dataesr/scientific-paragraphs-categorization huggingface dataset:
However, even on proteins with these characteristics, a test performed with reads simulated on poorly preserved areas will tend to show decreasing performances as the distances increase. To conclude on this matter, tests should be performed on varying protein types and with varying distances to constitute a good prediction on our ability to detect novel viruses. One downfall of the Virus Pop pipeline is however that, as it is based on protein evolution models, the retro-translation step adds silent mutations that are not well accounted for in any defined parameters. Thus, Virus Pop is more suitable for assessing pipelines within which reads and contigs are translated and alignment steps are performed on amino-acid sequences, such as Microseek, which was recently released  [2]. In addition, we demonstrated how generating a high number of simulated sequences (for example the Circovirus capsid protein) succeed at creating new sequences close enough to retrospectively identified real sequences, illustrating the usefulness of Virus Pop in discovering new viruses if simulated sequences are included in public databases used for virus taxonomic assignation.
As suggested by the fact that the simulated sequences on which the tested real new proteins were aligned are scattered in the tree (see Figure  9 ), there are probably multiple generated sequences that are locally very close to the real ones and that participate in the detection of reads. Though the increase of alignment score is moderate, adding simulations to taxonomic assignation tool databases could constitute a strategy to lower the risk of missing viruses belonging to groups of particular concern. Moreover, as the simulations are built with highly constrained evolution models, they should not be responsible for much additional background noise. Finally, this strategy could be improved with a filter based on an approach similar to the one presented for the Sarbecovirus spike protein in Section 3.
MajS tests the consistency between the input data, proposes minimal repairs on the network to establish consistency, and finally computes weighted and signed predictions over the network species. We tested MajS by comparing the HIF-1 signalling pathway with two gene-expression datasets. Our results show that MajS can predict 100% of unobserved species. When comparing MajS with two similar (discrete and quantitative) tools, we observed that compared with the discrete tool, MajS proposes a better coverage of the unobserved species, is more sensitive to system perturbations, and proposes predictions closer to real data. Compared to the quantitative tool, MajS provides more refined discrete predictions that agree with the dynamic proposed by the quantitative tool. MajS is a new method to test the consistency between a regulatory network and a dataset that provides computational predictions on unobserved network species.
Introduction In recent years, there has been a sharp increase in our understanding of biological networks such as metabolic, signalling, and regulatory networks. For many organisms, the biological networks topology is already known, at least partially, and can be found in databases such as KEGG  [1]  or BIGG model repositories  [2]. In parallel, gene expression data is also widely available in databases such as GEO  [3]  and provides us with gene expression profiles before and after a system perturbation. By system perturbation we refer to an over-or under-expressed node (or set of nodes) in the system compared to a control condition. The nodes can be either genes or proteins. Perturbations can be studied in the context of a disease, treatment, or environmental change. For many years, metabolic and regulatory networks have been studied separately.
In a previous study  [10] , we focused on the modelling of a regulatory network of the HIF-1 signaling pathway also called Hypoxia signaling pathway, which is of great interest in neurodegenerative diseases. We compared this regulatory network with Alzheimer's disease gene expression data and we perturbed the system by inducing or repressing the HIF1A protein in silico. In order to allow us to predict the behaviour of unobserved species, the system was modelled using a logical and a Bayesian approach. We demonstrated that the logical approach, Iggy  [7] , was fast and reliable enough to predict unobserved nodes in the network upon system perturbation when compared to the Bayesian approach, Probregnet  [4]. We have encountered, however, two issues that complicate the regulatory-metabolic network integration process.
First, a quantification of Iggy's qualitative predictions (in a three value domain) may introduce new biases to the entire modelling process. Second, because of the semantic of the sign consistency underlying Iggy's modelling approach, the comparison uses relaxed rules that do not allow us to distinguish the computational predictions output from two types of in silico perturbations in this case-study. We propose a novel logical approach using Answer Set Programming (ASP), named MajS, which addresses the previously mentioned difficulties. This approach, similar to Iggy, compares a regulatory network with gene-expression datasets, searches for inconsistencies, proposes minimal repairs and can predict unobserved nodes. It relies, however, on a different sign-consistency rule which takes into account the majoritarian sign of the nodes' direct predecessors.
