dataesr/scientific-paragraphs-categorization huggingface dataset:
Maximum likelihood estimations of both, hazard rates of the semi-Markov process and hazard rates of sojourn times can be deduced. Moreover, the effects of covariates on the process evolution can be studied using a semi-parametric Cox model for the distributions of sojourn times. The number of states, the possible transitions between them and the number of covariates affecting each transition can be chosen in order to fit sparse models adapted to a specific application. Package SemiMarkov is available from the Comprehensive R Archive Network (CRAN) at http://CRAN. R-project. org/package=SemiMarkov. The rest of this paper is organized as follows. Section 2 describes the multi-state semi-Markov models and the parametric maximum likelihood estimation used in the SemiMarkov package. Section 3 describes the SemiMarkov package whereas the Section 4 illustrates the different functions included in the package through an example on severe asthma.
Format of data A data set asthma is included in the SemiMarkov package. This cohort study (longitudinal data) of severe asthmatic patients can be analyzed using multi-state semi-Markov models. The data frame to be used in the function semiMarkov must be similar to the asthma data: a The rows must be grouped by individuals and ordered chronologically within groups. By definition of a semi-Markov model the waiting times must be known. Therefore, transitions between the same states are not possible. If such transitions are observed, the row must be combined with the next transition to obtain a transition from state h to state j with h = j. The last sojourn time of a semi-Markov process is observed only when the process enters an absorbing state. In other cases, the final state and the last sojourn time is unknown due to the right-censoring process.
Functions description Following is a brief description of the package functions. hazard: For any object of classes 'semiMarkov' and 'param. init', the function computes the values of the hazard rate of sojourn times or the values of the hazard rate of the semi-Markov process for a given vector of times. summary and print: Summary and printing methods for objects of classes 'semiMarkov' and 'hazard'. plot: Plot method for objects of class 'hazard'.
Sojourn times distribution The parametric estimation in homogeneous semi-Markov models is based on the specification of the sojourn times distribution. The following distributions are available in the package SemiMarkov: exponential ("E", "Exp" or "Exponential"), Weibull ("W" or "Weibull") and exponentiated Weibull ("EW", "EWeibull" or "Exponentiated Weibull"). If the logical value TRUE is given then the default is the Weibull distribution. These distributions are nested when the appropriate parameters are equal to 1 (see Section 2). The estimations of the distribution parameters are given with standard deviations and p values of the Wald test (H 0 : Θ hj = 1 is the default null hypothesis). One can then evaluate, for instance, the relevance of the exponentiated Weibull distribution in comparison to the Weibull or the exponential distribution.
Application to asthma control data A follow-up study of severe asthmatic patients was conducted in France between 1997 and 2001 by ARIA (Association pour la Recherche en Intelligence Artificielle). Adult asthmatics were prospectively enrolled over a 4-year period by a number of French chest physicians. The data reflects the real follow-up of patients consulting at varied times according to their perceived needs. At each visit, several covariates were recorded and asthma was evaluated using the concept of control scores (Saint-Pierre, Bourdin, Chanez, Daures, and Godard 2006). The control scores can be used to define the subject's state at each consultation. The considered model to study the evolution of asthma consists of three transient states (Figure  1 ): the optimal control (State 1), the sub-optimal control (State 2) and the unacceptable control (State 3).
A random selection of 371 patients with at least two visits (data asthma) is included in the package SemiMarkov. A total of 557 transitions between states are observed and no deaths are reported. Together with the control scores at each time, three covariates are included R> BMI <-as. data. frame(asthma$BMI) R> fit <-semiMarkov(data = asthma, states = states, mtrans = mtrans, + cov = BMI) The semiMarkov function provides estimations of parameters of the waiting times distributions, the standard deviations, the confidence intervals and the Wald test statistics (H 0 : θ hj = 1). One can observe that the coefficient ν 23 associated to the transition from state 2 to state 3 is not significantly different from 1. The exponential distribution can then be used instead of the Weibull distribution for this transition.
