dataesr/scientific-paragraphs-categorization huggingface dataset:
train. Benchmark procedures may be used to tune the classifier by setting different values and assessing the error rate. The function ddalpha. test is more appropriate for simulated data, while the two others are more suitable for subsampling learning with real data and testing sequences from it. Analogs of these procedures for a functional setting are present in the package as well: ddalphaf. test(learn, learnlabels, test, testlabels, disc. type,. ) ddalphaf. getErrorRateCV(dataf, labels, numchunks, disc. type,. ) ddalphaf. getErrorRatePart(dataf, labels, size, times, disc. type,. ) The discretization scheme is chosen with parameter disc. type setting it to "LS" or "comp". Note that these procedures are made to assess the error rates and the learning time for a single set of parameters. If the LS-transform is used, the parameters L and S shall be explicitly set with adc.
Multivariate and functional data sets and data generators are included in the package ddalpha to make the empirical comparison of different classifiers and data depths easier. 50 real multivariate binary classification problems were gathered and described by  Mozharovskyi et al. (2015)  and are also available at http://www. wisostat. uni-koeln. de/de/ forschung/software-und-daten/data-for-classification/. The data can be loaded to a separate variable with function variable = getdata("<name>"). Class labels are in the last column of each data set. Functional data sets are accessible through functions dataf. <name>() and contain four functional data sets and two generators from  Cuevas et al. (2007). A functional data object contains a list of functional observations, each characterized by two vectors of coordinates, the arguments vector args and the values vector vals, and a list of class labels.
All these depths are global and not able to reflect localities possibly present in the data. Local spatial depth as well as potentials compensate for this by fitting multimodal distributions well, which is bought at the price of computational burden for tuning a parameter due to an application specific criteria. Halfspace, simplicial, and projection depths are robust, while outlier sensitivity of Mahalanobis and spatial depths depends on the underlying estimate of the covariance matrix. To obtain their robust versions, the MCD estimator is applied in package ddalpha. Parameter mah. parMcd used with Mahalanobis and spatial depths corresponds to the portion of the data for which the covariance determinant is minimized. Simplicial volume and zonoid depths, being based on volume and mean, fail to be robust in general as well.
The degree of the DDα and the polynomial classifier and the number of nearest neighbors are automatically cross-validated, but maximal values may be set manually. To gain more insights, depth-transformed data may be plotted (using draw. ddplot). The outsider treatment should not be regarded as the one that gives the best separation of the classes in the original space, but rather be seen as a computationally cheap solution for points right beyond their convex hulls. In functional classification, parameters L and S can be set by the experience-guided applicant or determined automatically by means of cross-validation. The ranges for cross-validation can be based on previous knowledge of the area or conservatively calculated. Benchmark procedures that we included in the package may be used for empirical parameters' tuning, by iterating the parameters values and estimating the error rates.
Acknowledgments The authors want to thank  Karl Mosler  for his valuable suggestions that have substantially improved the present work. The authors would like to express their gratitude to the  Cologne Graduate School of Management, Economics and Social Sciences  who supported the work of  Oleksii Pokotylo  and to the  Lebesgue Centre of Mathematics  who supported the work of  Pavlo Mozharovskyi  (program  PIA-ANR-11-LABX-0020-01 ).
These estimate the capability of a method to compare quality of structures with different sequences, and thus to predict if a model is far or close to the native structure. We compared our method only with single-model methods (we excluded consensus-based methods), which are listed below. SBROD (  7 ) is a coarse-grain knowledge-based method trained using four types of structural features, residueresidue pairwise features, backbone atom-atom pairwise features, hydrogen bonding features and solventsolvate features. We used the version from https: //gitlab. inria. fr/grudinin/sbrod trained on CASP 5-10 server predictions. VoroMQA (  5 ) is a statistical potential trained on inter-atomic contact areas. We used the version included in the package voronota version 1. 18. 1877 from https://bitbucket.
org/kliment/ voronota/downloads/. RWplus (  6 ) is a classical gold-standard statistical potential that uses a pairwisedependent atomic potential, with a side chain orientationdependent energy term. We used the binary provided at https://zhanglab. ccmb. med. umich. edu/RW/. 3DCNN (  17 ) is another method that uses convolutional networks trained on protein density maps. We were not able to run this method on our side so we used the scores provided in http://proteinfoldingproject. com/static/datasets/models. tar. gz. Only the scores of CASP 11 are available in this archive. Proq3D  (10)  is a method that combines many heterogeneous expertdefined features using a deep-learning algorithm. It contains different back-end models to fit different geometrical scores. To fairly compare Proq3D with our method, we used the version of Proq3D trained on CAD-score with rotameric optimization enabled.