Sign consistency modelling The sign consistency approach tests the consistency between an interaction graph (IG) and a list of partial discrete observations of this graph nodes derived from experimental datasets. The IG is a signed directed graph, where the edges are signed as "+" or "-" and directed so that i → j means species i influences species j. The list of discrete observations is composed of discrete ("+", overexpressed; "-", under-expressed; 0, no-change) changes associated with some nodes. This change represents the differential expression of a gene between two system conditions (for example, normal and perturbed). Given a sign consistency rule, a graph is said to be consistent with respect to a list of discrete observations if the influences agree with the sign of observations for each node in the list.
Experimental setup We focus on the regulatory network, modelled by an interaction graph (IG), and the impact of a perturbation on this regulatory network evaluated by discrete observations obtained from two gene expression datasets. Biological network -interaction graph We focus on the regulatory network of the HIF-1 signalling pathway, known to be of importance in neurodegenerative diseases  [13]. This graph was extracted from the KEGG database  [1]. Nodes represent proteins or genes, and edges represent activations or inhibitions between two nodes. We reduce the regulatory network by keeping only nodes associated with expressed genes in the two datasets used in this study. The two networks, respectively reduced with Alzheimer's disease (AD) and RNA-Seq datasets, are composed of 94 and 81 nodes and 285 and 233 edges.
Datasets We evaluate our model against two datasets composed of gene differential expression in two conditions. The Microarray dataset corresponds to cells from the hippocampus brain region  [14]. It compares data from AD patients to data from Healthy individuals. The RNA-Seq dataset corresponds to HUVECS (Human umbilical vein endothelial cells)  [15]. It compares the HUVECS response to an induced overexpression of HIF1A to one with a normal HIF1A expression. Benchmarks We aim to study the impact of perturbing the system with a focus on the node HIF1A, a key protein of the HIF-1 signalling pathway. Recall that, one of the inputs of our method consists of a list of discrete observations for which a significant change of expression is detected between two conditions. The changes of expression our method accepts are: "+", over-expression; "-", under-expression; and "0", no-change of expression.
The values of the thresholds used to detect significant over-or under-expression are fixed according to the nature of each dataset as detailed below. Thresholds choice in HUVECS Benchmarks For the RNA-Seq dataset, we used the logFC (log of gene expression) from cells with HIF1A in vitro over-expressed over normally expressed genes that were already provided in  [15]. We use a threshold of 1. 5 that is commonly used for logFC as said in  [16]. The genes with logFC over 1. 5 are set to "+", the ones below -1. 5 are set to "-", and the ones between -0. 15 and 0. 15 are set to "0". Using these thresholds, we obtain 30 observed nodes (out of 81 in the graph).
Thresholds choice in AD Benchmarks We aimed to study the impact of perturbation over HIF1A on the enzymes for the AD dataset. We used a threshold over the fold change distribution. The fold change is the expression of the gene in AD patients over the expression of the corresponding gene in Healthy individuals of all the genes in this dataset. The genes with FC that are over the third quartile are set to "+"; the ones under the first quartile are set to "-"; and the ones between 0. 99 and 1. 01 are set to "0". Using these thresholds, we obtain 64 nodes (out of 96 in the graph) that compose the input observation list of our method. For the AD case, perturbations of HIF1A are only done in silico. We generate 3 different perturbations by adding the following observations to the list of 53 observations, described before: (plus) HIF1A='+', (minus) HIF1A='-', and (zero) HIF1A='0'.