Discussion Semi-Markov multi-state models are proven to be very useful in various applications. They are extensions of Markov models in which the evolution of a process is independent from time spent in a state between two consecutive events. Such an assumption is too stringent in some applications. In such case, semi-Markov models are of great interest for modeling the sojourn (waiting) times distributions. However, the implementation of such approach is complex and there are barely any packages or macros to adjust such models. The SemiMarkov package allows to fit parametric homogeneous semi-Markov models by maximizing the likelihood. The choice of waiting times distributions, in particular the exponentiated Weibull distribution, allows to fit various shapes of hazard rates functions. An advantage of the parametric approach is the possibility to study the effects of covariates via a proportional hazard model.
In order to obtain sparse models adapted to the process of interest, the user can choose the number of covariates and the distributions of waiting times for each transition. Some extensions of the SemiMarkov package could be of interest. For instance, the package could be updated to deal with more waiting times distributions. The methodology can be adapted to include random effects in order to deal with the correlation between subjects. Interval censored data could also be analyzed using a penalized likelihood approach  (Foucher, Giral, Soulillou, and Daures 2010)  or using an estimation method with piecewise constant hazard rates  (Kapetanakis, Matthews, and Hout 2012). The optimization step is a crucial point and needs to be investigated. Indeed, the multi-state approach is often limited by the number of parameters with several covariates.
PockDrug druggability model Protein datasets and pocket estimation methods. The first set used to construct PockDrug druggability model was the largest currently freely available dataset, i. e. the 'NonRedundant dataset of Druggable and Less Druggable binding sites' (NRDLD), proposed by Krasowski et al. (5). The NRDLD set contains 113 non-redundant complex proteins sharing a pairwise sequence identity of less than 60% and includes a large diversity of enzymes, such as oxidoreductases, ligases and hydrolases. It corresponds to 71 pockets classified as druggable (i. e. binding site that non-covalently binds small drug-like molecules, which are orally available and do not require administration as prodrugs) and 42 as less druggable, as defined by Krasowski et al. (5). Later, this NRDLD set was split into one training set and one independent test set, based on the identical division used by Krasowski et al.
(5)  and Desaphy et al. (6). The second pocket set used to validate PockDrug model when extrapoling to apo pockets, was collected from the 'Druggable Cavity Directory' (DCD) database (http: //fpocket. sourceforge. net/dcd), proposed by Schmidtke et al. (7). It includes 139 proteins in the apo form (later referred to as Apo139), all of which have an equivalent holo form; 132 were classified as druggable. During PockDrug model construction, different pocket estimation methods were considered: (i) estimation method guided by the ligand position information corresponds to the extraction of protein atoms localized within two fixed distance thresholds of 4 and 5. 5 Å from a binding ligand, respectively known as prox4 and prox5. 5; (ii) the two considered pocket estimation methods, not guided by the ligand position, are fpocket  (14)  and DoGSite  (15) , recently used  (16) (17) (18) (19)  and proposing their own druggability prediction methods: pocket druggability score  (7)  and DoGSiteScorer  (9).
For instance, pockets exhibiting >10 residues but <14 can correspond either to smallest druggable pockets or to decoy pockets (as defined by Hajduk et al. (22) ). Protein query can be an apo or a holo protein. In the case of a holo-protein the ligand can be included in the protein file or uploaded apart so an optional field for ligand information is additionally proposed. The user can also submit a single job for a set of apo or holo proteins in the form of a list of PDB codes. In this case the same previous protocol is applied so for each PDB code included in the list, pocket(s) is (are) estimated and its corresponding druggability probability is then predicted.
