dataesr/scientific-paragraphs-categorization huggingface dataset:
Reassuringly, prevalence of resistance to the NRDs was substantially lower than for first-and secondline agents in the dataset (Fig 3A ), and resistance to the new drugs bedaquiline and delamanid was less common than the repurposed drugs clofazimine and linezolid in an MDR/RR background (Fig  4C ). However, the presence of higher levels of delamanid and clofazimine resistance than ethambutol resistance in the isoniazid-and rifampicin-susceptible background does suggest some preexisting propensity towards NRD resistance  (Fig 4B). Coresistance between NRDs was seen in isolates in the compendium, the most common being isolates resistant to both bedaquiline and clofazimine. This link is well documented and has been attributed to shared resistance mechanisms such as nonsynonymous mutations in rv0678, which were found in both clofazimine-and bedaquiline-resistant isolates in the compendium  [31]  (Fig 5B  and 5C ).
Several research avenues are being actively explored by the CRyPTIC consortium that make further use of this compendium, including the following: (i) relating genetic mutations to quantitative changes in the MICs of different drugs  [13] ; (ii) genome-wide association studies  [15] ; (iii) training machine learning models that can predict resistance  [14] ; and (iv) exploration of the genetic determinants of resistance to second line and NRDs  [16]. Collectively, these studies share the same aim of facilitating the implementation of WGS-directed resistance prediction in the clinic. Finally, we urge other researchers to explore and analyse this large dataset of M. tuberculosis clinical isolates and hope it will lead to a wave of new and inciteful studies that will positively serve the TB community for years to come.
The CRyPTIC study involves analysis of microbiological isolates only-there is no associated data on patients. The study aggregates isolates from previous studies (which had previously obtained IRB approval) and also collected its own samples. Each IRB listed above assessed the protocol and saw no need for individual consent as only microbiological isolates were being analysed, and no personally identifiable information or host genetic data was used. In the remaining jurisdictions (IML Gauting (Germany), Public Health Scotland, Public Health Sweden, San Raffaele Scientific Institute, Italy), no IRB approval (and no individual patient consent) was required for studies analysing routinely collected microbiological isolates only.
Minimum inhibitory concentration (MIC) measurements MICs for each drug were read after incubation for 14 days by a laboratory scientist using a Thermo Fisher Sensititre Vizion digital MIC viewing system  [11]. The Vizion apparatus was also used to take a high contrast photograph of the plate with a white background, from which the MIC was measured again using the Automated Mycobacterial Growth Detection Algorithm (AMyGDA) software  [45]. The AMyGDA algorithm was specifically developed to automate and perform quality control of MIC measurements and to facilitate machine learning studies within the consortium. AMyGDA detects the boundaries of each well using a Hough transform for circles and measures growth as the number of dark pixels within the area contained by this boundary. All images where the MICs measured by Vizion and AMyGDA were different were uploaded to a citizen science project, BashTheBug, on the Zooniverse platform  [46].
Genomic data processing and variant calling Clinical samples were subcultured either using Lowenstein-Jensen tubes, 7H10 agar plates, or MGIT tubes for (no more than) 14 days prior to DNA extraction using either the FastPrep-24 instrument (MP Biomedicals) for cell lysis and ethanol precipitation or the cetyltrimethylammonium bromide (CTAB) method. Paired-end libraries were prepared using a Nextera XT DNA sample preparation kit (Illumina, San Diego, CA, USA) and were sequenced on Illumina instruments. The resulting FASTQ files were processed using the bespoke pipeline Clockwork (v0. 8. 3, github. com/iqbal-lab-org/clockwork; [47] ). Briefly, all raw sequencing files were indexed into a relational database with which Clockwork proceeds. Human, nasopharyngeal flora, and human immunodeficiency virus-related reads were removed, and remaining reads were trimmed (adapters and low-quality ends) using Trimmomatic and mapped with BWA-MEM to the M.