We should also mention that Proq3D does not only rely on protein structure, as it uses a sequence database to extract the relevant evolutionary information  (18). We used the method available at https://bitbucket. org/ElofssonLab/proq3 with a version of UniRef sequence database  (27)  from 2013 that was already available when CASP 11 challenge started. First, we evaluated the QA methods by comparing them with CAD-score as a ground-truth score. Table  1  lists the results. We can see that our method is almost always ranked second after Proq3D. We should note that historically, the protein structure prediction community was biased towards using GDT-TS (28) as a quality measure. Therefore, many of the machine learning-based methods (including those from our performance benchmark) have been specifically trained to approximate GDT-TS.
Therefore, for a fair comparison, we ran an additional test. Here, we computed the loss, Pearson correlation and Kendall's τ using GDT-TS as the groundtruth score. Table  2  lists the performance results. We can see that Proq3D is again the best performing method (even though it was trained on CAD-score). Here, our method performs, as expected, less impressive compared to the previous test (comparison with CAD-score). In particular, the average correlations are not as high as for the VoroMQA or SBROD methods, which were specifically trained to match GDT-TS. However, it is the second best method for picking the best structure in 3 out of 4 datasets, and it is also among the best for the global correlations, meaning that our method is still useful to assess the absolute quality of models. It is also very interesting to see the performance comparison between Ornate and 3dCNN.
B. Local scores. A particularity of Ornate is to also compute local scores for each model residue. This helps to predict which part of the model structure is poorly folded or should be refined. Figure  6  shows a few examples of CASP server predictions scored with Ornate (for targets T0854, T0367, and T0768) with some correctly modeled parts and poorly modeled ones. As a reference, we also show the same models colored according to the ground-truth CAD-scores, and the reference crystallographic structures. This figure demonstrates a very good correlation between what Ornate predicts as poorly folded residues and what are actually the poorly folded residues. C. Computational details. We implemented the Ornate method using a combination of C++ and Python programming languages. The part for the generation of input volumetric maps was written in C++.
The NN's training part uses Python with the TensorFlow framework  (29). The computational time thus can also be decomposed into two parts that take approximately the same time. Creating a 3D map from a residue structure and its neighborhood takes about 30 ms (measured with an I7 CPU), and running the network for one map takes about 20 ms (measured on a GeForce GTX 680 GPU). Please also note that the latter time was measured for TensorFlow with GPU support, and it may be up to 100 times slower without it. Overall, the complexity of scoring one protein model grows linearly with the number of residues in the model structure. For example, scoring a mid-size protein structure with about 200 residues takes about 1s.
Conclusion This work presents Ornate, one of the first 3D CNN methods for the protein model QA problem. Ornate demonstrates a significant improvement over the previous 3D CNN attempts. This improvement was made possible thanks to several dedicated network topology designs that we introduced. These include residue-wise scoring, orientation of the input maps according to the backbone atoms, the retyper layer and data routing. Ornate is competitive to most state-of-the-art single-model protein model QA methods. For example, when compared to the best of these, also trained to match CAD-score, on stage 2 of CASP 11 (which seems to be the hardest dataset to score according to our measures), the average correlations and prediction losses of Ornate are at the same level as the ones from Proq3D. However, Proq3D, compared to Ornate, accesses additional sequence data.
Acknowledgements First, the authors thank  Georgy Derevyanko  from  Concordia University, Montréal , who designed a preliminary version of a 3D CNN, which motivated us in constructing Ornate. The authors thank  Kliment Olechnovič  from  Vilnius University  for his support on Voronota usage, and  Arne Elofsson  and  Karolis Uziela  from  Stockholm University  for their support on Proq3D usage. Finally, the authors thank  Stephane Redon  from  Inria, Grenoble , for his permanent interest and motivated discussions in deep learning techniques, and also for.
