dataesr/scientific-paragraphs-categorization huggingface dataset:
Distance is a key concept of the temporal CBR approach. It is used intensively to sort and index the cases along the composition tree. It is used to find what attributes and object cases show a similar evolution. The smaller the distance, the more similar the evolution of the attributes and objects. As shown in the CBR cycle of Figure  11 , attributes and objects are indexed by their similarity and level of granularity, and then stored in the cases database. distance( f A(t), f B(t)) = t n t 0 f B(t)dt - t n t 0 f A(t)dt i=n ∑ i=0 | f B(t i ) -f A(t i )|(t n -t 0 ) n (.
The proposed approach in this paper applies fuzzy characteristic functions on object composition (is-part-of) and object-attribute relationships (has-a), whose existence is Boolean (that is, they either exist or do not exist) in classic object models. Thus, the proposed approach is not of fuzzy logic, but it is an extension of an object-oriented model that implements fuzzy composition and fuzzy (has-a) relationships with their qualifier attributes (QAD). This is a new extension of the ODMG meta-model in implementing fuzzy vector spaces, as shown in Figure  4. Several authors have proposed extensions of object-oriented databases with fuzzy logic  [7, 8] , for applications such as video application databases [9]; querying on fuzzy multivalued attributes  [73] ; reasoning on fuzzy object-oriented databases  [74] ; for deduction  [75] ; or to choose appropriate building elements in construction  [76].
Moreover, the FVS scalar product allows each attribute and component to be weighted according to its importance, as given by the domain expert, to assess the object and to take appropriate decisions, for example, to perform a diagnosis, predict a suitable treatment, or to assess the prognosis of a clinical state. The FVSOOMM method was described in this paper and provides a guideline to design object-oriented knowledge bases for many different domains and temporal case databases. These contain the objects required for the domain, together with their associated attributes and the fuzzy vectors necessary to assess their current states and temporal evolutions. Until now, FVSOOMM was used manually to develop knowledge bases. This paper presents a prototype version of the new platform used to develop knowledge base objects, their attributes and fuzzy vector spaces, and to verify their consistency.
Comparative analysis of Genome-Scale Metabolic Networks (GSMNs) may yield important information on the biology, evolution, and adaptation of species. However, it is impeded by the high heterogeneity of the quality and completeness of structural and functional genome annotations, which may bias the results of such comparisons. To address this issue, we developed AuCoMe -a pipeline to automatically reconstruct homogeneous GSMNs from a heterogeneous set of annotated genomes without discarding available manual annotations. We tested AuCoMe with three datasets, one bacterial, one fungal, and one algal, and demonstrated that it successfully reduces technical biases while capturing the metabolic specificities of each organism. Our results also point out shared metabolic traits and divergence points among evolutionarily distant species, such as algae, underlining the potential of AuCoMe to accelerate the broad exploration of metabolic evolution across the tree of life.
Introduction The comparison of genomic data gave rise to today's view of the three domains of life: bacteria, archaea, and eukaryotes, the latter being divided into five putative supergroups, opisthokonts, amoebozoans, archaeplastids (including green and red algae lineages), Stramenopiles-Alveolata-Rhizaria (including brown algae) and Excavata, and additional uncertain lineages  (Burki et al , 2020). The evolution of the organisms within these lineages is linked to their ability to adapt to their environment and, therefore, to the plasticity of their metabolic responses. In this context, the comparison and analysis of Genome-Scale Metabolic Networks (GSMNs) constitute a powerful approach  (Gu et al , 2019). The number of sequences available in public databases is continuously rising, as illustrated by the GenBank database, which grew by 74.
Indeed, a variety of methods exists to perform structural (gene structure prediction) and functional (association of functions to genes) annotation steps  (Yandell and Ence, 2012)  and the method choice has previously been shown to have direct effects on the reconstructed GSMNs  (Karimi et al , 2021). Similarly, numerous methods for GSMN reconstruction have been developed, e. g. Pathway Tools  (Karp et al , 2019) , RAVEN  (Wang et al , 2018) , merlin  (Dias et al , 2015a) , KBase  (Arkin et al , 2018) , Model SEED  (Devoid et al , 2013) , AuReMe  (Aite et al , 2018) , AutoKEGGRec  (Karlsen et al , 2018) , CarVeMe  (Machado et al , 2018) , and gapseq  (Zimmermann et al , 2021a). They rely on one or several metabolic databases such as MetaCyc  (Caspi et al , 2020) , KEGG  (Kanehisa and Goto, 2000; Kanehisa et al , 2017) , Model SEED  (Seaver et al , 2021)  or BiGG  (King et al , 2016).
