidence for SOX10 loss and/or activation of TGF-β signalling in 4 of the 6 EGFR-positive drug-resistant melanoma patient samples. Our findings provide a rationale for why some BRAF or MEK inhibitor-resistant melanoma patients may regain sensitivity to these drugs after a ‘drug holiday’ and identify patients with EGFR-positive melanoma as a group that may benefit from re-treatment after a drug holiday.
    Nature 03/2014; advance online publication. · 38.60 Impact Factor
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    ABSTRACT: Despite continuous efforts, not a single predictor of breast cancer chemotherapy resistance has made it into the clinic yet. However, it has become clear in recent years that breast cancer is a collection of molecularly distinct diseases. With ever increasing amounts of breast cancer data becoming available, we set out to study if gene expression based predictors of chemotherapy resistance that are specific for breast cancer subtypes can improve upon the performance of generic predictors. We trained predictors of resistance that were specific for a subtype and generic predictors that were not specific for a particular subtype, i.e. trained on all subtypes simultaneously. Through a rigorous double-loop cross-validation we compared the performance of these two types of predictors on the different subtypes on a large set of tumors all profiled on the same expression platform (n = 394). We evaluated predictors based on either mRNA gene expression or clinical features. For HER2+, ER- breast cancer, subtype specific predictor based on clinical features outperformed the generic, non-specific predictor. This can be explained by the fact that the generic predictor included HER2 and ER status, features that are predictive over the whole set, but not within this subtype. In all other scenarios the generic predictors outperformed the subtype specific predictors or showed equal performance. Since it depends on the specific context which type of predictor - subtype specific or generic- performed better, it is highly recommended to evaluate both specific and generic predictors when attempting to predict treatment response in breast cancer.
    PLoS ONE 01/2014; 9(2):e88551. · 3.73 Impact Factor
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    ABSTRACT: The most common risk factor for developing hepatocellular carcinoma (HCC) is chronic infection with hepatitis B virus (HBV). To better understand the evolutionary forces driving HCC, we performed a near-saturating transposon mutagenesis screen in a mouse HBV model of HCC. This screen identified 21 candidate early stage drivers and a very large number (2,860) of candidate later stage drivers that were enriched for genes that are mutated, deregulated or functioning in signaling pathways important for human HCC, with a striking 1,199 genes being linked to cellular metabolic processes. Our study provides a comprehensive overview of the genetic landscape of HCC.
    Nature Genetics 12/2013; · 35.21 Impact Factor
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    ABSTRACT: Most of the macromolecular structures in the Protein Data Bank (PDB), which are used daily by thousands of educators and scientists alike, are determined by X-ray crystallography. It was examined whether the crystallographic models and data were deposited to the PDB at the same time as the publications that describe them were submitted for peer review. This condition is necessary to ensure pre-publication validation and the quality of the PDB public archive. It was found that a significant proportion of PDB entries were submitted to the PDB after peer review of the corresponding publication started, and many were only submitted after peer review had ended. It is argued that clear description of journal policies and effective policing is important for pre-publication validation, which is key in ensuring the quality of the PDB and of peer-reviewed literature.
    Acta Crystallographica Section D Biological Crystallography 12/2013; 69(Pt 12):2293-5. · 12.67 Impact Factor
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    ABSTRACT: Aromatase inhibitors are the major first-line treatment of estrogen receptor-positive breast cancer, but resistance to treatment is common. To date, no biomarkers have been validated clinically to guide subsequent therapy in these patients. In this study, we mapped the genome-wide chromatin-binding profiles of estrogen receptor α (ERα), along with the epigenetic modifications H3K4me3 and H3K27me3, that are responsible for determining gene transcription (n = 12). Differential binding patterns of ERα, H3K4me3, and H3K27me3 were enriched between patients with good or poor outcomes after aromatase inhibition. ERα and H3K27me3 patterns were validated in an additional independent set of breast cancer cases (n = 10). We coupled these patterns to array-based proximal gene expression and progression-free survival data derived from a further independent cohort of 72 aromatase inhibitor-treated patients. Through this approach, we determined that the ERα and H3K27me3 profiles predicted the treatment outcomes for first-line aromatase inhibitors. In contrast, the H3K4me3 pattern identified was not similarly informative. The classification potential of these genes was only partially preserved in a cohort of 101 patients who received first-line tamoxifen treatment, suggesting some treatment selectivity in patient classification. Cancer Res; 73(22); 6632-41. ©2013 AACR.