Hereto, Pythium irregulare DAOM BR486 is the only P. irregulare strain sequenced and annotated at the National Center for Biotechnology Information -NCBI database (Bioproject number: PRJNA169053). Its annotation was performed automatically using MAKER v. 203 tool  [16]  and was based on Pythium ultimum Genome database  [17]   [18]. However, it aimed at evaluating the pathogenicity of oomycetes, disregarding the annotation of metabolic functions. Moreover the automatic annotation can produce false positive and erroneous data  [19]. The goal of this work was to identify the key genes and pathways related to EPA biosynthesis, as well as other metabolites of biotechnological importance, in P. irregulare strain CBS 494. 86, including amino acids and fatty acids production, and consumption of carbon and nitrogen sources, present in the wastewater.
Whole-genome sequencing The species classification of the isolate selected for genome sequencing was confirmed by Sanger sequencing and analysis of the cytochrome oxidase I gene (COI) and internal transcribed spacer regions (ITS1 and ITS2), which were aligned, using the nucleotide Basic Local Alignment Search Tool (BLAST)  [20] , with the NCBI genomic database. The ITS sequence was 98% identical to P. irregulare CBS 250. 28 (sequence ID: AY598702. 2) with coverage of 86%, wheras the COI sequence was 99% identical to P. irregulare CBS 493. 86 / CBS 250. 28 (sequence ID: GU071821. 1) with coverage of 99%. The genomic DNA from the cultivated P. irregulare strain CBS 494. 86 was extracted and sequenced on a HiSeq2500 using a single paired-end library (2x100bp). The HiSeq2500 produced 58,990,406 sequenced fragments 2x100bp which were used for assembly; 43,436, 209 of these remained after quality control.
Genome assembly resulted in 9658 scaffolds larger than 500 bp, with an N50 of 13. 460 bp (6. 653 scaffolds longer than 1 Kbp) and a total genome size of 47. 121. 789 bp (Table  1 ). The coverage evaluation of the gene space by our assembly was performed using BUSCÒ v3  [21]  with two sets of conserved genes, one for all eukaryotes with 303 conserved genes and one for protists with 215 conserved genes. For both datasets our assembly showed over 90% of coverage of complete BUSCÒs (Additional file 2). The gene space coverage observed in this project assembly is similar to that of the published genome sequence of P. irregulare strain DAOM BR486, and the size of their haploid genomes is also similar  [21]  (Additional file 2). Gene prediction was carried out with Augustus  [22] , which was trained by exploiting available data from the Buell lab of another strains of P.
irregulare and P. ultimum  [23]   [17] , and resulted in the prediction of 17,008 protein-coding genes and 29 tRNA genes. The different copies of the ribosomal operon were collapsed into a single copy. In strain CBS 493. 86, 95. 4% of the predicted genes can be mapped to the genome of strain CBS 805. 95. The sequenced genome was deposited in NCBI (Bioproject number: PRJNA371716).
Metabolic annotation The merlin software (metabolic models reconstruction using genome-scale information)  [24]  was used for the functional annotation of proteins with metabolic functions encoded in the genome of P. irregulare. For the analysis and interpretation of the results from the semi-automatic annotation performed by merlin, each candidate metabolic gene was inspected and accepted, or rejected, according to a developed annotation pipeline, reported in the Methods section. The manual curation of merlin results began by inspecting the information in different databases for each candidate and identified homologues, prioritizing UniProt's reviewed information  [25]. As the functional annotation of P. irregulare is yet to be described and most annotations in the Stramenopiles lineage are not reviewed in UniProt, the annotation pipeline took into account phylogeny  [26] , to retrieve the closest organisms with reviewed information at Swiss-Prot.
2%). Regarding the annotation of transporters, merlin's TRI-AGE  [30]  independent module identified candidate transporter proteins encoding genes and, for the genes that fulfilled certain conditions, automatically created transport reactions. From this annotation, 945 candidate genes were identified to encode membrane transporter proteins associated with the transport of 860 metabolites. These data are available in the Availability of data and materials (Additional file 4). TRIAGE classified 39. 9% genes as Electrochemical Potential-driven Transporters (transporter classification (TC) TC2), 24. 4% as Channel/Pores (TC1), 20. 2% as Primary Active Transporters (TC3), 9% as Incomplete Characterized Transport Systems (TC9), 4% as Accessory Factors Involved in Transport (TC8), 1% as Group Translocators (TC4), and 1% Transmembrane Electron Carriers (TC15) (Table  2 ).