Resistance prediction using a genetic catalogue A hybrid catalogue of genetic variants associated with resistance to first-and second-line drugs based on existing catalogues was created and can be found at github. com/oxfordmmm/ tuberculosis_amr_catalogues/blob/public/catalogues/NC_000962. 3/NC_000962. 3_CRyPTIC_ v1. 311_GARC1_RUS. csv [8, 26]. We specifically did not use the recent WHO catalogue to avoid circularity and overtraining, as that catalogue was developed (via prior literature, expert rules, and a heuristic algorithm) based partially on these isolates  [25] ). The resulting VCF file for each isolate (see "Genomic data processing and variant calling" section above) was compared to the genetic catalogue to determine the presence or absence of resistance-associated mutations for 8 drugs: RIF, INH, EMB, LEV, MXF, AMI, KAN, and ETH.
"reuse" directory We point the reader to this directory to gain access to CRyPTIC project data. "CRyPTIC_reu-se_table_20221019. csv" contains genotypic and phenotypic data relating to the figures and summaries listed in this manuscript and is what we present as a general use reference table for most future projects. It includes binary phenotypes (R/S), MICs, phenotype quality metrics, and ENA sample IDs for 12,288 compendium isolates (see "Quality assurance of the minimum inhibitory concentrations for 13 drugs" section below in Results for filters applied to obtain this final set of isolated). It also includes file paths to each isolate's VCF file and "regenotyped" VCF file (VCF files that have an entry at all variable positions; see "Genomic data processing and variant calling" section above for more).
"reproducibility" directory This directory contains the data used for multiple CRyPTIC project publications referenced throughout this manuscript. As stated above, each project has taken slightly different subsets of these data as documented in those papers. For example, see how tables such as "MUTA-TIONS. csv" and "GENOTYPES. csv" were used and filtered, (along with others) in this study to obtain the reuse file "CRyPTIC_reuse_table_20221019. csv" in Fig 1. Again, for optimal use of CRyPTIC data in your own project, please refer to "CRyPTIC_reuse_table_20221019. csv" in the "reuse" directory. All data for this study were analysed and visualised using either R or python3 libraries and packages. See github. com/kerrimalone/Brankin_Malone_2022 for codebase. and Juan Carlos Toro and Solomon Ghebremichael (Public Health Agency of Sweden, Solna, Sweden), Iñaki Comas and A ´lvaro Chiner-Oms (Instituto de Biologı ´a Integrativa de Sistemas, Valencia, Spain; CIBER en Epidemiologı ´a y Salud Publica, Valencia, Spain; Instituto de Biomedicina de Valencia, IBV-CSIC, Valencia, Spain).
The raw sequence, VCFs, MICs, and binary resistance calls for the data compendium are presented in "CRyPTIC_reuse_table_20211019. csv" via an FTP site (see Methods), and the raw sequence and VCF files for those samples present in the full dataset are presented in "CRyPTIC_excluded_samples_20220607. tsv" via the same FTP site (see Methods). The data tables GENOTYPES. csv, VARIANTS. csv, MUTATIONS. csv, SAMPLES. csv, and PHENOTYPES. csv used for the analyses presented in this manuscript are also accessible via the FTP site (see Methods). DSTAU : AbbreviationlistshavebeencompiledforthoseusedinFigs1an , drug susceptibility testing; MIC, minimum inhibitory concentration; WGS, whole-genome sequencing.
domain (see http://www. naturalearthdata. com/about/terms-of-use/), and created using the python library "geopandas" (see github. com/kerrimalone/Brankin_ Malone_2022/). AMI, amikacin; BDQ, bedaquiline; CFZ, clofazimine; CRyPTIC, Comprehensive Resistance Prediction for Tuberculosis: an International Consortium; DLM, delamanid; EMB, ethambutol; ETH, ethionamide; INH, isoniazid; KAN, kanamycin; LEV, levofloxacin; LZD, linezolid; MDR, multidrug resistant; MXF, moxifloxacin; pre-XDR, pre-extensively drug resistant; RFB, rifabutin; RIF, rifampicin; RR, rifampicin resistant; XDR, extensively drug resistant. https://doi. org/10. 1371/journal. pbio. 3001721. g003.
Statistics on how much resistance can be explained in a dataset enriched for rare resistance mutations using a standard resistance catalogue that predates the CRyPTIC project. TP, the number of phenotypically resistant samples that are correctly identified as resistant ("true positives"); FP, the number of phenotypically susceptible samples that are falsely identified as resistant ("false positives"); TN, the number of phenotypically susceptible samples that are correctly identified as susceptible ("true negatives"); FN, the number of phenotypically resistant samples that are incorrectly identified as susceptible ("false negative"); VME, very major error rate (falsenegative rate), 0-1; ME, major error rate (false-positive rate), 0-1; PPV, positive predictive value, 0-1; NPV, negative predictive value, 0-1.