Output: Pocket descriptors and druggability prediction. The output page may consist of one or two tab(s), varying accordingly to the choice of one or two estimation method(s): one result tab per selected estimation method. Relative to the input type, two result displays are possible: (i) If the submitted query corresponds to a single pocket structure or a single protein entry (PDB code or PDB file) each tab is structured as following: (a) Sortable table(s): 1 or 2 tables can be provided for each query; one table corresponding to pockets having at least 14 residues, and if necessary, a second table displaying smaller pockets (<14 residues), in order to help the user to distinguish the decoys from pockets: (1) In table 1, corresponding pockets with more than 14 residues are large enough to bind a ligand and the druggability probability can be directly interpreted.
See section of pocket comparison in the supplementary data for the definition of overlapping scores. A bookmark button saving the link on which the user can follow the evolution of his job, access and download it within 7 days. -Protein PDB code: giving access to the detailed result page as it is described previously in this paragraph (case i) -Number of estimated pockets (for each method) -Number of druggable pockets (druggability probability greater than 0. 5) -The highest druggability probability and its standard deviation Computation time. Once the job is submitted, all the input data is checked and the computation begins on our server. A holding page, giving the link on which the user can check the progress of his job is displayed while awaiting the end of calculations. Computation time is relatively short; it mainly depends on the protein and pocket size and the choice of estimation methods as well.
Generally, the computation takes approximately a minute to a medium-sized protein or a pocket entry. Calculation time can be longer for a dataset (file of PDB code list). During the submission, the user can optionally provide his email address to be notified once the job is done and receive results. This option is recommended in the case of a PDB list. In order to test the server and to reduce computational time delays, the whole RCSB Protein Data Bank  (25)  was downloaded, all estimations using two proposed estimation methods (prox4 and fpocket) and pocket descriptor computations were performed. Resulting associated druggability probabilities of pockets estimated were pre-computed and stored on our servers. Therefore, displaying results tends to be relatively fast. Calculations for the other prox method thresholds are being generated.
A monthly automatic update of our database is implemented by adding new PDB entries that are recently included in the PDB. If one user query is a PDB code not pre-included in our database, the results will be added to our pool. Web server implementation. The entire web server has been implemented using Python, HTML, CSS and Javascript. Jmol Applet has been used for the protein 3D structure visualization. The descriptor computation scripts have been written in python and bash while the PockDrug LDA model has been developed using R. External softwares have been also used: fpocket  (14) , RADI, available in website http://petitjeanmichel. free. fr/itoweb. petitjean. freeware. html, NACCESS V2. 1. 1, available in the website http://www. bioinf. manchester. ac. uk/naccess/ and Open Babel  (26).
A comprehensive help section is available on the website to help the user understand each of the steps involved and infer the output of the results produced in the workflow. Acetylcholinesterase, an example of target with several pockets predicted as druggable. Here, we present an example of the acetylcholinesterase (AChE) protein. AChE Irreversible inhibitors may have various serious effects some of which can be fatal. In contrast reversible inhibitors, occupying also the esteratic site, are used to treat a range of central nervous system diseases. Several cholinesterase inhibitors are currently either being used for symptomatic treatment of Alzheimer's disease or are in advanced clinical trials. We choose the AChE holo-protein complexed with the antialzheimer drug E20 marketed as Ariceptand (PDB code 1EVE)  (27).
This protein consisting of 534 residues, was used for recent computational investigations  (28, 29). AChe, denoted as 1eve for the rest of this study, was not included in the training set used to build PockDrug model. The E20 binding pocket is tested by PockDrug-Server. A first query consists on the using of an external pocket estimation method (DoGSite, input case i), (see results in output part of the Figure  1 ). PockDrug-Server returns an average druggability probability of 0. 98 (±0. 01). When the same pocket is estimated and directly predicted by DoGSiteScorer  (9) , an ambiguous druggability score of 0. 56 is assigned for this pocket, ranking it as a 'potentially/difficult druggable pocket'. PockDrug-Server result is in a good agreement with the literature  (30)  and the annotation of DCD database confirming that E20 binding site corresponds to a druggable pocket.
