dataesr/scientific-paragraphs-categorization huggingface dataset:
Simulations We generated synthetic data using SimuPOP  (Peng & Amos 2008; Peng et al. 2011) , a general-purpose, individual-based simulation platform for forward-in-time population genetic modelling. The Python scripts used to carry out the simulations are available at GitHub (https://github. com/alexvat/simulations). Initially, we simulated three different population structure scenarios, an island model  (Wright 1990 ), a stepping-stone model  (Kimura 1953)  and a dichotomous population fission model that leads to a hierarchical island structure (Fig. S1 , Supporting information). In these cases, we considered four diploid demes, each of constant effective population size N e = 2500. Thus, total population size was 10 000. Table  1  presents a summary of the parameters that were used in the simulations.
The lack of a common exchange format for mathematical models in pharmacometrics has been a long-standing problem. Such a format has the potential to increase productivity and analysis quality, simplify the handling of complex workflows, ensure reproducibility of research, and facilitate the reuse of existing model resources. Pharmacometrics Markup Language (PharmML), currently under development by the Drug Disease Model Resources (DDMoRe) consortium, is intended to become an exchange standard in pharmacometrics by providing means to encode models, trial designs, and modeling steps.
The field of pharmacometrics has progressed at an impressive pace in the past decade. New software tools and methods have become available for estimation tasks as well as for clinical trial simulation and optimal design. 1  The development of new tools, however, brings challenges for the users in terms of integrating them into existing workflows. Typically, this requires manual translation of the underlying pharmacometric model for each tool being used, not only due to differences in model formulation/language but also due to tool capabilities, software-specific methods, and algorithms. Such translation along with the often-needed conversion of associated datasets may introduce errors and takes unnecessary time, as no converters exist. A common exchange format within pharmacometrics, which would reduce the efforts needed to exchange models, is clearly needed.
This was recognized by the partners within the NonLinear Mixed Effects (NLME) Consortium several years ago, and an initial specification for such a format was drafted. Unfortunately, development did not continue beyond the first version. These initial results were not lost altogether and were the starting point when the idea was picked up again in 2011, with the initiation of the Drug Disease Model Resources (DDMoRe) project (http://ddmore. eu)  2  under the European Innovative Medicines Initiative (IMI). DDMoRe aims to "build and maintain a universally applicable, open source, model-based framework, intended as the gold standard for future collaborative drug and disease Modeling & Simulation. " An integral part of this framework is the Pharmacometrics Markup Language (PharmML) exchange format.
Using PharmML, the DDMoRe framework specifically aims to integrate existing tools (Figure  1 ) such as NON-MEM, Monolix, win(open)BUGS, PFIM, PopED, PsN, Xpose, SIMCYP Simulator, MatLab, R, as well as new tools developed within DDMoRe, e. g. , Simulx and infix2pharmml (Table  1  in Supplemental Material). Another major part of DDMoRe, the Model Repository (http://repository. ddmore. eu), is the place where PharmML-coded models can be stored, retrieved, and shared with the community. In this article we discuss the scope and structure of PharmML v. 0. 6, which was released publicly in January 2015 as the second public release (see related websites http://ddmore. eu/ pharmml and http://www. pharmml. org for more information).
MOTIVATION The current situation in pharmacometrics, specifically the lack of a common exchange format, resembles that in other areas of life sciences 10-15 years ago, most notably within systems biology and neurosciences. There, standards such as CellML, SBML, and NeuroML have been developed to encode models of biochemical, cellular, and multicellular processes (Table  2  in Supplemental Material). These standards have transformed the corresponding areas of science. Many new tools have appeared after their establishment and model exchange has become much easier. 3 eveloping a similar, powerful exchange format for pharmacometrics is challenging, because the tools available in this area use a variety of approaches for model encoding. For example, the most popular tool in the field, NONMEM with the NMTRAN language for model specification,  4  allows users to encode virtually any conceivable modeling scenario in an assignment-based style, which gives great flexibility but also makes standardization difficult.