Microorganism cultivation P. irregulare strain CBS 494. 86 was acquired from the CBS-KNAW Fungal Biodiversity Centre. It was inoculated on PDA plates (Potato Dextrose Agar) and incubated for 3 days at 29 °C, then 1 cm 2 of the grown microorganism was transferred and cultivated on YPDO medium (g/L: yeast extract 1. 25, peptone 25, glucose 3, oatmeal 2) for 5 days at 29 °C in a shaker at 200 rpm. Even though several articles have been published about P. irregulare DAOM BR 486 / CBS 250. 28  [14, 18, 23, 71] , mainly associated with its pathogenicity, with its genome sequences deposited at NCBI (Bioproject number: PRJNA169053), strain Pythium irregulare CBS 493. 84 had not been explored yet.
Sequencing was carried out on a Illumina HiSeq 2500 instrument, using paired-end chemistry (58. 990. 406 sequenced fragments 2*100 bp) at the NGS core facility of the Brazilian Bioethanol Science and Technology Laboratory (CTBE). K-mer statistics revealed an expected genome size of approx. 75 Mbp  [74]. Genome assembly was carried out SPAdes using an ensemble of different k-mer values  [75]  and improved with Pilon  [76]. Ploidy level analysis was carried out with ploidyNGS  [77] , which revealed that this organism is diploid, thus the inferred genome sequence was processed with Redundans to eliminate redundancies due to allelic polymorphisms. The genome of P. irregulare strain (CBS 494. 86) is described at the GenBank sequence database provided by the National Center for Biotechnology Information (NCBI) in PRJNA371716.
Functional annotation The phenotypic potential of an organism is embedded in its genome sequence, and gene product identification is compulsory in order to understand the occurring biological processes  [78]. The annotation of a genome is the process of identifying and cataloging functional information of genes in a sequenced genome  [79]. The important information retrieved from a genome annotation is gene name, assigned cellular functions, and Enzyme Commission (EC) number, for enzyme coding genes  [80]. The merlin, a user-friendly software tool, was created to assist in the processes of annotation and reconstruction of genome-scale metabolic models, by performing automatic genome-wide functional annotations and providing a numeric score for each automatic assignment, taking into account the frequency and taxonomy within the annotation of all similar sequences  [81].
The selection of the best threshold for automatic annotation in merlin involved adjusting the alphavalue (a ratio of taxonomy and frequency score) using a set of random manually curated sequences and comparing these with the automatic annotation provided by the software (automatic classification and final metabolic annotation available in Additional files 3 and 4). In this process, the alpha-value in merlin was set at 0. 9, which emphasises the frequency score, due to the lack of reviewed information on the Stramenopiles lineage. The selected score threshold for automatically accepting annotation in merlin was set to 0. 5, meaning that any candidate genes with a score higher than 0. 5 are automatically annotated. Candidate genes with a score below 0. 2 were automatically discarded (Additional file 3).
8 ), extending then the search, covering a larger amount of possible annotations, whilst decreasing the confidence level. The EC number classification and the taxonomic distance of the results are taken into account, as well as reviewed information and literature on the studied gene. A complete metabolic annotation involves identifying genes encoding enzymes and membrane transporters (Additional file 4). The transporter candidate genes (TCGs) annotation of Pythium irregulare was performed in merlin's TRIAGE (Transport Proteins Annotation and Reactions Generation)  [24]. Initially, protein-encoding genes with transmembrane helices were identified using Phobius  [82, 83]. Afterwards, merlin runs the Smith-Waterman (SW) algorithm  [84]  to compare the target TCGs' translated gene sequences with transmembrane helices with all protein sequences available in the TCDB database.
Acknowledgements The authors are grateful to the  Brazilian Bioethanol Science and Technology Laboratory -CTBE  and the  Institute for Biotechnology and Bioengineering, Centre of Biological Engineering ,  Universidade do Minho  for the infrastructure. The authors also thank  Dr. André Lévesque  from  Agriculture and Agri-Food Canada  and  Pedro Raposo  from  Universidade do Minho  for valuable advice and discussion.