The analysis and management of MS data, especially those generated by data independent MS acquisition, exemplified by SWATH-MS, pose significant challenges for proteomics bioinformatics. The large size and vast amount of information inherent to these data sets need to be properly structured to enable an efficient and straightforward extraction of the signals used to identify specific target peptides. Standard XML based formats are not well suited to large MS data files, for example, those generated by SWATH-MS, and compromise high-throughput data processing and storing. We developed mzDB, an efficient file format for large MS data sets. It relies on the SQLite software library and consists of a standardized and portable server-less single-file database. An optimized 3D indexing approach is adopted, where the LC-MS coordinates (retention time and m/z), along with the precursor m/z for SWATH-MS data, are used to query the database for data extraction.
In comparison with XML formats, mzDB saves ϳ25% of storage space and improves access times by a factor of twofold up to even 2000-fold, depending on the particular data access. Similarly, mzDB shows also slightly to significantly lower access times in comparison with other formats like mz5. Both C؉؉ and Java implementations, converting raw or XML formats to mzDB and providing access methods, will be released under permissive license. mzDB can be easily accessed by the SQLite C library and its drivers for all major languages, and browsed with existing dedicated GUIs. The mzDB described here can boost existing mass spectrometry data analysis pipelines, offering unprecedented performance in terms of efficiency, portability, compactness, and flexibility.
literature  (14 -17). These include the verbosity of language syntax, the lack of support for multidimensional chromatographic analyses, and the low performance showed during data processing. Although XML standards were originally conceived as a format for enabling data sharing in the community, they are commonly used as the input for MS data analysis. Latest software tools  (18, 19)  are usually only compatible with mzML files, limiting de facto the throughput of proteomic analyses. To tackle these issues, some independent laboratories developed open formats relying on binary specifications  (14, 17, 20, 21) , to optimize both file size and data processing performance. Similar efforts started already more than ten years ago, and, among the others, the NetCDF version 4, first described in 2004, added the support for a new data model called HDF5.
Because it is particularly well suited to the representation of complex data, HDF5 was used in several scientific projects to store and efficiently access large volumes of bytes, as for the mz5 format  (17). Compared with XML based formats, mz5 is much more efficient in terms of file size, memory footprint, and access time. Thus, after replacing the JCAMP text format more than 10 years ago, netCDF is nowadays a suitable alternative to XML based formats. Nonetheless, solutions for storing and indexing large amounts of data in a binary file are not limited to netCDF. For instance, it has been demonstrated that a relational model can represent raw data, as in YAFMS format  (14) , which is based on SQLite, a technology that allows implementing a portable, self-contained, single file database. Similarly to mz5, YAFMS is definitely more efficient in terms of file size and access times than XML.
However, it would still be a sub-optimal solution because of redundant loads of full MS spectra, whereas only a small spectral window centered on the peptide m/z is of interest. Thus the quantification of dozens of thousands of peptides  (32, 33)  requires appropriate data access methods to cope with the repetitive and high load of MS data. We therefore deem that an ideal file format should show comparable efficiency regardless of the particular use case. In order to achieve this important flexibility and efficiency on any data access, we developed a new solution featuring multiple indexing strategies: the mzDB format (i. e. m/z database). As the YAFMS format, mzDB is implemented using SQLite, which is commonly adopted in several computational projects and is compatible with most programming languages.
EXPERIMENTAL PROCEDURES MS Data Sets-To perform the evaluation of the different file formats on DDA data, a total lysate of cultured primary human vascular ECs was used. It was submitted to 1D-SDS-PAGE and fractionated into 12 gel bands, processed as described before (45). Peptides were eluted during an 80 min gradient by nanoLC-MS/MS using an Ultimate 3000 system (Thermo Scientific Dionex, Sunnyvale, CA) coupled to an LTQ-Orbitrap Velos mass spectrometer (Thermo Fisher Scientific Inc. , Waltham, MA). The LTQ-Orbitrap Velos was operated in data-dependent acquisition mode with the XCalibur software. Survey scan MS were acquired in the Orbitrap on the 300 -2000 m/z range with the resolution set to a value of 60,000. The 10 most intense ions per survey scan were selected for CID fragmentation and the resulting fragments were analyzed in the linear trap (LTQ).