Z. Y. is supported by the  National Science and Technology Major Project, China  Grant No. 2018ZX10103001. K. M. M. is supported by  EMBL 's  EIPOD3  programme funded by the  European Union 's  Horizon 2020 research and innovation programme under Marie Sklodowska Curie Actions. T. C. R. is funded in part by funding from  Unitaid  Grant No. 2019-32-FIND MDR. R. S. O. is supported by  FAPESP  Grant No. 17/16082-7. L. F. received financial support from  FAPESP  Grant No. 2012/51756-5. B. Z. is supported by the  National Natural Science Foundation of China  ( 81991534 ) and the  Beijing Municipal Science & Technology Commission  ( Z201100005520041 ). N. T. T. T. is supported by the  Wellcome Trust   International Intermediate Fellowship  ( 206724/Z/17/Z ). G. T. is funded by the  Wellcome Trust.
R. W. is supported by the  South African Medical Research Council. J. C. is supported by the  Rhodes Trust  and  Stanford   Medical Scientist Training Program  ( T32 GM007365 ). A. L. is supported by the  National Institute for Health Research (NIHR) Health Protection Research Unit in Respiratory Infections at Imperial College London. S. G. L. is supported by the  Fonds de Recherche en Santedu Quebec. C. N. is funded by  Wellcome Trust  Grant No. 203583/Z / Acknowledgments We thank  Faisal Masood Khanzada  and  Alamdar Hussain Rizvi  ( NTRL, Islamabad, Pakistan ),  Angela Starks  and  James Posey (Centers for Disease Control and Prevention, Atlanta, USA) ,.
This work was supported by  Wellcome Trust/Newton Fund-MRC  Collaborative Award  16/Z. A. V. R. is supported by  Research Foundation Flanders (FWO)  under Grant No. G0F8316N  ( FWO Odysseus ). G. M. was supported by the  Wellcome Trust  ( 098316 ,  214321/Z/18/Z , and  203135/Z/16/ Z ), and the  South African Research Chairs Initiative of the Department of Science and Technology and National Research Foundation (NRF) of South Africa  (Grant No. 64787 ). The funders had no role in the study design, data collection, data analysis, data interpretation, or writing of this report. The opinions, findings and conclusions expressed in this manuscript reflect those of the authors alone. L. G. was supported by the  Wellcome Trust  ( 201470/Z/16/Z ), the  National Institute of Allergy.
(2008)  propose a particle swarm optimization and demonstrate its efficiency by comparing it to grid search in different public datasets;  García-Pedrajas et al. (2014)  propose a memetic algorithm for dealing with many instances and many features simultaneously by performing joint instance and feature selection; Gauthama  Raman et al. (2017)  present an adaptive and a robust intrusion detection technique for parameter setting and feature selection in SVM;  Aladeemy et al. (2017)  propose a variation of Cohort Intelligence algorithm for SVM with feature selection;  Bouraoui et al. (2018)  propose a multi-objective approach to simultaneously optimize SVM parameters and feature subset using different kernel functions;  Faris et al. (2018)  present a multiverse optimizer approach for feature selection and optimizing SVM parameters based on a robust system architecture;  Tao et al.
(2019)  present a good classifier for data regarding hospitalization expenses which is obtained by using feature selection in SVM;  Ibrahim et al. (2019)  develop a novel metaheuristic, the Grasshopper Optimization Algorithm, which is inspired by grasshoppers searching for food and approved its ability to solve some biomedical datasets;  Cheng et al. (2020)  explore the benefit of subdividing the training set into smaller regions when training sets are large-scale;  Dudzik et al. (2021)  propose an evolutionary technique that efficiently classifies difficult datasets, including very large and extremely imbalanced cases;  Al-Zoubi et al. (2021)  present an improved evolutionary variant of competitive swarm optimizer and its superiority over a genetic algorithm is shown; All these works learn from a training set and check the performance with a testing set.
