dataesr/scientific-paragraphs-categorization huggingface dataset:
AER and SM. AER, SM and LGL performed the data analysis and interpretation. AER and SM drafted the manuscript, and AGP, FIW and LGL provided critical revisions. All authors approved the final version of the manuscript for submission. Funding Source:  French National Agency for Research (ANR , grant  n°ANR-15-CE33-0003 ) and Prevent'Horizon chair Financial Disclosure: The authors have no financial relationship relevant to this article to disclose.
Computational experiments Our algorithm was coded in C++ using the Standard Template Library (STL) for data structures, and compiled using the GNU GCC compiler in a Linux environment. In the following, we present the computational experiments that were carried out in order to tune the algorithm, to evaluate its performance relative to state-of-the-art algorithms, and to assess the contribution of each of its components. All the experiments were conducted on a single thread on an Intel Xeon X7542 CPU at 2. 67GHz processor.
Benchmark instances Our algorithm was tested on the various TOPTW benchmark instances. All the literature benchmarks are available online at https://www. mech. kuleuven. be/en/cib/op. TOPTW benchmark instances are derived from  Solomon et al. 's (Solomon, 1987)  instances for the Vehicle Routing Problem with Time Windows (VRPTW), and Cordeau's  (Cordeau et al. , 1997)  instances for the Multi Depot Periodic VRPTW (MDPVRPTW). The instances were adapted by considering the customer demands in the original data sets as node profits in the TOPTW instances. Travel time between two vertices is assumed to be equal to the euclidean distance. It is rounded down to the first decimal for Solomon's instances and to the second decimal for Cordeau's instances. Solomon's instances are organized into six sets: C100, C200, R100, R200, RC100 and RC200, divided according to the distribution of vertices on the plane (clustered, random, random-clustered) and the width of the time windows (narrow, wide).
Acknowledgments. The authors would like to thank the reviewers for their valuable comments and suggestions which improved the quality of the paper. This work is supported by the  French Environment and Energy Management Agency (ADEME) , the  Hauts-de-France region  and the  European Regional Development Fund (ERDF). It was carried out within the framework of the  Labex   MS2T , funded by the  French Government (Reference   ANR-11-IDEX-0004-02 ). It is also partially supported by the  TCDU  project ( Collaborative Transportation in Urban Distribution ,  ANR-14-CE22-0017 ).
Online Tutorial: https://ccms-ucsd. github. io/GNPSDocumentation/gcanalysis/ Abstract: Gas chromatography-mass spectrometry (GC-MS) represents an analytical technique with significant practical societal impact. Spectral deconvolution is an essential step for interpreting GC-MS data. No public GC-MS repositories that also enable repositoryscale analysis exist, in part because deconvolution requires significant user input. We therefore engineered a scalable machine learning workflow for the Global Natural Product Social Molecular Networking (GNPS) analysis platform to enable the mass spectrometry community to store, process, share, annotate, compare, and perform molecular networking of GC-MS data. The workflow performs auto-deconvolution of compound fragmentation patterns via unsupervised non-negative matrix factorization, using a Fast Fourier Transform-based strategy to overcome scalability limitations.
There are now ~1. 2 million reference spectra, accumulated and curated over a period of >50 years, that are commercially or publicly available for the annotation of GC-MS data  6, 7. To date, many analytical tools and several repositories for GC-MS data have been introduced  5, [8] [9] [10] [11] [12] [13] [14] [15] [16]. Despite these developments, much GC-MS data processing is restricted to vendor-specific formats and software (e. g. VocBinBase  15  uses Leco ChromaTOF data). Moreover, the deconvolution requires multiple parameters to be set by the user or manual peak integration. Further, none of the tools are integrated into a mass spectrometry/metabolomics public data repository that retains every setting and result of an analysis job, features that are vital for reproducibility of data processing.