e. profile mode encoding), indexing enabled, and zlib compression disabled. The raw files were converted into mzDB using the in-house software tool "raw2mzDB. exe" (see "Implementations" in the results section) with the default bounding boxes dimensions: time width of 15 s and m/z width of 5 Da for MS bounding boxes, one bounding box per MS/MS spectrum (time width of 0 s and m/z width of 10,000 Da). The integrity of mzDB data was checked by comparing the MD5 signature of spectra values between the mzDB and the mz5 file formats (data not shown). To evaluate the sequential reading time, the twelve acquired DDA files were used, and from this small MS data set, we created a large and heterogeneous panel of data files using a procedure similar to the one used for mz5 benchmarking  (17). Each file was repeatedly truncated with an increasing limit on the number of spectra (step size was set to 800 spectra), until the total size of the original file was reached.
In the case of the mzDB files, all kinds of data access and tests were performed using the "pwiz-mzDB" library that is built in Cϩϩ on the same model as "msaccess," to ensure homogeneous reading methods for all file formats. The benchmarks based on the SWATH-MS data consisted of targeted data extraction of XICs of different sizes (50 ppm ϫ 60 s and 50 ppm ϫ 200 s) on the four files of increasing size (2, 5, 10, and 25 GB). In addition, the time necessary to establish a connection with the files was also evaluated, as was file size shrinkage. The comparison was run against the open mzXML file format, the standard currently adopted in the ETH lab, by means of in-house developed Java software. In particular, the access to mzXML files was implemented using the Java Proteomic Library (an enhanced version of the Java Random Access Library (JRAP) library from the Seattle Proteome Center) to retrieve the spectra (i.
1 ). The "spectrum" and "run_slice" indexes are SQL native, whereas the "bounding_box_rtree" is an R*Tree index, which is a built-in feature of the SQLite engine. Finally, we also developed a solution optimized for SWATH-MS data where a customized 3D indexing approach is implemented: the LC-MS coordinates (retention time and m/z of the fragment ion) along with the m/z of the precursor are the indexes used to retrieve data when querying the database for targeted data extraction. Currently, state-of-the-art software that process SWATH data based on open formats (i. e. OpenSwath)  (11, 34)  use a split version of each mzXML file. Essentially, the initial SWATH mzXML file is split in many mzXML files, corresponding each to a given "swath" containing the MS/MS fragments from all the parent ions isolated in a given m/z window.
As a result, for each query (extraction of the signal of a particular fragment derived from a precursor) a different data file has to be accessed depending on the m/z of the precursor ion. This results in the management of series of mzXML files, which complicates the analysis, particularly if the number of data sets to compare is high. Conversely, the 3D indexing in mzDB allows interrogating the SWATH-MS data for any precursor by accessing a single data file without any overhead (supplemental Fig. S2 ). Implementations-We developed two software libraries in order to create and handle mzDB data files starting from DDA data, and a third one specific to DIA/SWATH-MS data. The first instance named "pwiz-mzDB" that can be considered as a ProteoWizard extension, is dedicated to the generation and conversion of the mzDB format.
It is written in Cϩϩ language and exploits the ProteoWizard framework  (38)  to read vendor raw file formats and standards such as mzXML, mzML, and mz5. Two command line interfaces are available: "raw2mzDB. exe" that converts the aforementioned formats to the mzDB, and "mzDB2mzML. exe," which performs the reversed conversion to the mzML standard, and thus also allows to read and manipulate the mzDB files. The second instance is a full-featured Java library called "mzDB-access," which allows to read the mzDB format and to optimize the data extraction in the different modes of access. According to the extraction to be performed, it is possible to use the best-suited index among the three available: spectrum, run slice and BB R*Tree index. The third instance named "mzDB- Swath" is a complementary Java library that was explicitly developed for SWATH-MS data, and is clearly adaptable to other specific DIA methods.