dataesr/scientific-paragraphs-categorization huggingface dataset:
Correlation of parameters of interest Since our proposed sampling algorithm is based on the number of retweets, the number of followers and the favorite count, we study the correlation of these parameters in our datasets. Intuitively, we would expect them to be correlated positively, under the assumption that people who have more followers are influential, so they would get more retweets and likes. In addition to this, tweets that have good content are retweeted and the owner of the tweet would get more followers. The relation between the parameters of interest is shown in Fig. 2. As expected, the correlations are positive, expect a small negative correlation of -0. 05 between the number of followers and number of retweets in Samsung. The highest correlation of 0. 58 is between number of retweets and the favorite count for Trump (Fig.
2a ), followed by 0. 41 for Obama (Fig. 2b ). Nevertheless, we notice low correlations between the number of followers and the favorite count or number of retweets of the datasets of people. This is an interesting observation that shows that the number of followers of the owner is not correlated to the content of the tweets he posts. La La Land (Fig. 2c ), The Voice (Fig. 2d ), and Samsung (Fig. 2e ) are characterized generally by low correlations. In contrast to the datasets of peo-ple, the number of followers seems to be more correlated with the content of the tweets. Generally, the low values of correlation indicate that the parameters are not dependent on each other. Consequently, our sampling algorithm is not sensitive to the internal correlation between parameters, resulting in a better sample.
Similarly, Trump dataset shows that tweets with more than 6 entities do not get high values of parameters of interest, simply because it is a rare event. However, in the case of Trump , there is a considerable amount of reaction even for tweets with only one entity, which would be Trump. This shows that event involving only Trump gets retweets and likes (Fig. 3d  and Fig. 3e ). In the case of the number of followers, the behavior is equally distributed between tweets of 1-4 entities (Fig. 3f ). La La Land (Fig. 3g , Fig. 3h ) behaves similarly to Trump, most of the reaction as related to tweets with 1 entity. In the case of the followers (Fig. 3i ), in contrast to Trump, La La Land continues to be concentrated in tweets with one NE. It is important to mention that La La Land is characterized by lower coverage and density of NEs compared to the people datasets, but also by low values of parameters of interest.
The Voice is the dataset that has the lowest numbers in terms of coverage and density of NEs. These statistics are obvious in the corresponding figures (Fig. 3j , Fig. 3k , Fig. 3l ). The distribution is almost equal for tweets with more than one entity. Samsung brings new insights, where the tweets with more than 4 entities are able to get a reaction comparable to tweets with less than 4 entities (Fig. 3m , Fig. 3n , Fig. 3o ). Apparently, events, where Samsung is involved, contain more NEs compared to other entities and they are interesting enough as to attract the audience.
The richness of weighted sample In our approach, we propose using weighted sampling for reputation discovery. Our hypotheses states that the weighted sampling provides richer information These indicators are calculated for each of the samples. We iterated the procedure for 10 random samples and 10 weighted samples, for each of the datasets. The average of the indicators are presented in Table  3. Table  3 : Average indicators of the samples According to Table  3 , the weighted sample is significantly richer in terms of the aforementioned indicators for Trump, Obama, and Samsung datasets. Tweets that contain more information are more useful to be analyzed. Nevertheless, in terms of entities in La La Land and in terms of hashtags in The Voice, weighted sample has not been able to perform better.
Frequent Named Entity Mining in weighted sample Frequent Named Entities are discovered through itemset mining techniques  [2]. The tweets are considered as transactions and the Named Entities as itemsets. We used R to perform these experiments, arules package and eclat algorithm. For all three datasets, we used 50 random samples and 50 weighted samples to extract FNEs and to get an average of the number of FNEs for each support value. For Obama dataset (Fig. 4 ) and Trump dataset (Fig. 5 ) the weighted sample performs better for each of the support values, providing more FNEs than the random sample. The weighted sample in La La Land (Fig. 6 ), in general, extracts more FNEs than the random sample. However, there are fluctuations in this behavior. The reason behind this event might be related to the fact that the itemsets in the random sample are dependent only to the support, while for the weighted sample, the parameters of interest play an important role as well.