Despite efforts in the direction of database reconciliation  (Moretti et al , 2021) , such heterogeneity of metabolic databases still requires time-consuming matching of their respective identifiers and may thus impede the comparison of GSMNs generated. One strategy to resolve the issue of GSMN comparison is to work directly on GSMNs. A first method is the reconstruction annotation jamboree  (Thiele and Palsson, 2010) , a community effort to curate pathway discrepancies by examining reactions, Gene-Protein-Reaction (GPR) associations, and metabolites in GSMNs in order to create a consensus GSMN for an organism. This is relevant for organisms for which multiple GSMNs exist, in order to establish a reference one. This strategy was successfully applied to Salmonella Typhimurium LT2  (Thiele et al , 2011)  as well as Saccharomyces cerevisiae  (Herrgård et al , 2008) , and was later extended to multiple organisms to create a panmetabolism of 33 fungi from the Dikarya subkingdom  (Correia and Mahadevan, 2020).
Although platforms now facilitate such community efforts  (Cottret et al , 2018) , these methods are costly in terms of time and manpower involved. Another method was proposed by  Oberhardt et al (2011)  to resolve the comparison of GSMNs by reconciling their GPR associations using reciprocal gene pairs. A second strategy to resolve GSMN comparison issues is to adapt the GSMN reconstruction method used to tackle these issues. This strategy aims at reducing annotation biases through the reconstruction of GSMNs from homogeneously annotated genomes using the same method and database, possibly followed by the propagation of annotations with sequence alignments  (Vieira et al , 2011; Prigent et al , 2018). This strategy was pushed forward and automatized in the tool CoReCo, which enabled the reconstruction of gap-less metabolic networks from several non-annotated genomes  (Pitkänen et al , 2014; Castillo et al , 2016).
The main limitation of such approaches is that the re-annotation of the genomes supplants the previous genome annotation. Annotations of genomes in databases also reflect the expertise of scientists. Such valuable information, possibly originating from manual curation, would be lost during a systematic re-annotation step. For a reliable interpretation of data, expert annotations therefore ought to be preserved while automatically inferring metabolic networks from any type of genomic resource. In this article, we introduce a new method, AuCoMe (Automated Comparison of Metabolism) that creates a set of homogenized GSMNs from heterogeneouslyannotated genomes. This enables the less biased functional comparison of the networks and the determination of metabolic distances using the presence/absence of reactions.
Our objective was to develop an efficient, fast, and robust approach, which does not depend on the quality of the initial annotations and is able to aggregate heterogeneous information. AuCoMe combines metabolic network reconstruction, propagation, and verification of annotations. The method automatizes the strategy of transferring information from the annotations of the genomes and complements this information transfer with local searches of missing structural annotations. AuCoMe was applied to three heterogeneous datasets composed of fungal, algal, and bacterial genomes, respectively. Our results demonstrate that AuCoMe succeeds at propagating missing reactions to degraded metabolic networks while capturing the metabolic specificities of organisms despite profound differences in the quality of genome annotations.
A tool for homogenizing metabolism inference AuCoMe is a python package that aims to build homogeneous metabolic networks and panmetabolisms starting from genomes with heterogeneous functional and structural annotations. AuCoMe propagates annotation information among organisms through a four-step pipeline (Fig. 1 ). As a first step, draft metabolic networks are automatically inferred from the original annotations of genomes using Pathway Tools (Fig. 1A ). By default, only reactions supported by gene associations are kept in the draft metabolic networks (see Methods). The resulting GSMNs and their proteomes are then subjected to comparative genomic analyses. During this process, GPR associations are propagated across GSMNs according to orthology relations established using OrthoFinder (Fig.