AUGMECON2 is one of these methods, which is precisely that used in this paper. In this paper we present a metaheuristic approach to approximate the Pareto-optimal frontier simultaneously considering the two SVM objectives and making the selection of features. As far as we know this type of metaheuristic has not been proposed before. The main advantage of the metaheuristic we propose compared with the ones in the literature, is that our approach gives a good description of the Pareto front of the SVM with feature selection whereas the others focus on optimizing the subset of features and parameters, and then efficiently classify some benchmark datasets. This paper is organized as follows, Section 2 presents the SVM mathematical model and its connections with the related hyperplane distances.
The encoding we propose for the selected features consists of a non ordered list of a subset of the complete set of features, and this type of encoding is similar to the one proposed in  Alcaraz et al. (2020)  to encode a list of open facilities. Therefore, the proposed encoding has a fixed length of 2 + 2 positions: 1 mode, +1 vectors and  features. Fig. 3  shows the encoding proposed for a given database with a selection of 7 features. Therefore, the total length of the chromosome is 16, the first position indicates the mode, the following 8 indicate the vectors to build the hyperplanes and the last 7 the features selected among the  available. If the mode equals A, the positions 1th to 7th in the vector array correspond to vectors in class ''-1'' and position 8th to the only vector in class ''1''.
Therefore, the selection is based on the dominance ideas. First, between two solutions in different fronts, we prefer the solution which belongs to a lower front. Otherwise, if both solutions belong to the same front, then we choose the solution which dominates a higher number of solutions in the population. In case of ties, the winner is randomly chosen among the candidates. Example 4. Let us suppose that the population of the metaheuristic is formed, during the genetic process, with the 8 solutions presented Table  4  when the dataset presented in Fig. 1  is being processed. The second, third and fourth columns present the information that the encoding includes, the mode, the vectors supported by the hyperplanes and the features selected. Some of these solutions have been used previously in different figures.
Computational experience In order to compare the performance of both methods, the exact approach (AUGMECON2) when solving the SVM linear programming model and the metaheuristic algorithm (NSGA-II-SVM-f) we have carried out a computational experience, which has been developed on a PC with 2. 33 GHz Intel Xeon dual core processor, 8. 5 GB of RAM, and operating system LINUX Debian 4. 0. The exact method has been solved by CPLEX v11. 0 and the evolutionary algorithm in C++. We have selected different datasets with a big number of features previously employed in different works (see, for example,  Labbé et al. , 2019). The main characteristics of the instances are given in Table  5. The two last datasets, Arrythmia and Mfeat can be found in the UCI repository  (Asuncion & Newman, 2007)  and a description of the remaining sets can be found in several works (e.
g. ,  Alon et al. , 1999; Carrizosa et al. , 2010; Golub et al. , 1999; Maldonado et al. , 2014; Notterman et al. , 2001; Shipp et al. , 2002). Later, these datasets are considered as instances with a big number of features and, as regard the sample size, the first four are classified as small and the two last as big  (Labbé et al. , 2019). The aim of this computational experience is to run both methods solving each instance in order to get the Pareto-optimal front or, at least, the best approximation. Comparing the frontiers given by the methods would lead us to a consistent comparison of their efficiency when solving the Support Vector Machine Problem with Feature Selection. To compare frontiers given by different multi-objective optimization methods is a complex task, given that we can establish different quality measures.
• Convergence: Quantify how close an approximation frontier is from the Pareto-optimal front in the objective space. • Distribution: Quantify how well every region of the objective space is represented, the distance between the points in the frontier (spread) and the distance between the extreme points of the front (extent). There are also some indicators which combine convergence and distribution characteristics. All the datasets have been solved by both methods setting the number of selected features  = 5. The experience has not been repeated for other values of  because the objective is not to see how the objective value varies when  varies but to analyze the properties of the boundary in terms of certain metrics. The time limit imposed to the exact method to solve each instance has been fixed in 7 h.
The metaheuristic gives, as a result, all the non-dominated solutions which are present in the final population, which form the frontier required. Therefore, this frontier will be built with a maximum of  individuals. Given that the metaheuristic is a randomized technique and running it different times may lead to different results, we have run the metaheuristic three separate times per instance and we have calculated the average in all the metrics computed. In order to establish a fixed configuration for the metaheuristic to run all the datasets we have carried out several preliminary experiments. Based on that we have decided to use the following configuration in all the instances:  = 500;  _ = 0. 8;    = 0. 7;    _  =    _   = 0. 4. However, these preliminary studies indicated that slightly varying the above parameters, except the population size, does not have a great influence in the final results.