Introduction Description logic (DL) knowledge bases (KBs) consist of a TBox (ontology) that provides conceptual knowledge about the application domain and an ABox (dataset) that contains facts about particular entities  (Baader, Calvanese, McGuinness, Nardi, & Patel-Schneider, 2003). The problem of querying such KBs using database-style queries (in particular, conjunctive queries, or CQs) has been a major focus of recent DL research. Since scalability is a key concern, much of the work has focused on lightweight DLs for which query answering can be performed in polynomial time w. r. t. the size of the ABox. The DL-Lite family of lightweight DLs  (Calvanese, De Giacomo, Lembo, Lenzerini, & Rosati, 2007)  is especially c 2019 AI Access Foundation. All rights reserved. popular due to the fact that query answering can be reduced, via query rewriting, to the problem of standard database query evaluation.
1 for further discussion and references). Arguably the most natural and well-known inconsistency-tolerant semantics is the AR semantics (AR for 'ABox Repair')  (Lembo, Lenzerini, Rosati, Ruzzi, & Savo, 2010 , 2015) , inspired by work on consistent query answering in databases (initiated by  Arenas, Bertossi, and Chomicki, 1999 ; see also the survey by  Bertossi, 2011). Query answering under AR semantics amounts to considering those answers that can be obtained from every repair, the latter being defined as an inclusion-maximal subset of the ABox that is consistent with the TBox. The more cautious IAR semantics (IAR for 'Intersection of ABox Repairs')  (Lembo et al. , 2010 (Lembo et al. , , 2015) )  queries the intersection of the repairs and provides a lower bound on AR semantics. The brave semantics  (Bienvenu & Rosati, 2013) , which considers those answers holding in at least one repair, provides a natural upper bound.
The complexity of ontology-mediated query answering (OMQA) under inconsistencytolerant semantics has been the subject of several papers (see e. g. Rosati, 2011; Bienvenu, 2012; Lukasiewicz, Martinez, & Simari, 2013). Not too surprisingly, given prior negative results on consistent query answering in databases, the AR semantics was shown to be coNPhard in data complexity for DL-Lite ontologies  (Lembo et al. , 2010) , and coNP-hardness has been shown to hold in even more restricted settings  (Bienvenu, 2012) , dashing hopes of taming intractability by limiting the expressivity of the ontology language. The IAR and brave semantics, by contrast, enjoy polynomial data complexity for DL-Lite ontologies (and more generally, all ontology languages that admit first-order query rewritings)  (Lembo, Lenzerini, Rosati, Ruzzi, & Savo, 2011; Lembo et al.
, 2015; Bienvenu & Rosati, 2013). As the complexity of inconsistency-tolerant query answering in the presence of ontologies is now well understood, attention has turned to the problem of implementing these alternative semantics. Most work has focused on the IAR semantics and DL-Lite ontology languages, due to the aforementioned tractability result. The QuID system  (Rosati, Ruzzi, Graziosi, & Masotti, 2012)  implements the IAR semantics, using either query rewriting or ABox cleaning, and the SaQAI system  (Tsalapati, Stoilos, Stamou, & Koletsos, 2016)  implements IAR using ABox cleaning and saturation, as well as the ICAR semantics (that corresponds to the IAR semantics over the saturated ABox,  Lembo et al. , 2010). For the natural but intractable AR semantics, some implementations of consistent query answering have been proposed in the database setting, but we are not aware of any system implementing the AR semantics for DL KBs.
Specifically, we define explanations of positive and negative query answers under brave, AR and IAR semantics. Intuitively, such explanations pinpoint the portions of the ABox that, in combination with the TBox, suffice to obtain a given query (non-)answer under the considered semantics. We focus on ABox assertions since inconsistencies are assumed to stem from errors in the ABox, and because this already yields a non-trivial framework to study. We investigate the main search and decision problems related to explanations: generating an (arbitrary) explanation, generating a most preferred explanation according to some natural ranking criteria, recognizing (most preferred) explanations, and checking whether an assertion is relevant/necessary (i. e. appears in some/all explanations). We study the data complexity of these problems for DL-Lite R , showing (in)tractability of each of the tasks and pinpointing the exact complexity of the intractable decision problems.