1B ). A robustness filter (see Methods) then selects the robust GPR relationships among all propagated associations. A third step consists in checking for the presence of additional GPR associations by finding missing structural annotations in all genomes (Fig. 1C ). Finally, a fourth step adds spontaneous reactions associated with MetaCyc metabolic pathways that were completed by the two previous steps (Fig. 1D ). The AuCoMe pipeline was tested on three datasets composed of genomes that offer different levels of phylogenetic diversity. The bacterial dataset includes 29 genomes belonging to different species of Escherichia and closely related Shigella, the fungal dataset (74 fungal genomes and 3 outgroup genomes) covers a range of different phyla within this kingdom, and finally the algal dataset (36 algal genomes and 4 outgroup genomes) exhibits the highest phylogenetic diversity including eukaryotes from the supergroups SAR (Stramenopiles, Alveolata and Rhizaria), Haptophyta, Cryptophyta, and Archaeplastidia.
For all species included in the three datasets, both annotated genomes and proteomes were publicly available (see Appendix Tables S1, S2, S3). Run times of AuCoMe on a cluster were 7 hours (10 CPUs), 25 hours (40 CPUs), and 45 hours (40 CPUs) for the bacterial, fungal, and algal datasets, respectively. Details for individual steps are reported in Appendix, section 2.
AuCoMe homogenizes the content of metabolic network collections In Fig. 2 , we compared the number of reactions inferred during the different steps of the AuCoMe pipeline. After the draft reconstruction step, draft GSMNs from the three datasets exhibit a highly heterogeneous range of reactions (Fig. 2  A and blue bars in Fig. 2 B, C, D ). This is especially visible for the fungal dataset, demonstrating the variability of the original genomic annotations. In this dataset, no reactions were inferred from annotations in seven species, and 12 draft GSMNs contained less than ten reactions. For the latter, their respective genome annotations included no EC number, and eleven genomes did not include any GO term. As Pathway-Tools relies mainly on these two annotations to infer reactions, this absence impedes the reconstruction process.
Similar observations were also made, although to a lesser extent, for the algal genome dataset, with seven genomes having more than 2,000 reactions and seven genomes less than 500 reactions. At this step, high heterogeneity in the number of reactions can be attributed mainly to differences in the quality and quantity of the functional annotations provided, precluding biologically meaningful comparisons of the GSMNs obtained at the draft reconstruction step. After the orthology propagation step, we observed an homogenization of the number of reactions in the datasets (orange bars in Fig. 2 ). GSMNs with few reactions after the draft reconstruction recovered more reactions during the orthology propagation step compared to GSMNs with thousands of reactions. This observation is supported by the negative correlation between the number of reactions added at the draft reconstruction step and at the orthology propagation step (Spearman's rs -0.
82 p¡0. 001). The fungal dataset exhibits an outlier at this step; the GSMN of Encephalitozoon cuniculi contained only 681 reactions compared to the thousand of reactions in the other fungal GSMNs. This is consistent with the fact that this species is a microsporidian parasite with a strong genome and gene compaction  (Grisdale et al , 2013). In all datasets, among the reactions propagated byorthology, a few hundred were removed because they did not fulfill the robustness score criterion (see methods). Compared to the orthology propagation, the structural verification step had a smaller impact on the size of the final networks (green bars in Fig. 2 ). Ninety-five percent of the GSMNs received less than 28 reactions during this step, and the maximum was 209. In the bacterial dataset, the six Shigella received an average of 76.
2 reactions which is more than ten times the average of the other strains (7. 4). We manually examined these differences and found that the majority of genes in the GPR associations added at this step in the Shigella's GSMNs corresponded to pseudogenes. For the fungal dataset, AuCoMe added 209 reactions for the species Saccharomyces kudriavzevii. These reactions were associated with 192 sequences recovered during the structural step. For all of these sequences, we found corresponding transcripts in a previously published transcriptome dataset  (Blevins et al , 2021). As for the algal dataset, 86 reactions were added for Ectocarpus subulatus. Likewise, we validated the presence of 59 out of 65 genes (83 out of 86 reactions) by associating them with existing transcripts. The remaining six genes (three reactions) corresponded to valid plastid sequences that had remained in the nuclear genome assembly.