Fig. 7 : Average number of itemsets for The Voice dataset Since La La Land was inferior in terms of parameters of interest and in density and coverage of Named Entities, compared to the public figures' datasets, the weighted sample is not able to make a sustainable difference. In the case of The Voice dataset (Fig. 7 ) the weighted sample is superior to the random sample. In contrast to La La Land, even though The voice has lower density and coverage, the weighted sample maintains a more stable behavior, since it is advantageous in terms of parameters of interest. Thus, we can highlight here the ability of the weighted sample to produce richer information, provided that the parameters of interest are satisfying, even though the dataset itself might be poor in terms of Named Entities. Samsung proves to be robust for different values of support in terms of FNEs discovered through the weighted sample.
Comparing the ranking of the FNEs Since we are exploring FNEs through samples, we want to guarantee that the FNEs discovered are similar to the FNEs of the population. We ran eclat algorithm on the whole datasets to discover the FNEs. As we need to compare lists of itemsets, Kendall rank correlation is helpful in identifying how similar the lists are. It takes into consideration the concordant pairs(C) and discordant pairs(D) to generate a value between -1 and 1. Concordant implies that if rank (x) > rank (y) in the list A, then rank (x) > rank (y) in list B as well. Otherwise, they are discordant pairs. The higher the Kendall value, the more similar the lists are. The Kendall coefficient is defined as: r = C -D n(n -1)/2 ( 3 ) where n is the number of pairs that are compared. Spearman's Rank Order is used as well to compute the similarities between different ranks.
Even though Kendall coefficient is more direct, as it considers the agreeing and disagreeing ranks, the Spearman coefficient tends to find the relationship between ordinal variables as in the following formula: ρ = 1 - 6 d 2 i n(n 2 -1) (4) where d is the distance between ranks and n is the number of pairs that are compared. We matched and ranked the FNEs in the population and in the sample. Then we calculated the Kendall coefficient and the Spearman rank order for both rankings. We repeated the experiment for 10 samples from Obama, Trump, La La Land, The Voice, and Samsung dataset. The average values of 10 samples of each dataset regarding Kendall and Spearman coefficient are presented in Table  4 , showing a considerable similarity between the sample and the whole population in terms of ranking of itemsets.
Exploring the reputation of an entity through the Frequent Named Entities that the dataset contains is interesting to discover. In this experiment we used Algorithm 3 to track back the tweets of the sample that represent the explored FNEs. For each FNE, we found the sentiment and calculated its reputation. In order to respect the frequency of the FNE in the sample, we weighted the reputation by the support of the FNE. In the end, we calculated an overall reputation as in: n k=1 r k * s k (5) where r k is the sentiment of the reputation of the itemset i k and s k is the support of the i k in S. We implemented this idea for Obama dataset and Trump dataset and repeated the experiment 10 times for each case (Table  5 ). Both datasets related to public figures showed a precise alignment of the reputation explored through FNEs after weighted sampling with the reputation of the whole population.
The average accuracy of the interpretation through FNEs is 90%. Nevertheless, in the case of the movie La La Land, we can distinguish a difference between both results. This misalignment comes from the fact that movies are not as dynamic as public figures, therefore, the reputation of a movie is enriched by FNEs, but not defined by them. We can also explain this result with the lower coverage and density of NE in La La Land compared to Obama and Trump (Table  2 ). Moreover, since the parameters of interest are the lowest compared to the other datasets, the weighted sample cannot exploit a lot of behavior from the dataset. The Voice is an interesting case, as since it is a TV show, it is expected to behave as La La Land. Even though it is inferior in density and coverage of Named Entities, it manages to round up the reputation of the entity from its Frequent Named Entities almost precisely.