Data The IG for this case study is composed of 81 nodes and 233 edges derived from the HIF-1 signalling pathway and compared with a RNA-Seq dataset from HUVECS (see Section 3. 1). This IG is compared with two different lists of discrete observations; denoted by Benchmark 1 and Benchmark 2 in Table  1. Benchmark 1 is composed of 30 nodes that are a partial observation of the IG, generated by estimating significantly expressed genes in the RNA-Seq dataset using specific thresholds (see Section 3. 1). Benchmark 2, composed of 25 nodes, is a modification of Benchmark 1; where we have altered or removed the value of 9 observations, direct neighbours of HIF1A or directly linked to the network enzymes. These modifications were done to improve the coverage of Iggy. By modifying these observations, the problem becomes simpler to solve for Iggy, leading to a better coverage for Iggy.
MajS results on HUVECS dataset On Benchmarks 1 and 2, MajS generates predictions for all initially unobserved nodes. MajS is configured by setting K = 3 as the maximum artificial influences per node. The computations took approximately 97 s. 3  for each benchmark. MajS obtains 2016 optimal answer sets for both benchmarks, that is, 2016 assignments of nodes with a sign. The number of minimal artificial influences added by MajS to restore consistency in both benchmarks was 8. They are spread over seven repaired nodes and a maximum of 2 artificial influences per repaired node. The HIF-1 signalling IG is 2-consistent concerning the HUVECS dataset (see Section 5. 1. 3).
Comparison of MajS and Iggy The aim of the sections from 3. 2. 3 to 3. 2. 5 is to understand the difference in prediction on the HUVECS dataset between MajS and Iggy. Iggy is described in Section 2 and in  [10]. In Table  1 , we show a global comparison of both tools with the two different benchmarks. For Benchmark 1, 51 nodes are unobserved in the IG. We can see that MajS was able to predict all of them (100% of coverage), whereas Iggy could predict only 30 nodes (59% of coverage). In order to compare the predictions' signs for both methods, we consider for MajS the majoritarian sign of the predicted nodes. 22 nodes are predicted with the same sign for both methods, while 8 nodes are predicted differently between both methods. For Benchmark 2, we obtain for both methods 100% of coverage. The number of predicted nodes in common is 48, and the number of predicted nodes different remains 8.
Comparison of MajS and Iggy predictions with real data Using a normal distribution mixture (see Section 5. 4), we compare the significance score of both methods to predict the real fold change data. This one is extracted from HUVECS dataset (see Section 3. 1) where the perturbation was conducted in vitro. The aim of comparing this data with both methods is to see if they are able to model a perturbation in silico and have predicted fold change close to the fold change with an in vitro perturbation. This comparison is conducted as a validation. We apply our method introduced in Section 5. 4 using µ -= -0. 394, µ 0 = 0 and µ + = 0. 489 as parameters of the three normal distributions. These values are the respective means observed in the experimental HUVECS data using the thresholds fixed in Section 3.
MajS applied to model HIF-1 signalling pathway and Alzheimer's disease (AD) dataset integration This section focuses on the enzyme prediction to do the link with the metabolic network. Our first aim is to illustrate the difference in coverage and sensitivity between the two discrete approaches: MajS and Iggy, on a Microarray dataset of Alzheimer's Disease patients. Our second aim is to compare MajS with a Bayesian quantitative approach, Probregnet, to point out the similarity of predictions in terms of dynamic evolution across different perturbations. Probregnet  [4]  predicts unobserved nodes with quantitative values in a regulatory network (see details in Section Background 5).
Data The IG for this case study comprises 94 nodes and 283 edges. It was derived from the HIF-1 signalling pathway (see Section 3. 1) and initially compared to three different lists of discrete observations. Each list is based on the AD Benchmark generated by estimating significantly expressed genes in the AD dataset using thresholds (see Section 3. 1). The three lists of observations were derived by fixing the value of node HIF1A to "+", "-", or "0" in order to simulate in silico a HIF1A perturbation. These three original datasets were composed of 64 observed nodes. When comparing the IG with these original datasets, Iggy provided new predictions for only 9 nodes (out of 30 unobserved ones), whereas MajS predicted the 30 nodes. In order to provide a comparison of the predictions of MajS, Iggy and Probregnet for this case study, we modified the original datasets by fixing the value of HIF1A neighbours and removing observations referring to enzymes.