These genes lacked transcription data, because plastid mRNA lacks the PolyA tail used to prepare most RNAseq libraries. In both outlier cases, the structural completion step was, therefore, able to recover sequences that are translated into mRNA and thus likely to correspond to functional genes. Lastly, the spontaneous completion step added spontaneous reactions to each metabolic network if these reactions complete BioCyc pathways (red bars in Fig. 2 ). For the fungal dataset, this step added between two and 23 spontaneous reactions, leading to two to 27 additional MetaCyc pathways that achieved a completion rate greater than 80%. As for the algae, the same step added between 4 and 36 spontaneous reactions yielding two to 31 additional pathways that reached a completion rate greater than 80%.
Generally, we observed that the fewer reactions were inferred at the draft reconstruction step, the more spontaneous reactions were added to complete pathways (Pearson R = -0. 83 and -0. 84 for the fungal and algal datasets, respectively). This can be explained by the fact that the only other possibility to introduce spontaneous reactions in the GSMNs is the draft reconstruction step because Pathway Tools infers GSMN with both enzymatic and spontaneous reactions. When looking at the size of the final networks, we observed in the bacterial dataset (Fig. 2B ) that the networks of Shigella strains comprised fewer reactions than the rest (average of 2,148 reactions vs. 2,294, Wilcoxon rank-sum test W = 138, P = 2e-4). This is consistent with the results of  Vieira et al (2011)  (average of reactions of 1,437 for Shigella strains compared to 1,504 for non-Shigella strains).
On the other hand, E. coli K-12 MG1655 stood out with 2,568 reactions compared to the range of reactions between 2,047 and 2,342 for the other strains. An explanation is that this strain is clearly the best annotated of all tested strains, and the corresponding reference genome in the databases has been extensively curated. For this strain, 172 reactions were removed by the robustness filter, which is less than the 333-447 reactions removed for the other strains. The difference between these two numbers can be explained because reactions propagated from E. coli K-12 MG1655 to the other strains were frequently supported by only one gene predicted at the draft reconstruction step, and were removed after the orthology propagation (see Methods). Overall, in the three datasets, the final GSMNs were of similar size after applying AuCoMe regardless of the quantity and quality of their corresponding genome annotations.
Validation of filtering steps and final GPR associations To validate the above results, two independent verification procedures were performed, the first one at the level of the orthology propagation step in order to evaluate the efficiency of the propagation filters, and the second one applying manual examination of the final networks for the algal dataset. For the first verification procedure, we extracted the EC numbers of all reactions of the fungal and the algal dataset GSMNs for which GPR associations were only predicted by orthology propagation. For each EC number, we then extracted the associated protein sequence and used DeepEC  (Ryu et al , 2019)  to infer EC numbers for these proteins and compared them (if any) to the EC number(s) linked to the reaction by the pipeline. An enrichment of sequences confirmed by DeepEC is observed in robust GPR associations compared to those discarded by the filter: 24% vs.
4. 9 % in the fungal dataset and 13% vs. 1. 4% in the algal dataset (see Appendix Fig  S3 ). This confirms that the robustness filter removed predominantly poorly supported reactions. The second evaluation of the reliability of the reconstruction process was performed on the final algal dataset. We manually examined 100 random GPR associations across the metabolic networks generated by AuCoMe: 50 reactions that were predicted to be present and 50 reactions that were predicted to be absent (see methods). Not counting spontaneous reactions (seven among the randomly selected reactions), manual annotations and automatic predictions corresponded in 86% of all cases (42/49) for the reactions predicted to be present and in 91% (40/44) for the reactions predicted to be absent (see Appendix Tables  S5  and S6 ).
Validation of the orthology propagation and structural verification steps Thirty-two datasets were formed, each containing the 29 bacterial E. coli and Shigella strains studied in  Vieira et al (2011) , among them a replicate of the E. coli K-12 MG1655 genome being degraded to a variable extent, in its functional and/or structural annotations (see Methods and Appendix Table  S4 ). The manuallycurated EcoCyc database  (Karp et al , 2018)  was used to check the reliability of the GSMN reconstructed for each corresponding degraded genome. For each of the 32 datasets, F-measures were computed at each AuCoMe step according to comparisons of the reconstructed GSMN with the gold-standard EcoCyc database (see Methods). The F-measure stands for the harmonic mean of the AuCoMe precision and recall, with values close to 1 indicating high precision and recall i.