The advantage of The voice lies in the fact that the weighted sample is more powerful, due to the fact that the parameters of interest are considerably better than La La Land. Samsung reveals new insights regarding Named Entities. Samsung resembles La La Land in terms of parameters of interest; it has low retweets and favorite count, but a high number of followers. Moreover, in terms of density of NEs and Coverage of NEs, Samsung is rich, comparable to the people datasets of Trump and Obama. As a result, Samsung manages to have a good interpretation of reputation regarding the whole dataset. In the case of La La Land , through Frequent Named Entities it is possible to discover dominating opinions that bias the dataset. For instance, in all of our 10 samples, the first FNE was related Emma Stone and JAEBUM and had a reputation of (+100, -0).
Emma Stone has held a picture of JAEBUM as a gesture of appreciation and this event has gone viral on Twitter. As a result, it dominated the dataset in a positive way and affects the whole reputation. Emma Stone is the main actress in La La Land , that is the reason why this event is part of La La Land dataset. However, this event is not related to the movie. With the help of the itemset mining, viral events that are not relevant can be distinguished and discarded from the aggregation. To conclude, rich tweets in Named Entities are able to interpret better the reputation of target entities. When the tweets have a considerably low coverage and density of NEs, then the weighted sample provides a better sample to discover the reputation. This is the case of La La Land in our experiments, which is able to overcome the problem of low density and coverage of NEs through the weighted sample.
Conclusions We addressed the problem of reputation discovery and aggregation of sentiments by exploring the underlying entities that co-exist in the data. We stressed the importance of information interpretation in explaining the reputation of an entity. We introduced a weighted sampling technique to improve the richness of the dataset. We evaluated our approach comparing random and weighted sample in terms of statistics of indicators and we tested the power of Frequent Named Entity Mining on reputation discovery. Our proposed weighted sampling technique proved to have an advantage over the random sample. We showed that our approach proves to be generally robust to the type of the entity of interest. In the case of entities that have low values of coverage and density of FNEs like La La Land and The Voice , the weighted sampling helps in improving the reputation discovery.
Dataset characteristics related to NE characteristics, tweets regarding public figures come from popular users, and they are considerably retweeted and liked. Tweets regarding La La Land are less influential in terms of parameters of interest. For instance, the average favorites of Trump and Obama are eight times more than the average favorites of La La Land and Samsung. The same argument goes for the retweets, La La Land and Samsung has almost half the retweets of Trump and almost one-third of the retweets of Obama. Moreover, La La Land has a moderate density and low coverage of Named Entities. Samsung ,on the contrary, has the highest number of followers compared to other datasets. This could be due to the fact that the community who follows technology is highly likely to be active on Twitter and excited about the brand and wants to follow the news.
Furthermore, we could imagine that such an automatic method could allow for a systematic extraction of contacts localisations on massive retrospective datasets, in order to feed a deep learning algorithm in the context of the preoperative planning of SEEG implantation. Current approaches for preoperative planning of electrodes trajectories  [7] [8] [9]  use conventional methods, and it could be interesting to compare them with machine learning methods if the number of examples is sufficient, which would require a huge work of post-operative contacts detection. Regarding the accuracy, the principal axes of the automatically segmented electrodes are almost superimposed for the majority of electrodes as shown in Table  2. This promising result further confirms that the automatic method seems to approximate the reality quite well.
Results: We combined two comprehensive manually curated network maps. Atlas of Cancer Signalling Network (ACSN), containing mechanisms frequently implicated in cancer; and ReconMap 2. 0, a comprehensive reconstruction of human metabolic network. We linked ACSN and ReconMap 2. 0 maps via common players and represented the two maps as interconnected layers using the NaviCell platform for maps exploration. In addition, proteins catalysing metabolic reactions in ReconMap 2. 0 were not previously visually represented on the map canvas. This precluded visualisation of omics data in the context of ReconMap 2. 0. We suggested a solution for displaying protein nodes on the ReconMap 2. 0 map in the vicinity of the corresponding reaction or process nodes. This permits multi-omics data visualisation in the context of both map layers.