A public informatics resource that can not only be integrated with a public repository, but also perform GC-MS deconvolution, alignment, and mass spectral library matching for large numbers (>100) of data files is needed. Technical reasons, such as the lack of a shared and uniform data format, often preclude GC-MS data comparison between different laboratories and prevents taking advantage of repository-scale information and community knowledge about the data. This, coupled to a lack of incentive to deposit data into public domain, leads to GC-MS datasets being infrequently shared and rarely reused across studies and/or biological systems  15, [17] [18] [19] [20] [21]. One of the developments that enabled finding additional structural relationships within mass spectrometry data is spectral alignment, which forms the basis for molecular networking  [22] [23] [24] [25] [26].
Here, we develop a repository-scale analysis infrastructure for GC-MS data enable to create networks within the Global Natural Products Social (GNPS) Networking platform. GNPS promotes Findable, Accessible, Interoperable, and Reusable (FAIR) use practices for mass spectrometry data  27. The community infrastructure can be accessed at https://gnps. ucsd. edu under the header "GC-MS EI Data Analysis". Results: Creating a web-based scalable strategy for spectral deconvolution. Current EI spectral deconvolution strategies can save settings and apply them to the next analysis, but require initial manual parameter setting (e. g. AMDIS  5  , MZmine/ADAP 8 , MS-DIAL 9 , PARAFAC2  12  ); some require extensive computational skills to run (e. g. XCMS  28  , eRah  14  ). Although batch modes exist, they do not enhance deconvolution quality by utilizing information from other files of the dataset.
To use this across-file information, improve scalability of spectral deconvolution, and eliminate manual parameter setting, we developed an algorithmic learning strategy for deconvolution of entire datasets (Figure  1a-f ). We deployed this functionality within GNPS/MassIVE 29 (Figure  1f-i ). To promote analysis reproducibility, all GNPS jobs performed are retained in the "My User" space and can be shared as hyperlinks in collaborations or publications. Classically, when performing spectral deconvolution of GC-MS data, the user defines parameters specific to their data to the best of their abilities. The user must therefore have a thorough understanding of the characteristics (i. e. , peak shape, peak width, resolution etc. ) of the particular GC-MS data set before spectral deconvolution.
Even when a compound is present in only a few samples in the dataset, as long as the spectral patterns are highly conserved across samples (e. g. not contaminated by spurious noise peaks), it would result in a high balance score. Balance score thus allows discarding low-quality spectra that are more likely to be noise, and provides an orthogonal metric to matching scores when searching spectral libraries. We refer to the dataset-based spectral deconvolution tool within the GNPS environment as "MSHub". MSHub converts raw GC-MS data of any kind (e. g. Table  S1 ) into spectral patterns, enabling molecular networking within GNPS. issues such as baseline rise with thermal gradient due to bleeds. c) Common profile established across entire dataset and peaks in RT dimension are aligned to it using FFT-accelerated correlation in full resolution via iterative approach.
d) Fast peak detection picks and integrates peaks to generate both peak integrals for all samples and their common fragmentation patterns. For datasets with more than 5 samples peak deconvolution step e) is employed to separate overlapping peaks with different patterns across samples using NMF approach. f) MSHub produces peak integrals for all samples and canonical fragmentation patterns. g) GNPS employs either public or user-provided reference libraries to annotate peaks. h) Molecular networks are built for further metabolite analysis. i) Data and results are shared between users via GNPS's cloud architecture. NMF -Non-negative matrix factorization, FFT -Fast Fourier Transform, RT -retention time, m/z -mass-to-charge ratio. All MSHub algorithms operate iteratively for enhanced scalability, using highperformance HDF5 technologies saving settings for each analysis step.
The Fourier transform step with multiplication dramatically improves MSHub's efficiency, resulting in deconvolution times that scale linearly rather than exponentially with the number of files (Figure  2a, S2 ). The GNPS GC-MS workflow can process thousands of files in hours (Figure  2a ), which is faster than data acquisition, making data processing no longer a bottleneck. We achieved this performance using out-of-core processing, a technique used to process data that are too large to fit in a computer's main memory (RAM): MSHub uploads files one at a time into the specific RAM module, data are then processed and deleted from memory, iteratively. Figure  2a  illustrates the linear dependency between the number of samples processed and the processing time. Because only one sample is stored in memory at any given time, the workflow memory load is constant.