    Cancer Research 11/2013; 73(22):6632-41. · 8.65 Impact Factor
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    ABSTRACT: Traditional methods that aim to identify biomarkers that distinguish between two groups, like Significance Analysis of Microarrays or the t-test, perform optimally when such biomarkers show homogeneous behavior within each group and differential behavior between the groups. However, in many applications, this is not the case. Instead, a subgroup of samples in one group shows differential behavior with respect to all other samples. To successfully detect markers showing such imbalanced patterns of differential signal, a different approach is required. We propose a novel method, specifically designed for the Detection of Imbalanced Differential Signal (DIDS). We use an artificial dataset and a human breast cancer dataset to measure its performance and compare it with three traditional methods and four approaches that take imbalanced signal into account. Supported by extensive experimental results, we show that DIDS outperforms all other approaches in terms of power and positive predictive value. In a mouse breast cancer dataset, DIDS is the only approach that detects a functionally validated marker of chemotherapy resistance. DIDS can be applied to any continuous value data, including gene expression data, and in any context where imbalanced differential signal is manifested.
    Nucleic Acids Research 09/2013; · 8.28 Impact Factor
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    Dataset: 2403373a
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    ABSTRACT: Reporter genes integrated into the genome are a powerful tool to reveal effects of regulatory elements and local chromatin context on gene expression. However, so far such reporter assays have been of low throughput. Here, we describe a multiplexing approach for the parallel monitoring of transcriptional activity of thousands of randomly integrated reporters. More than 27,000 distinct reporter integrations in mouse embryonic stem cells, obtained with two different promoters, show ∼1,000-fold variation in expression levels. Data analysis indicates that lamina-associated domains act as attenuators of transcription, likely by reducing access of transcription factors to binding sites. Furthermore, chromatin compaction is predictive of reporter activity. We also found evidence for crosstalk between neighboring genes and estimate that enhancers can influence gene expression on average over ∼20 kb. The multiplexed reporter assay is highly flexible in design and can be modified to query a wide range of aspects of gene regulation.
    Cell 08/2013; 154(4):914-27. · 31.96 Impact Factor
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    ABSTRACT: In most colorectal cancer (CRC) patients, outcome cannot be predicted because tumors with similar clinicopathological features can have differences in disease progression and treatment response. Therefore a better understanding of the CRC biology is required to identify those patients who will benefit from chemotherapy and to find a more tailored therapy plan for other patients. Based on unsupervised classification of whole genome data from 188 stage I-IV CRC patients, a molecular classification was developed that consist of at least three major intrinsic subtypes (A-, B-, C-type). The subtypes were validated in 543 stage II-III patients and were associated with prognosis and benefit from chemotherapy. The heterogeneity of the intrinsic subtypes is largely based on 3 biological hallmarks of the tumor: epithelial-to-mesenchymal transition, deficiency in mismatch repair genes that result in high mutation frequency associated with MSI, and cellular proliferation. A-type tumors, observed in 22% of the patients, have the best prognosis, have frequent BRAF mutations and a deficient DNA mismatch repair system. C-type patients (16%) have the worst outcome, a mesenchymal gene expression phenotype, and show no benefit from adjuvant chemotherapy treatment. Both A-type and B-type tumors have a more proliferative and epithelial phenotype and B-types benefit from adjuvant chemotherapy. B-type tumors (62%) show a low overall mutation frequency consistent with the absence of DNA mismatch repair deficiency. Classification based on molecular subtypes made it possible to expand and improve CRC classification beyond standard molecular and immunohistochemical assessment and might help in the future to guide treatment in CRC patients. © 2013 Wiley Periodicals, Inc.
    International Journal of Cancer 07/2013; · 6.20 Impact Factor
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    ABSTRACT: Intrinsic subtypes are widely accepted for the classification of breast cancer. Lacking gene expression data, surrogate classifications based on immunohistochemistry (IHC) have been proposed. A recent St. Gallen consensus meeting recommends to use this "surrogate intrinsic subtypes" for predicting adjuvant chemotherapy resistance, implying that "Surrogate Luminal A" breast cancers should only receive endocrine therapy. In this study we assessed both gene expression based intrinsic subtypes as well as surrogate intrinsic subtypes regarding their power to predict neoadjuvant chemotherapy benefit. Single institution data of 560 breast cancer patients were reviewed. Gene expression data was available for 247 patients. Subtypes were determined on the basis of IHC, Ki67, histological grade, endocrine responsiveness, and gene expression, and were correlated with chemotherapy response and recurrence-free survival. In ER+/HER2- tumors, a high histological grade was the best predictor for chemotherapy benefit, both in terms of pCR (p = 0.004) and recurrence-free survival (p = 0.002). The gene expression based and surrogate intrinsic subtype based on Ki67 had no predictive or prognostic value in ER+/HER2- tumors. Histological grade, ER, PR, and HER2 were the best predictive factors for chemotherapy response in breast cancer. We propose to continue the conventional use of these markers.