On the other hand, Monolix with the MLXTRAN language for model specification 5 uses a declarative style with a clearly defined vocabulary, grammar, and clear language boundaries. Ensuring that models formulated using both approaches can be implemented in an exchange format is very demanding and requires an adaptable structure. The different data formats or data file layouts being used add to the complexity of the problem. The challenge the field is facing is also a consequence of the complexity and scope of pharmacometrics, with models at different scales (from models of intracellular pathways to whole body models) being applied for different purposes (from descriptive models to clinical trial simulation models) to address a wide range of problems in drug development. Tool support for PharmML is a demanding engineering task because it requires good understanding of both the pharmacometrics and computational science.
Despite the complexity of the task, we firmly believe that we are able to make the daily work for pharmacometricians easier by alleviating the burden of translating models and converting datasets. Most important, as recoding is a potential source of errors, a model should only have to be encoded once, regardless of how many different tools use it within a given workflow. To summarize, a common exchange format is expected to facilitate: • Smooth and error-free transmission of models between tools. • Use of complex workflows via standardized model and output definitions (Figure  2 ). • Reproducibility of research. • Easier reporting and bug tracking. • Improved interaction with regulatory agencies regarding modeling and simulation. • Reuse of existing model resources, e. g. , BioModels database.
Pharmacokinetic (PK) models, as the most frequently used models, can also be encoded using PK macros, a concept borrowed from MLXTRAN, which allows an equation-free encoding of a vast number of compartment models using predefined macros. The Observation Model supports continuous data models with a flexible residual error model as well as different types of discrete data models, e. g. , categorical, count, and time-to-event. Here, both declarative and assignment-based encoding styles are also supported. The Trial Design section is based on a CDISC standard 9 and plays a central role in encoding of simulation and optimal design tasks, but it can also be used for estimation tasks. In contrast to the traditional approach, where the trial design is implemented within the dataset, this element permits formulating a study design in a dataset-independent manner.
Using only a few basic elements, it is possible to encode complex designs, e. g. , crossover trials with multiple arms, epochs, occasions, treatment types, and/or washout events. Within the Trial Design element, PharmML also has distinct placeholders for covariates, dosing records, and observations, and, depending on the task, only the relevant records have to be provided. Here, it starts with data processing in R, which can consist of data formatting, merging, and/or missing data imputation. After that an explanatory analysis is carried out in MlxPlore, followed by estimation using either Monolix or NONMEM. Subsequent steps are bootstrapping using PsN, clinical trial simulation in MatLab/Simulx, and finally Optimal Design in either PFIM or PopED. At every step of the workflow, the PharmML model can be stored and the results following each step can be recorded the corresponding SO file.
Documenting workflows in such a detailed way can potentially simplify reporting and ensures reproducibility. The third section, Modeling Steps, is used to define basic tasks to be performed with the model. Currently, two are supported: estimation and simulation. For estimation, the default option is that all information about the underlying trial design is given implicitly by the dataset (the alternative, Trial Design, is in such cases not needed) with appropriate mapping to relevant parts of the Model Definition. In addition, initial estimates with or without boundaries have to be provided along with basic settings for the particular purpose, e. g. , estimation of individual parameters, estimation of population parameters, or calculation of the Fisher information matrix. For simulation, information about the underlying trial design can either be specified in the Trial Design section or sourced from a dataset.
INTEROPERABILITY AND FUTURE PLANS PharmML has been designed for the exchange of models between tools. Users will be able to write models using a human readable language also developed within DDMoRe, the Modeling Description Language (MDL) (http://ddmore. eu/mdl). To facilitate its use, an Integrated Development Environment tool (MDL-IDE) is available, within which the model is automatically translated to PharmML and can be passed to PharmML-compatible tools. Development of the MDL and the MDL-IDE is still ongoing, but initial results are very promising. Alternatively, in when only the Structural Model is required, modelers can use the web-editor infix2pharmml (http://infix2pharmml. sourceforge. net/) to draft ready-touse PharmML models. Another key element of the DDMoRe framework is lib-PharmML (https://sourceforge.