Spectral deconvolution scales linearly because each step in the processing pipeline is linear with respect to time (Figure  S2a-f ), taking ~1 min per file (Figure  S1 ). The machine learning approaches gain improved performance with increasing amounts of data, which means that increasing dataset size would boost learning each spectral pattern. Indeed, larger volume of analyzed data leads to better scores of spectral matches for the known compounds in derivatized blood serum samples that were spiked with 37 fatty acid methyl esters (FAMEs) and 17 long-chain hydrocarbons (Figure  2b, c ). Cosine and balance score can be jointly used as filters for processing the final results (Figure  2d-f ). In the analysis of biological samples, similar trends are found as for the reference dataset: the spectral matching scores against the library increase with increasing number of processed files while their distributions become narrower, a reflection that more data leads to better quality of results (Figure  2g, h ).
When there are more files deconvoluted, MSHub is leveraged to reduce chimeric spectra and discover more real spectral features, which leads to higher quality spectra and a rise in the number of unique annotations with greater match scores (Figure  2i, j ). If the user only has a few files (fewer than 10), spectral deconvolution and alignment should be performed using alternative methods (e. g. MZmine  30  , OpenChrom  28, 31  , AMDIS  5  , MZmine/ADAP 8 , MS-DIA  9  , BinBase  15  , XCMS  32  /XCMS Online 28 , MetAlign 10 , SpecAlign  33  , SpectConnect 11 , PARAFAC2  12  , MeltDB 13 , eRah  14  ). Using those tools, molecular networking can be performed in the same fashion as for MSHub, as the library search GNPS workflow accepts input from other tools into the GNPS/MassIVE environment. We have further benchmarked the MSHub against XCMS  28  (MassIVE dataset MSV000084622) and the quantitative results were nearly identical (the calibration curve was within 99.
f) Heat map of the number of library matches for spiked compounds. g) Distributions of library matching scores for the top match in a study of oesophageal and gastric cancer detection using breath analysis (non-derivatized, datasets ICL1-ICL11 in Table  S1 ) and h) studies of human and mouse blood serum, adipose tissue and cerebrospinal fluid (silalated, datasets UCD1-UCD16 in Table  S1 ). i) Heat map of the number of unique annotations (top hit only) for the data across datasets ICL1-ICL11 in Table  S1  and j) datasets UCD1-UCD16 in Table  S1 ; no balance score filtering applied. Spurious features corresponding to the low cosine tail of the distribution on panels (g) and (h) are improved as higher volume of the data enhances the frequency domain for deconvolution quality. k, l) Quantitative integrals of abundances quantitation for the mixture of standards (MassIVE dataset MSV000084622) evaluated using XCMS (l) and MSHub (k).
GNPS enables searches against public spectral reference libraries and molecular networking at repository scale. Once the. mgf file is generated by GNPS-MSHub or imported from another deconvolution tool, the spectral features can be searched against public libraries  36  (currently GNPS has Fiehn 37 , HMDB  38  , MoNA 17 , VocBinBase  15  ) or the user's own private or commercial libraries (such as NIST 2017  39  and Wiley). Matches are narrowed down based on user-defined filtering criteria such as number of matched ions, Kovats retention index (RI, calculated if hydrocarbon reference values are provided), balance score, cosine score, and abundance. With this release, we also provide additional freely available reference data compiled by co-authors of this manuscript of 19,808 spectra for 19,708 standards.
We explored clustering patterns for the EI data (Figure  S4 ) and observed that the EI-based cosine similarity networks are predominantly driven by structural similarity (Figure  S4a )  35. These EI networks can be used to visualize chemical distributions and guide annotations (Figure  S5 ). Networking enables data co-and re-analysis, as it is agnostic to the data origin once the features are deconvoluted. To demonstrate this, we have built a global network of various public GC-MS datasets deposited on GNPS (38 datasets comprising ~8,500 GC-MS files, Figure  3c ). These data encompass various types of samples, modes of sample introduction etc. and thus the global network is a snapshot of all chemistries detectable by GC-MS (Figure  3c-e, S6 ). Prior to networking, we applied a balance score of 65%, which allowed us to remove a bulk of spurious low quality matches (Figure  3 a, b ).