    Breast Cancer Research and Treatment 07/2013; · 4.47 Impact Factor
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    ABSTRACT: BACKGROUND: We investigated whether genomic aberrations in primary colorectal cancer (CRC) can identify patients who are at increased risk of developing additional hepatic recurrence after colorectal liver metastases (CLM) resection. METHODS: Primary tumour DNA from 79 CLM resected patients was analysed for recurrent copy number changes (12x135k NimbleGen(™) aCGH). The cohort was divided into three groups: CLM patients with a recurrence-free survival after hepatic resection of at least 5 years (n = 21), patients who developed intra-hepatic recurrence (n = 32), and patients who developed extrahepatic recurrence (n = 26). By contrasting the primary tumour profiles of recurrence free and the extrahepatic recurrence CLM patients, a classifier, the extra-hepatic recurrence classifier (ERC1), predictive for subsequent extrahepatic-recurrence was developed. RESULTS: The ERC1 had an accuracy of 70 % (95 % confidence interval (CI): 55-82 %, misclassification error 30 %, base error rate: 45 %). This analysis identified a region on Chromosome 12p13 as differentially aberrated between these two groups. The classifier was further optimized by contrasting the extrahepatic recurrence group with the combined group of intrahepatic and no recurrence group, resulting in an extrahepatic prognostic classifier (ERC2) able to classify patients with CLMs suitable for hepatic resection with 74 % accuracy (95 % CI: 62-83 %, misclassification error 26 %, base error rate: 32 %). CONCLUSIONS: Patients with CLM who will develop extrahepatic recurrence may be identified with ERCs based on information in the primary tumour. Risk estimates for the occurrence of extrahepatic metastases may allow a reduction of hepatic resections of colorectal liver metastases for those who are unlikely to develop extrahepatic metastases.
    Annals of Surgical Oncology 06/2013; · 4.12 Impact Factor
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    ABSTRACT: Previously, we employed bacterial artificial chromosome (BAC) array comparative genomic hybridization (aCGH) profiles from BRCA1 and -2 mutation carriers and sporadic tumours to construct classifiers that identify tumour samples most likely to harbour BRCA1 and -2 mutations, designated 'BRCA1 and -2-like' tumours, respectively. The classifiers are used in clinical genetics to evaluate unclassified variants, and patients for which no good quality germline DNA is available. Furthermore, we have shown that breast cancer patients with BRCA-like tumour aCGH profiles benefit substantially from platinum-based chemotherapy, potentially due to their inability to repair DNA double strand breaks (DSB), providing a further important clinical application for the classifiers. The BAC array technology has been replaced with oligonucleotide arrays. To continue clinical use of existing classifiers, we mapped oligonucleotide aCGH data to the BAC domain, such that the oligonucleotide profiles can be employed as in the BAC classifier. We demonstrate that segmented profiles derived from oligonucleotide aCGH show high correlation with BAC aCGH profiles. Furthermore, we trained a support vector machine score to objectify aCGH profile quality. Using the mapped oligonucleotide aCGH data, we show equivalence in classification of biologically relevant cases between BAC and oligonucleotide data. Furthermore, the predicted benefit of DSB inducing chemotherapy due to a homologous recombination defect is retained. We conclude that oligonucleotide aCGH data can be mapped to and used in the previously developed and validated BAC aCGH classifiers. Our findings suggest that it is possible to map copy number data from any other technology in a similar way.
    Breast Cancer Research and Treatment 05/2013; · 4.47 Impact Factor
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    ABSTRACT: Breast cancer is the most common cancer among women. Common variants at 27 loci have been identified as associated with susceptibility to breast cancer, and these account for ∼9% of the familial risk of the disease. We report here a meta-analysis of 9 genome-wide association studies, including 10,052 breast cancer cases and 12,575 controls of European ancestry, from which we selected 29,807 SNPs for further genotyping. These SNPs were genotyped in 45,290 cases and 41,880 controls of European ancestry from 41 studies in the Breast Cancer Association Consortium (BCAC). The SNPs were genotyped as part of a collaborative genotyping experiment involving four consortia (Collaborative Oncological Gene-environment Study, COGS) and used a custom Illumina iSelect genotyping array, iCOGS, comprising more than 200,000 SNPs. We identified SNPs at 41 new breast cancer susceptibility loci at genome-wide significance (P < 5 × 10(-8)). Further analyses suggest that more than 1,000 additional loci are involved in breast cancer susceptibility.