Acknowledgments. We thank  Wendy Aartsen ,  Landry Cochard ,  Elisabetta Cargnello ,  Geraldine Dupin , and  Angel Rafael  for their outstanding support. The research leading to these results received support from the  Innovative Medicines Initiative Joint Undertaking  under grant agreement  115156 , resources of which are composed of financial contributions from the  European Union 's  Seventh Framework Programme  ( FP7/2007-2013 ) and EFPIA companies' in kind contribution. The  DDMoRe  project is also supported by financial contribution from  Academic  and SME partners.
Genome-wide screen of Pex11 localization We used high-content microscopy on a genome-wide scale to accurately determine the subcellular localization pattern of yeast Pex11 in all non-essential gene deletion mutants and in temperature-sensitive essential gene mutants. Pex11 was genetically tagged with green fluorescent protein (GFP) in the library of 4292 non-essential gene deletion strains and 793 strains with temperature-sensitive alleles of 503 essential genes. Fluorescence microscopy was performed using a confocal microscope to screen the whole collection in matter of days, as previously described  [16]. All the images are available in the online image database YPLpex ||. The majority of tested mutants did not significantly affect Pex11-GFP localization compared to the reference strain, whereas abnormal Pex11-GFP localization patterns were assigned to the 10% (483) of strains with the phenotype farthest away from the wild type.
Computational analysis pipeline for microscopy images generated in the screen The computational analysis pipeline for images generated in the genome-wide screen involved image preprocessing and analysis, data mining for outlier detection and clustering, data visualization and gene set enrichment analysis (Fig. 3 ). The pipeline designed in Orange data mining toolbox  [24]  allowed us to systematically analyze and explore data and to define a workflow that could be used in subsequent studies. This result advances the recently proposed analysis protocol  [25]  that is based on individual inspection of strain images and their visual comparison with reference strains by unbiased observers. In our approach, the phenotypes were quantified with vectors containing morphological features extracted from high-content microscopy images.
We defined a workflow consisting of several Orange widgets that collectively performed computational analysis of mutant strains generated in the screen. Outlier detection method (its corresponding Orange widget is marked with "1") was applied to the preprocessed morphological profiles with the aim of identifying mutants with substantially different Pex11-GFP localization patterns from those of the reference strains. Hierarchical clustering (its corresponding Orange widget is marked with "2") followed this step and clustered the genes according to the distance between their profiles. Images for one of the clusters are shown. Notably, the illustrated cluster contains strains for two genes, MDM10 and MDM12, which encode two components of the ERMES complex. The clustering results were used for functional classification based on Gene Ontology of genes/ proteins that affect the localization of Pex11.
Object segmentation and extraction of morphological features Object (peroxisome) segmentation and feature extraction were performed on maximum-intensity projections of GFP channel images using CellProfiler, an open-source software package for image analysis  [52]. Peroxisome morphology was summarized with a series of 145 quantitative features describing the distribution of object size, shape and intensity. Collectively, these features define profiles of mutant strains-one real-valued vector per strain-that were used in subsequent computational analysis. Strains with less than 50 or more than 1000 detected objects per image were removed from analysis, since it was likely that profiles of these strains were affected by spurious image segmentation results.
Intuitively, the further the strain is from the area whose frontier is determined by the separating hyperplane, the more pronounced an outlier it is. Hence, we used the magnitude of the outlyingness score to select outlier strains. The cutoff value used in the outlier criterion was selected by searching for the first kink in the scores that were ordered by their magnitudes in descending order (Supplementary Fig. 1 ). We assessed the quality of the outlier list with a Kolmogorov-Smirnov test comparing the score distribution of mutant strains to that of the reference strains. Kolmogorov-Smirnov test is a two-sided test for the null hypothesis that two samples are drawn from the same distribution and can be used if sample sizes are different. Further exploratory analyses of outlying mutants were performed in Orange, an open-source data mining and visualization package  [24].
mCherry was excited at 561 nm and emission was detected between 570 and 600 nm. Deconvolution was applied to improve the quality of acquired z-stacks (Huygens 4. 0, maximum-likelihood estimation method, 5 iterations; SVI, The Netherlands). Image processing and co-localization analysis was performed using the public domain software Fiji  [55]. In brief, processed z-stacks were projected using the maximum-intensity method. Colocalization of GFP and mCherry fluorescence indicated by yellow spots was determined manually in the generated maximum-intensity projections. Co-localization of GFP and mCherry signal was additionally evaluated by interactive inspection of generated multichannel threedimensional volume rendering representations.