To highlight the broader utility of GNPS GC-MS and GC-MS based molecular networking, 6 supplemental videos were created that carry the user through how to navigate and perform analysis with the tools (Supporting Videos 1-6). Nodes are connected if cosine > 0. 5. The size of the node is proportional to the number of nodes that connect to it  45  , the edge thickness is proportional to the cosine score (Figure  S3 ), the annotation is the top match with cosine above 0. 65. d) The inset shows the zoomed-in portion of the network. e) Close up of a cluster of compounds found in the dart frog skin samples with the top spectral library match shown -all nodes are nitrogen heterocyclic alkaloids such as gephyrotoxin  46  that are unique to these frogs. The output from GNPS deconvolution, annotations, and molecular network analysis can be exported for use in a statistical analysis environment such as Qiime 4748 , Qiita  49  , or MetaboAnalyst  50, 51  , or for data visualization in tools such as Cytoscape  52  or Gephi  53  (e.
Lipid peroxidation of polyunsaturated fatty acids in cell membranes generates alkanes that can then be excreted in the breath  71  , which makes their observation in relation to OGC biologically plausible. as described in the tutorial (https://ccmsucsd. github. io/GNPSDocumentation/gcanalysis/). Molecular distributions on skin of a volunteer shown for: a) squalene, a key component of natural skin grease. Low abundance of squalene is aligned with areas of dry skin 72 b) hexanoic acid, one of the malodour molecules responsible for the unpleasant sour body odor 73 c) globulol, naturally occurring in plant essential oils, likely introduced via use of skin cosmetics d) phenylenedibenzoate, also introduced via use of a skin product. e) Chemical distributions that relate to cancer status are visualized via molecular network that combines two studies.
Discussion: GNPS provides a platform for data sharing and accumulation of public knowledge. Community adoption of GNPS has sharply increased the volume of MS data in the public domain  7. It has also spurred new tools development (MASST  75  , FBMN  41  , ReDU  76  ) and enabled many biological discoveries. Due to the fundamental differences between CID and EI fragmentation, the GNPS infrastructure could not previously support the analysis of EI data. Adopting existing solutions for deconvolution was not possible as all of them required too much manual input from the user and could not operate at repository scale. Here we used an unsupervised non-negative matrix factorization and a Fast Fourier Transform-based approach to scale the deconvolution step. Such strategies are most effective when largescale datasets become available, as features can be extracted with increasing quality of fragmentation patterns, as defined by the balance score.
Currently, all 1D EI GC-MS data are amenable and we will extend the same approach to 2D GC-MS data. These features can then be subjected to EI-based molecular networking. The algorithm for molecular networking within GNPS had to be modified to accommodate EI data to function without molecular ion information and can reinforce candidate annotations to level 3 by assessing if the annotations are similar at the family level and if annotations share chemical class terms. Such analysis can now be achieved with the data at repository scale, enabling co-and re-analysis of GC-MS data. Here we show how the co-analysis could be beneficial for two cancer breathomics data sets, but in the same fashion other breathomics (or other volatilome) data can now be co-analyzed with these datasets as long as they are publicly available in an open file format.
Co-analyzing multiple disparate GC-MS studies would be challenging otherwise. Further, when considering GC-MS data as networks, in addition to conventional statistical approaches, strategies such as networks on graphs 77 could be deployed to investigate global biochemical patterns rather than differences in individual compounds. The networks, in principle, are not limited to any one kind of data and can be extended to any number or type of datasets as shown in Figure  3c. Surprisingly, although GC-MS is the oldest and most established of MS-based methods, and the sheer volume of existing EI reference data accumulated over decades (far exceeding that for any other kind of MS), researchers still use decades-old data analysis strategies. We anticipate that the new GNPS community infrastructure will incentivize moving raw EI data into the public domain for data reuse, comparable to the trajectory for tandem MS  7, 75, 76.
GC-MS analysis within GNPS/MassIVE will lower the expertise threshold required for analysis, encourage FAIR practices  27  through reusable deposition of the data in the public domain, and promote data analysis reproducibility and "recycling" of GC-MS data. Finally, this work is a piece of the puzzle to democratize scientific analysis from all over the world. GC-MS the most widely used MS method, in part due to its competitive operational cost. It is often the only mass spectrometry method available at smaller, e. g. undergraduate institutions, non-metabolomics laboratories, or local testing facilities. The proposed infrastructure will enable labs with fewer resources, including those from developing countries, to have free access to data and reference data in a uniform format, and to free, powerful computing infrastructures.