In total, we performed 14 modifications. These modifications were done with a similar idea as for the HUVECS dataset (see Section 4. 3. 1), to improve Iggy's prediction coverage. With the original dataset, Iggy predicted less than 30% unobserved nodes. Besides, observed enzymes are removed to see if the prediction agrees with the real dataset observations. Our three resulting lists of observations were composed of 54 observations. We denote these three benchmarks as: Benchmark zero, Benchmark plus, and Benchmark minus referring to HIF1A fixed to "0", "+", "-" respectively. The rest of this section is presented with these modified benchmarks.
MajS results on AD dataset The computation took less than 40s. for each benchmark. For Benchmark minus we obtain 480 optimal answer sets with 32 repaired nodes, for Benchmark zero we obtain 320 optimal answer sets with 27 repaired nodes and Benchmark plus we obtain 160 optimal answer sets with 28 repaired nodes. The number of artificial influences K added for each repaired node by MajS to restore consistency was maximum 4. The IG is 4-consistent for the AD dataset (see Section 3. 1).
MajS is more sensitive than Iggy to nodes perturbations This section focuses on the 15 enzymes present in our IG; a more refined discrete prediction of these nodes may facilitate the IG model integration with a metabolic network model. Table  3  presents the computational predictions of MajS and Iggy on the enzyme nodes when comparing the IG with the three datasets of observations with different values ("-", "0", "+") set for HIF1A. All MajS predictions were strong predictions (i. e. , no variation across all optimal answer sets) with a unique weight. For Benchmark minus, Iggy could not predict the enzyme signs, while MajS was able to give a majoritarian sign of "0" associated with a weight of 100 to all the enzymes. This is explained by the different rule imposed to each node by Iggy (sign-consistency) and MajS (majoritarian sign).
As found for the HUVECS dataset (see Section 3. 2. 3), Iggy constraints less the problem, generating more answer sets and producing fewer predictions. For Benchmark zero and Benchmark plus, Iggy proposes similar predictions; consequently, it is not possible to observe any impact of the HIF1A perturbation. Instead, for MajS 12 enzymes are predicted as "+", and they hold different weights (25 and 50 respectively) according to the benchmark. MajS is more sensitive than Iggy to perturbations on node HIF1A for this case study. It allows a measurable repercussion of the perturbation with different strengths for most enzymes. This is possible thanks to the weight term used in the domain of the answer sets obtained with MajS, and the weight assignment rule (see Section 5. 1. 2).
Discussion and Conclusion We present in our study a new logical approach, MajS, implemented in Answer Set Programming. MajS takes as input an interaction graph and a set of discrete observations. Discrete observations are expressed in the forms of "+", "-", or "0" signs in some of the graph nodes. This information is extracted from gene expression datasets. MajS tests the consistency between the majoritarian sign of a node's direct predecessors and the node's sign; detects and repairs inconsistencies, and predicts unobserved nodes. MajS' prediction is given as a sign and a weight assigned to each unobserved node, where the weight represents the sign confidence. In addition, MajS outputs information concerning the prediction distribution across all consistent optimal answer sets or models. MajS was tested On the right y-axis is the weighted label given by MajS.
The x-axis shows the names of the enzymes. In purple, those that agree on evolution across perturbations between Probregnet and MajS. In orange, those with a disagreement on evolution. on two networks derived from the HIF-1 signalling pathway. These two networks were reduced with Alzheimer's disease (AD) Microarray and HUVECS RNA-Seq datasets. They are composed of respectively, 94 and 81 nodes and 285 and 233 edges. For both studied networks, MajS finds results in a couple of minutes which opens perspectives to handle larger networks. MajS outputs informative predictions on all unobserved nodes such as the majoritarian sign, the average weight, and the standard deviation of this weight in all benchmarks studied. Several results are obtained upon comparison to a similar discrete and logical approach, Iggy.