Acknowledgments This work was supported by the  Slovenian Research Agency  grants  P1-0207  (M. M. U. , P. K. , M. B. and U. P. ),  P2-0209  (M. Ž. and B. Z. ) and  J2-5480  (B. Z. ); by the  Fonds zur Förderung  der wissenschaftlichen Forschung Projects SFB  F3005  LIPOTOX and PhD program W901-B12 Molecular Enzymology; by  NAWI Graz  (H. W. and S. D. K. ) and by the Slovenian-Austrian bilateral project  BI-AT/ 09-10-019  (S. D. K. and U. P. ).
66, P valueo0. 01 in LUAD; Pearson's correlation coefficient ¼ À 0. 43, P value ¼ 0. 29 in LUSC). F2RL3-probe methylation did not show a statistically significant methylation-expression correlation. Regarding HapMap, we focused on expression (RNA-Seq experiments) and methylation (Illumina HumanMethylation27 BeadChip) data from lymphoblastoid cell lines of 69 HapMap Yoruba individuals. In this case, only the F2RL3-probe is present on the platform and its methylation seems to be significantly inversely correlated with F2RL3 expression (Pearson's correlation coefficient ¼ À 0. 28, P valueo0. 01). Mediation analysis. Although the results described above from the analysis of a discovery set and three validation sets seem to provide evidence that hypomethylation of the cg05575921 and cg03636183 probes is associated with both tobacco exposure and lung cancer risk, the key question is whether their hypomethylation is involved in the causal pathway, or whether they are simply epiphenomena of smoking habits (that is, the association of DNA methylation with lung cancer risk is confounded by smoking).
All participants gave informed consent. The study was approved by the Regional Committee for Medical and Health Research Ethics in North Norway. Data storage and linkage to the National Cancer Registry of Norway were approved by the Norwegian Data Inspectorate; follow-up identified 132 eligible cases of lung cancer by 2011. For each case, one control with adequate blood samples was selected matched on time since blood sampling and year of birth in order to control for effects of storing time and ageing. The cases and the controls were kept together through all later laboratory procedures in order to reduce any batch effects. Validation sets. The MCCS is a prospective cohort study of 41,514 volunteers (24,469 women) aged between 27 and 76 years at baseline (99. 3% of whom were aged 40-69 years)  28.
EPIC controls without any neoplastic disease were randomly matched to the lung cancer cases using an incidence density protocol. Matching was done on the basis of age at baseline ( ± 5 years), gender, smoking status (current and former) and pack years ( ± 1 PY). After initial quality control, 63 incident lung cancer cases (25 adenocarcinomas, 15 squamous cell carcinoma, 19 small-cell lung cancer and 4 uncharacterized lung cancers) with a mean interval between blood draw and diagnosis of 4. 8 years (range: 1. 1-8. 6) and 63 individually matched controls remained for further analysis. This study was approved by the ethics committee of the Medical Faculty of the University of Heidelberg (S-627/2013). DNA methylation measurement and data pre-processing. Genome-wide DNA methylation analyses were performed on pre-diagnostic blood samples using the Illumina Infinium HumanMethylation450 platform.
NOWAC laboratory procedures were carried out at the Human Genetics Foundation (Turin, Italy), using the Illumina Infinium HumanMethylation450 (HM450). Buffy coats stored in liquid nitrogen were thawed, and genomic DNA was extracted using the QIAGEN QIAsymphony DNA Midi Kit. 500 ng of DNA were bisulphite-converted using the Zymo Research EZ-96 DNA Methylation-Gold Kit, and hybridized to Illumina Infinium HumanMethylation450 BeadChips. These were subsequently scanned using the Illumina HiScanSQ system, and sample quality was assessed using control probes present on the micro-arrays. Finally, raw intensity data were exported from Illumina GenomeStudio (version 2011. 1). MCCS laboratory procedures were carried out at the Genetic Epidemiology Laboratory, the University of Melbourne according to manufacturers' protocols.