    Nature Genetics 03/2013; 45(4):353-361. · 35.21 Impact Factor
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    Ewald van Dyk, Marcel J T Reinders, Lodewyk F A Wessels
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    ABSTRACT: Tumor formation is partially driven by DNA copy number changes, which are typically measured using array comparative genomic hybridization, SNP arrays and DNA sequencing platforms. Many techniques are available for detecting recurring aberrations across multiple tumor samples, including CMAR, STAC, GISTIC and KC-SMART. GISTIC is widely used and detects both broad and focal (potentially overlapping) recurring events. However, GISTIC performs false discovery rate control on probes instead of events. Here we propose Analytical Multi-scale Identification of Recurrent Events, a multi-scale Gaussian smoothing approach, for the detection of both broad and focal (potentially overlapping) recurring copy number alterations. Importantly, false discovery rate control is performed analytically (no need for permutations) on events rather than probes. The method does not require segmentation or calling on the input dataset and therefore reduces the potential loss of information due to discretization. An important characteristic of the approach is that the error rate is controlled across all scales and that the algorithm outputs a single profile of significant events selected from the appropriate scales. We perform extensive simulations and showcase its utility on a glioblastoma SNP array dataset. Importantly, ADMIRE detects focal events that are missed by GISTIC, including two events involving known glioma tumor-suppressor genes: CDKN2C and NF1.
    Nucleic Acids Research 03/2013; · 8.28 Impact Factor
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    ABSTRACT: Histone deacetylases (HDACs) are epigenetic erasers of lysine-acetyl marks. Inhibition of HDACs using small molecule inhibitors (HDACi) is a potential strategy in the treatment of various diseases and is approved for treating hematological malignancies. Harnessing the therapeutic potential of HDACi requires knowledge of HDAC-function in vivo. Here, we generated a thymocyte-specific gradient of HDAC-activity using compound conditional knockout mice for Hdac1 and Hdac2. Unexpectedly, gradual loss of HDAC-activity engendered a dosage-dependent accumulation of immature thymocytes and correlated with the incidence and latency of monoclonal lymphoblastic thymic lymphomas. Strikingly, complete ablation of Hdac1 and Hdac2 abrogated lymphomagenesis due to a block in early thymic development. Genomic, biochemical and functional analyses of pre-leukemic thymocytes and tumors revealed a critical role for Hdac1/Hdac2-governed HDAC-activity in regulating a p53-dependent barrier to constrain Myc-overexpressing thymocytes from progressing into lymphomas by regulating Myc-collaborating genes. One Myc-collaborating and p53-suppressing gene, Jdp2, was derepressed in an Hdac1/2-dependent manner and critical for the survival of Jdp2-overexpressing lymphoma cells. Although reduced HDAC-activity facilitates oncogenic transformation in normal cells, resulting tumor cells remain highly dependent on HDAC-activity, indicating that a critical level of Hdac1 and Hdac2 governed HDAC-activity is required for tumor maintenance.
    Blood 01/2013; · 9.06 Impact Factor
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    ABSTRACT: Integrating gene expression data with secondary data such as pathway or protein-protein interaction data has been proposed as a promising approach for improved outcome prediction of cancer patients. Methods employing this approach usually aggregate the expression of genes into new composite features, while the secondary data guide this aggregation. Previous studies were limited to few data sets with a small number of patients. Moreover, each study used different data and evaluation procedures. This makes it difficult to objectively assess the gain in classification performance. Here we introduce the Amsterdam Classification Evaluation Suite (ACES). ACES is a Python package to objectively evaluate classification and feature-selection methods and contains methods for pooling and normalizing Affymetrix microarrays from different studies. It is simple to use and therefore facilitates the comparison of new approaches to best-in-class approaches. In addition to the methods described in our earlier study (Staiger et al., 2012), we have included two prominent prognostic gene signatures specific for breast cancer outcome, one more composite feature selection method and two network-based gene ranking methods. Employing the evaluation pipeline we show that current composite-feature classification methods do not outperform simple single-genes classifiers in predicting outcome in breast cancer. Furthermore, we find that also the stability of features across different data sets is not higher for composite features. Most stunningly, we observe that prediction performances are not affected when extracting features from randomized PPI networks.
    Frontiers in Genetics 01/2013; 4:289.

Publication Stats

5k Citations
1,182.69 Total Impact Points

Institutions

  • 2012
    • Centrum Wiskunde & Informatica
      Amsterdamo, North Holland, Netherlands
  • 1999–2012
    • Delft University Of Technology
      • • Faculty of Electrical Engineering, Mathematics and Computer Sciences (EEMCS)
      • • Department of Biotechnology
      Delft, South Holland, Netherlands
  • 2004–2011
    • Netherlands Cancer Institute
      • • Division of Experimental Therapy
      • • Division of Molecular Biology
      Amsterdamo, North Holland, Netherlands
  • 2006
    • Leiden University Medical Centre
      • Department of Medical Statistics and Bioinformatics
      Leiden, South Holland, Netherlands
    • Slotervaartziekenhuis
      Amsterdamo, North Holland, Netherlands