Exploration and shuttling between the two map layers is possible using Google Maps-like features of NaviCell. The integrated ACSN-ReconMap 2. 0 resource is accessible online and allows data visualisation through various modes such as markers, heat maps, bar-plots, glyphs and map staining. The integrated resource was applied for comparison of immunoreactive and proliferative ovarian cancer subtypes using transcriptomic, copy number and mutation multi-omics data. A certain number of metabolic and signalling processes specifically deregulated in each of the ovarian cancer sub-types were identified.
Much of produced high-throughput molecular data in many medical and biological applications remain under-explored due to the lack of insightful methods for data representation in the context of formally represented biological knowledge. Carefully designed maps of complex molecular mechanisms such as the whole-cell reconstructions of human metabolism in ReconMap 2. 0  [1]   [2]  or the global reconstruction of cell signalling of cancer in ACSN  [3]  potentially provide ways to better exploit existing and new multi-omics data, by overlaying it on top of large molecular maps. ACSN is a resource and a web-based environment that contains a collection of interconnected signalling network maps (https://acsn. curie. fr). Cell signalling mechanisms are depicted on the maps at the level of biochemical interactions, forming a large network of 4600 reactions covering 1821 proteins and 564 genes and connecting several major cellular processes  [3].
ACSN is composed of 5 interconnected maps of major biological processes implicated in cancer. The maps are further divided into functional modules that represent signalling pathways collectively responsible for the execution of a particular process. In total, there are 52 functional modules in the ACSN resource (See Table  1  for terms definition). Each of these modules can be visualised in the context of the global ACSN map or accessed as individual maps. The Atlas is a "geographic-like" interactive "world map" of molecular interactions. ACSN is supported by NaviCell platform for easy map navigation and its annotations using Google maps™ engine. The logic of navigation as scrolling and zooming; features as markers, pop-up bubbles and zoom bar are adapted from the Google map. Finally, NaviCell includes a powerful module for data visualisation.
The integrated networks can be provided as interconnected layers supported by NaviCell platform for navigation and data integration. The suggested methodology is applied for ACSN and ReconMap 2. 0 resources integration. However, this is a generic method applicable for integration of different types of networks prepared in CellDesigner SBML format. In the following sections of the paper, we explain the challenges and describe how each step mentioned in the workflow was addressed. The workflow in the Section 2 includes the following major steps (see Table  1  for terms definition): -Identification of common proteins between ACSN and ReconMap 2. 0 networks -Finding metabolic and molecular processes crosstalk between ACSN and ReconMap 2. 0 -Displaying proteins nodes on the ReconMap 2. 0 map -ACSN-ReconMap 2.
Finding metabolic and molecular processes crosstalk between ACSN and ReconMap 2. 0 ACSN and ReconMap 2. 0 networks have a particular hierarchical structure. ACSN is divided into functional modules, whereas ReconMap 2. 0 is divided into subsystems. Each of these structures are a subset of processes from the global network, implicated in regulation and execution of a specific molecular or metabolic pathway respectively (See Table  1  for terms definition). To address the question which metabolic processes are connected to which signalling mechanisms, the enrichment analysis of ACSN modules and Recon 2. 0 subsystems was performed using the 252 common proteins (Additional file 1). The composition of ACSN modules and ReconMap 2. 0 subsystems are provided as gene sets in Additional files 2 and 3 in gmt format and the enrichment was calculated using a Hypergeometric Test.
The analysis demonstrated that proteins shared between the two maps are implicated in 22 modules of ACSN and in 51 subsystems of ReconMap 2. 0 (Figure  1  and Table  2 ). Fructose and Mannose metabolism, Glycolysis and Gluconeogenesis. Finally, the subsystem Inositol Phosphate Metabolism is enriched by the shared proteins (Figure  1  and Table  2 ). The same trend is observed at the level of the reactions regulation, finding proteins from Apoptosis, Cell cycle and Mitochondrial processes implicated in catalysis of a large number of reactions in the ReconMap 2. 0 is expected as these pathways are known to be closely related to the metabolism. However, it is interesting to note that some less intuitive connections between ReconMap 2. 0 subsystems and ACSN modules were retrieved. For example, proteins implicated in cell survival modules of ACSN as Hedgehog, MapK, PI3K-AKT-mTOR and WNT regulate reactions in the Inositol phosphate metabolism subsystem from ReconMap 2.