EPIC HD laboratory procedures were carried out at the German Cancer Research Center (DKFZ) and at LGC Bioscience. Buffy coat DNA was isolated at LGC Bioscience by the company's standardized protocols and returned to DKFZ. DNA methylation profiling with the Illumina Infinium HumanMethylation450 BeadChip array was performed according to the manufacturer's instructions at the DKFZ Genomics and Proteomics Core Facility. Quality control of genomic DNA included three independent measurements with Quant-iT Picogreen dsDNA assay and all samples were tested on 1% agarose gels for DNA integrity. The Zymo Research EZ-96 DNA Methylation Kit was used for bisulfite conversion of DNA. All subsequent steps were performed as described for NOWAC. NOWAC data pre-processing was carried out using in-house software written for the R statistical computing environment.
For each sample and each probe, measurements were set to missing if obtained by averaging intensities over less than three beads, or if averaged intensities were below detection thresholds estimated from negative control probes. Background subtraction (to remove background noise) and dye bias correction (for probes using the Infinium II design) were also performed. The resulting subset of 473,929 probes targeting autosomal CpG loci was selected for further analyses, and among these, probes with missing values in more than 20% of the samples were excluded from the analyses, leaving 450,890 probes. Samples with more than 5% of non-detected probes were also excluded from the analysis (14 samples excluded). For the MCCS, methylation data were normalized to the internal built-in controls as provided by the standard Illumina software and subset-quantile within array normalization for type I and II probe bias correction  33.
Acknowledgements The NOWAC postgenome cohort study has been funded by the  ERC  advanced grant ' Transcriptomics in Cancer Epidemiology-ERC-2008-AdG-232997 '. The methylation analyses for the NOWAC cohort were performed at the  Human Genetics Foundation in Torino, Italy  with funds from  Compagnia di San Paolo. The MCCS nested case-control study of methylation and lung cancer risk was funded by a grant from the  Australian National Health and Medical Research Council  (Project Grant # 1050198 ). For the MCCS, we acknowledge the contribution of  Dr Chol-hee Jung  and  Dr Jessica Chung  from the  Life Sciences Computation Centre  and the  Victorian Life Sciences Computation Initiative in Melbourne  for the preparation of the bioinformatics pipeline and  Drs Ming Wong  and  Jihoon Joo  for their contribution to the methylation analyses conducted in the  Genetic Epidemiology Laboratory ,  Department of Pathology , the  University of Melbourne.
The  NSHDS  nested case-control study of methylation and lung cancer risk was funded by a grant from the  Roy Castle Lung Cancer Foundation  and supported by the  MRC Integrative Epidemiology Unit at the University of Bristol  ( MC_UU_12013_2 ). We are indebted to the laboratory and bioinformatics team ( Dr Wendy McArdle ,  Dr Nabila Kazmi ,  Dr Hashem Shihab  and  Dr Tom Gaunt ) for their contribution to the generation of the  Illumina Infiumiun HumanMethylation450 data  for this analysis. The EPIC-Heidelberg cohort study was conducted with the financial support of the ' Europe Against Cancer' program  of the  European Commission (SANCO) , the  German Cancer Aid (Deutsche Krebshilfe)  and the  German Federal Ministry of Education and Research (BMBF). The present nested case-control study of methylation and lung cancer risk was supported by the  German Center for Lung Research (DZL) , grant  PB13394.
Background: The explosion of NGS (Next Generation Sequencing) sequence data requires a huge effort in Bioinformatics methods and analyses. The creation of dedicated, robust and reliable pipelines able to handle dozens of samples from raw FASTQ data to relevant biological data is a time-consuming task in all projects relying on NGS. To address this, we created a generic and modular toolbox for developing such pipelines. Results: TOGGLE (TOolbox for Generic nGs anaLysEs) is a suite of tools able to design pipelines that manage large sets of NGS softwares and utilities. Moreover, TOGGLE offers an easy way to manipulate the various options of the different softwares through the pipelines in using a single basic configuration file, which can be changed for each assay without having to change the code itself.