In Table  6  we show the metrics calculated for both methods when solving the datasets presented in Table  5. The time limit for AUGME-CON2 is 7 h and for the metaheuristic of 1 h. For each method,   indicates the number of non-dominated points in the approximation frontier obtained as outcome, , the percentage of solutions in the frontier given by the corresponding method which are dominated by points of the frontier generated by the other method,  3 measures the extent of the frontier and  the size of holes in the front. If we look first to the cardinality indicators we can deduce that the metaheuristic obtains in 1 h of computation time better frontiers than the exact approximation in 7 h. OVNG indicates that AUGMECON2 gives frontiers with 4 non-dominated points, on average, compared to the 366 non-dominated solutions, on average, of the metaheuristic frontiers.
The number of points varies between 3 and 5 with AUGMECON2 and between around 34 and 500 in the case of NSGA-II. However, it is interesting to study how many points in a frontier are dominated by points on the other frontier. The C-metric indicates that, in most of the instances solved there are points in the approximation frontier given by one method that are dominated by points in the other frontier. In the case of AUGMECON2, the 44. 72%, on average, of the frontiers given by the method are dominated by points in the NSGA-II frontier. On the other hand, only a 4. 73% of the NSGA-II frontier, on average, are dominated by points of the approximation given by the exact method. In the Carcinoma dataset, for instance, none of the points in the frontier found by the metaheuristic is dominated by points of the AUGMECON frontier.
Therefore, the two cardinality metrics are much better, by far, for the metaheuristic frontiers. Let us analyze now the distribution indicators, that in this case, measure the extent and the distance between consecutive points in the frontiers. As  3 calculates the extent of the frontier, a higher value indicates a better performance. AUGMECON2 gives frontiers with a better extent in only two (Leukemia and Carcinoma) of the six datasets, showing a better performance of the metaheuristic in the other four. The last indicator,  , which evaluates the maximum distance between two adjacent points in a frontier shows that the frontiers given by the metaheuristic approach are better in all the datasets solved. High values of this indicator for a method show that the generated frontiers present big holes and this is not a desirable fact.
In all the datasets of our study, the  values are much higher for AUGMECON2 that for the NSGA-II approach, and in most cases is more than 30 times higher, indicating that in the metaheuristic approximations the points are much closer and therefore the frontier shows a better spread. As the four indicators show different aspects of the frontiers and one frontier could be better than the other in one indicator but worse in another and in order to determine the best approximation for each dataset, we have determined the number of indicators, for each dataset, in which one approach is better than the other. The conclusion is that the frontiers given by the metaheuristic are better than those given by the exact approach in all the datasets: NSGA-II gives better results in all the indicators in 4 datasets (Colon, DBLCL, Arrythmia and Mfeat) and in 3 of the 4 indicators for the rest of the datasets (Leukemia and Carcinoma).
As an example, we graphically present the comparison of frontiers for the Colon dataset in Fig. 5. The graph shows the results for one of the three runs of the metaheuristic. It is interesting to point out that the computation time employed by AUGMECON2 to generate the frontier is of seven hours and the employed by the metaheuristic of only one hour. We can clearly observe that the frontier obtained by AUGMECON2 is formed with only three non-dominated points (OVNG) compared to the 500 points given by NSGA-II. Moreover, two of these three points ( = 66. 7%) are dominated by points in the metaheuristic frontier. By contrast, only a few points in the NSGA-II frontier are dominated by those in the AUGMECON2 frontier. Concerning the extent, the range of values covered by the metaheuristic frontier is larger than the one corresponding to the AUGMECON2 frontier and this one presents larger holes, i.
e. the maximal distance between adjacent points is much higher. The previous computational experiments show that the metaheuristic is a good alternative to the exact method in those datasets in which the last cannot obtain the Pareto-optimal front due to the computational effort required. However, we also want to show that the evolutionary algorithm performs well in different datasets in which the exact approach can obtain the optimal front. We have also solved 4 different datasets, which have been also obtained from the UCI repository  (Asuncion & Newman, 2007). The characteristics of these datasets are presented in Table  7. All the datasets have been solved by both methods with a selection of 5 features. AUGMECON2 has been running for one hour, and the non-dominated points which are obtained represent our Pareto-Optimal front.