Data and code availability All of the data used in preparation of this manuscript are publicly available at the MassIVE repository at the UCSD Center for Computational Mass Spectrometry website (https://massive. ucsd. edu). The dataset accession numbers are: #1 (MSV000084033), #2 (MSV000084033), #3 (MSV000084034), #4 (MSV000084036), #5 (MSV000084032), #6 (MSV000084038), #7 (MSV000084042), #8 (MSV000084039), #9 (MSV000084040), #10 (MSV000084037), #11 (MSV000084211), #12 (MSV000083598), #13 (MSV000080892), #14 All of the GNPS analysis jobs for all of the studies are summarized in Table  S1. The source code of the MSHub software is available online at Github (version used in GNPS) (https://github. com/CCMS-UCSD/GNPS_Workflows/tree/master/mshub-gc/tools/mshubgc/proc) and at BitBucket (standalone version in MSHub developers' repository: https://bitbucket.
org/iAnalytica/mshub_process/src/master/ ). Scripts used to parse, filter, organize data and generate the plots in the manuscript are available online at Github (https://github. com/bittremieux/GNPS_GC_fig). Script for merging individual. mgf files into a single file for creating global network is available at Github: https://github. com/bittremieux/GNPS_GC/blob/master/src/merge_mgf. py ) The 3D model, feature table with coordinates used for the mapping and snapshots shown on the Figure  4a -d are available at: https://github. com/aaksenov1/Human-volatilome-3Dmapping-Methods: These are provided as supporting information. The tools are accessible through gnps. ucsd. edu and the documentation on how to use the GNPS GC-MS Deconvolution workflow and molecular networking workflows can be found here https://ccmsucsd.
from 2017 Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) Pilot Research Grant and NIH/NIAMS T32AR069515. RG is grateful to the Azrieli Foundation for the award of an Azrieli Fellowship. JJJvdH acknowledges support from an ASDI eScience grant, ASDI. 2017. 030, from the Netherlands eScience Center-NLeSC. BA was supported by the National Science Foundation (NSF) through the Graduate Research Fellowship Program. GC-MS analyses for collection of the dataset MSV000083743 were supported by the Pacific Northwest National Laboratory, Laboratory Directed Research and Development Program, and were contributed by the Microbiomes in Transition Initiative; data were collected in the Environmental Molecular Sciences Laboratory, a national scientific user facility sponsored by the Department of Energy (DOE) Office of Biological and Environmental Research and located at Pacific Northwest National Laboratory (PNNL).
PNNL is operated by Battelle Memorial Institute for the DOE under contract DEAC05-76RLO1830. Authors are grateful to Drs. Marina Vance and Delphine Farmer who have organized the sampling for HomeChem indoor chemistry project (https://indoorchem. org/projects/homechem/) that allowed to collect samples for the dataset MSV000083598. Brandon Ross has assisted with collecting data for the dataset MSV000084348. GC-MS analyses for collection of the datasets MSV000084211 and MSV000084212 were supported by the announcement N757 Doctorados Nacionales and project EXT-2016-69-1713 from Departamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS), the seed project INV-2019-67-1747 and FAPA project of Chiara Carazzone from the Faculty of Science at Universidad de los Andes, and the grant No.
High-dimensional multi-omics data are now standard in biology. They can greatly enhance our understanding of biological systems when effectively integrated. To achieve this multi-omics data integration, Joint Dimensionality Reduction (jDR) methods are among the most efficient approaches. However, several jDR methods are available, urging the need for a comprehensive benchmark with practical guidelines. We performed a systematic evaluation of nine representative jDR methods using three complementary benchmarks. First, we evaluated their performances in retrieving ground-truth sample clustering from simulated multi-omics datasets. Second, we used TCGA cancer data to assess their strengths in predicting survival, clinical annotations and known pathways/biological processes. Finally, we assessed their classification of multi-omics singlecell data.