0. In addition, the proteins from cell migration-related and epithelial-to-mesenchymal transition (EMT)-related processes we involved in the regulation on the reactions in five different phospholipids and amino acid metabolic pathways, indicating most probably that the invasion process requires very active metabolism in migrating cancer cells. These connections are less obvious and may help to highlight interesting connections between signalling and metabolic processes in cancer (Additional file 4). By extracting information about crosstalk between ACSN modules and ReconMap 2. 0 subsystems, it was possible to generate a network where nodes represent ACSN modules and ReconMap 2. 0 subsystems connected by edges if they shared common proteins (Figure  2 , Table  2  and Additional File 5). The obtained network contains one large connected component and also a number of modules and subsystems that are not connected to each other.
The interconnections between many signalling and metabolic processes on the network form communities. It was possible to identify three major communities each containing ACSN modules interconnected with ReconMap 2. 0 subsystems, that we called 'Apoptosis and Mitochondrial Metabolism', 'Cell Cycle' and 'Cell Survival and Motility' according to their biological functions (Figure  2 ). The nodes Mitochondria Metabolism and Apoptosis Genes, two modules of ACSN, are shown to be enriched with common proteins and connected to 36 and 24 subsystems of ReconMap 2. 0 respectively. A total of 20 metabolic subsystems were found grouped forming a big community related to 'Apoptosis and Mitochondrial Metabolism' This result is not surprising since within the mitochondrion we find main cellular pathways such as Citric acid cycle, Oxidative phosphorylation and Fatty acid oxidation.
Conversion of BiNoM Reaction Format into a CellDesigner map Using a custom python script, information stored in the BiNoM Reaction Format is transformed into a XML file following the SBML format. This file will contain each protein names, IDs, alias IDs, coordinates and type. As for now, only the manipulation of simple proteins is available. -Merging of the ReconMap 2. 0 and Proteins maps using BiNoM merging function. Once the file containing proteins to add to the metabolic map is obtained, as they both are in the same SBML format, it is possible to merge them by using a function of the BiNoM plugin. This function allows transforming two or more separated maps into one unique map. This final merged map will be transformed into the NaviCell environment using the NaviCell Factory package (https://github.
ACSN and ReconMap 2. 0merging Once the protein positions file has been generated, it was converted to a CellDesigner  [19] [20] XML format through a custom python script (https://github. com/sysbiocurie/CellDesigner_networks_map_integration_procedure). This script allows to obtain a file in XML format following the standard of CellDesigner's SBML. This 'map' contains only proteins in the positions they should belong on the final metabolic map. This file was then merged with the ReconMap 2. 0 network by using an existing merging function of BiNoM  [21][22]  to obtain the final network containing the original ReconMap 2. 0 as well as the proteins in the vicinity of reactions they catalyse.
NaviCell representation of ACNS-ReconMap 2. 0 resource Due to their corresponding size and technical limitations, the two maps couldn't be merged into one single seamless map. The cross-linking via shared proteins was performed and the two maps were represented as interconnected layers using NaviCell web-based platform, allowing to shuttle between the maps by clicking on a common entity (see next paragraph). Both maps were preserved with their original layout so that their correspondent relevance of the visual organisation was not lost. Furthermore, this allows users to have an easier view and understanding of the whole system. Moreover, entities annotations from ReconMap 2. 0 have been recuperated from the original map and transferring to a NaviCell annotation format (see Materials and Methods), allowing to link entities to corresponding databases (Figure  5 ).