Another limitation of MajS is that it keeps the sign of observed nodes even if they are inconsistent. In case of many inconsistent nodes, we should question the quality of the experimental data or the interaction graph. Because of these inconsistencies, the method may give predictions which are not relevant. All in all, we have proposed MajS, a new method, fast and reliable, that tests consistency and predicts the change of expression on unobserved nodes (sign and weights) when comparing a regulatory network with a gene expression dataset. Our method is applied to perturbed data. In particular, we applied this method to test consistency between the HIF-1 signalling pathway and a HIF1A overexpressed dataset. Besides, Majs by predicting weights allows us to have a more refined prediction and proposes predictions which are sensitive to system perturbation.
MajS application on a toy example This section presents the results obtained while applying MajS on a toy example, an IG composed of 10 nodes, 7 activation edges, and 1 inhibition edge (E ⊣ D). In Figure  5  we illustrate how MajS proceeds when comparing this toy IG with one dataset of observations. First, MajS adds two artificial influences (art1 and art2) on node I to recover consistency. Then, it predicts values over nodes D, E, and G. In Figure  5 , we show the prediction of MajS for nodes D, E, G, and the repaired node, I. This toy example outputs two optimal answer sets: Solutions 1 and 2. Focusing on node D, we observe that in the answer set Solution 1, the predicted weight is 75, while in Solution 2, the predicted weight is 50. Node D has 4 parents: 3 observed (A ="-", B ="+", C ="+"), and an unobserved parent E.
Finally, in Figure  5  (b), we see that for node I, the weight is fixed to 0 by MajS in all optimal answer sets, implying that it was repaired. Indeed, J is the only predecessor of I and activates it. Thus, J and I would have the same sign in a consistent local behaviour. To guarantee a global consistency of the whole network, I should be repaired. The inputs of the logical program for the toy case study can be found on GitHub: https: //github. com/soph-lebars/MajS/tree/main/toycasestudy.
Different and common points between MajS and Iggy We summarise the main differences and similarities between MajS and Iggy in Table  4. The common points are that the two tools use the ASP paradigm to describe the inputs of the logical program, also called an instance, and to encode the logical constraints of the problem. Secondly, the optimisation of the problem is the same. Both methods seek to minimise the number of influences added to repair. The two methods are different in three ways. First, the solution space for MajS will be larger because the sign of the nodes is also associated with a weight ranging from 0 to 100. The second difference is that Iggy adds only one influence per node to fix inconsistencies, whereas MajS adds several influences to reestablish consistency. The third difference is in the logical rules, which are implemented differently.
Further, large asset-intensive organizations will be analysed, focusing on physical asset management, hence the results may not be relevant for service industry and organizations where physical assets are not the main generator of value. Introduction Courses focusing on database fundamentals are offered in all IT-related departments, but also in engineering, accounting, and generally in departments dealing with data management  (Nagataki et al. , 2013). Typically, the content of these courses covers topics related to data models, database design and introduction to the SQL language  (Conklin & Heinrichs, 2005; Robbert & Ricardo, 2003). More recently, due to the rapid production of data from various sources, there is a need to manage these huge amounts of data, and the teaching of NoSQL database systems, which are based on a different design from structured SQL databases, is a hot topic  (Dietrich et al.
, 2008; Garcia-Molina, 2008). Also, at a more advanced level of study, there are courses that include topics related to DB architecture, transactions, concurrency, reliability, distribution, parallelism, replication, query optimization, as well as the design of comprehensive case studies aimed at solving a real business problem  (Abid et al. , 2015; Domínguez & Jaime, 2010). There are research papers that propose evaluation methods in both the theoretical and laboratory parts of the database course. (Kawash et al. , 2020). There are proposals for teaching this course  (30-50 hours)  with the aim of learning both theoretically and in the laboratory to understand the concepts of designing a DB and the commands needed to implement a relational DB. (Cvetanovic et al. , 2010). The increasing use of cloud computing combined with big data is leading to the adoption of new tools that students can use and apply to their future clients after graduation  (Ishaq et al.