e. low rates of false positives and false negatives, respectively. The maximal theoretical value of the F-measure in our analyses was obtained by computing the maximal F-measure that can be reached when comparing to EcoCyc. To achieve this, we computed the number of reactions contained in EcoCyc but not in the panmetabolism of dataset 0, which is the union of GSMNs created by AuCoMe from all the genomes of dataset 0, including the non-degraded annotations of the E. coli K-12 MG1655 genome. Among the reactions of EcoCyc, 1,019 reactions could not be retrieved in this panmetabolism. This number of reactions corresponded to the false negatives, the other reactions of EcoCyc corresponded to the true positives and using these measures we computed the maximal theoretical F-measure of 0. 79. A first analysis was performed on the non-degraded dataset 0, and after running the AuCoMe process, the F-measure between the GSMN of the E.
coli K-12 MG1655 genome in dataset 0 and EcoCyc was 0. 67. Therefore we consider 0. 67 as the reference F-measure for this experiment. Fig. 3  A illustrates the number of reactions predicted by the steps of AuCoMe for the E. coli K-12 MG1655 GMSN in each of the 32 synthetic bacterial datasets to assess the importance of each step in the homogenization of the GSMN sizes. Fig. 3  B represents the F-measure for the corresponding dataset. As expected, increasing degradation led to the recovery of fewer GPR associations and decreased F-measures in the draft GSMN (blue bars and blue circles). When all annotations were removed (dataset 10, 21 and 31) both the number of reactions and F-measure were closed to 0. When only functional annotations were degraded in the E. coli K-12 MG1655 genome (dataset labeled 1 to 10), the orthology propagation step enabled the recovery of a large number of reactions, nearly up to the size of the GSMN associated with the non-degraded genome.
This also resulted in increasing F-measures (orange bars and orange triangles). In datasets where only structural annotations of the E. coli K-12 MG1655 genome were altered (datasets labeled 22 to 31), the structural verification step recovered a part of the GPR associations and a F-measure close to the reference F-measure of 0. 67 (green bars and green crosses). Finally, when both structural and functional annotations were degraded (datasets 11 to 21), the combination of the second and third AuCoMe steps recovered the discarded reactions. Notably, even when 100% of the E. coli K-12 MG1655 functional and structural annotations are degraded, the information from the other 28 non-altered genomes enabled the recovery of 2,244 reactions (Fig. 3  A, dataset 31) and a F-measure of 0. 60. Altogether, these results demonstrate that, by taking advantage of the annotations present in the other genomes of the considered dataset, AuCoMe builds GSMNs with reactions even for genomes completely missing functional and structural annotations.
Biological validation on the Calvin pathway The Calvin cycle is a pathway of biochemical reactions present in photosynthetic organisms to fix carbon from CO 2 into three-carbon sugars. In the MetaCyc v. 23. 5 database  (Karp et al , 2019) , the Calvin cycle (id CALVIN-PWY) is composed of 13 reactions. In Fig. 4 , the different reactions (rows) predicted for each genome of the algal dataset (columns) are depicted together with the step of AuCoMe at which the prediction occurred. In this example, all three main AuCoMe steps are required to obtain a homogeneous view of this pathway in all organisms. The draft reconstruction (blue) and the orthology propagation (orange) steps provide most of the reactions. Interestingly, the pathway was predicted to be complete for three species G. pectorale, C.
None of the 40 GSMNs contained the reaction SEDOBISALDOL-RXN. In the MetaCyc v23. 5 database, which was used for the experiment, this reaction was associated with the EC number 4. 1. 2. X, whereas it was associated with the EC number 4. 1. 2. 13 in the KEGG  (Kanehisa et al , 2008)  and Brenda  (Schomburg et al , 2017)  databases. This prevented the draft reconstruction tool (Pathway Tools v23. 5) from associating genes annotated with EC 4. 1. 2. 13 to this reaction. We therefore manually added the corresponding GPR associations, indicated in yellow in Fig. 4. Regarding the outgroup organisms, the robustness criterion applied during the orthology propagation step removed a weak GPR association with the reaction RIBULOSE-BISPHOSPHATE-CARBOXYLASE-RXN for N. crassa. This weak association was previously reported and explained by the sharing of a SET domain by Rubisco and other lysine protein methyltransferases  (Yeates, 2002).