dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2002; Piehler, 2005; Browne et al. , 2010; Ngounou Wetie et al. , 2013). In response to these challenges, computational methods have emerged, showing promising results in terms of performance to integrate functional (i. e. , same biochemical reaction) and physical interactions. A physical interaction describes a physical contact between proteins, as a result of biochemical events steered by interactions including electrostatic forces, hydrogen bonding, and the hydrophobic effect  (Berne, Weeks and Zhou, 2009; Nitzan, Casadiego and Timme, 2017). These computational methods allow a more specific identification of interactions than experimental prediction methods  (Droit, Poirier and Hunter, 2005; Shoemaker and Panchenko, 2007; Zhou, Li and Wang, 2016). Although PPIs from computational methods provide a better prediction of physical interactions, PPI databases contain a few false positive interactions  (Peng et al.
Feature vectors of a protein pair will be the input of the SVM. The closer these feature vectors of a pair of proteins are to to hyperplane, the higher the probability of an interaction between these proteins. Dunham and Ganapathiraju, (2021)  benchmarked different PPI prediction algorithms, and show how well they perform on realistically proportioned datasets. Based on verified interactions and a known false interaction rate, 16 datasets using the SVM method are generated. Accuracy values ranged from 51 to 96%, which highlights false interactions predicted or not predicted by the SVM methods.
In PPI prediction, each observation is represented by a vector Z (X 1 ; X 2 ;X 3 ;. ; X m ,Y), where X{X1,X 2, X 3,. ,X m } is the m-dimensional input variable and Y is the output variable taking {0,1}. As input, this method can take either protein interaction datasets or genomic interaction datasets  (Jansen et al. , 2003; Alashwal, Deris and Othman, 2009; Lin et al. , 2021). In the end, the classifier gives a binary response, a zero indicating the interaction is not verified, and a one when there is a potential interaction. Geng et al. (2015)  adopted naive Bayes classification to predict site interactions between two proteins. Each pair of proteins is split into several residues, with two residues of two proteins in the same cluster interacting. In terms of performance, they achieved an accuracy value of 60%, which is generally lower than those of the SVM and ANN methods, due to the difference in the amount of information available on the proteins  (Ahmed, 2020; Jonathan et al.
The phylogenetic profile is a comparative genomic method that predicts the large-scale biological molecule function through evolution information  (Mikkelsen, Galagan and Mesirov, 2005). Liu et al. (2018)  resorted to hot spot residues databases and in particular the Alanine Thermodynamic Scanning Database. Hot spot residues are functional sites in protein interaction interfaces, and these sites allow the understanding of the type of interactions and are highly conserved in proteins to ensure the functions. Itraq (K. Wang et al. , 2018a)  used protein sequences as input and hierarchical clustering to identify age-related biomarkers of dental caries. Protein interactions were then successfully validated by multiple reaction control mass spectrometry. Each of these two clustering methods has sub-methods.
, 2019; Balogh et al. , 2022) , but machine learning requires large computational resources and large datasets of good quality  (Hashemifar et al. , 2018; Y. Wang et al. , 2018b). Machine learning-based approaches are approaches that will be scalable in different domains, these approaches offer very promising results  (Casadio, Martelli and Savojardo, 2022; Huang et al. , 2022; Pan et al. , 2022). However, as we have seen in the articles, many sequences or interactions are necessary to train the model  (Li M. et al, 2022; Hu et al. , 2022; Jha, Saha and Singh, 2022). So, these approaches will be preferred for large-scale omics approaches, prediction of new interactions, or identification of clusters or hubs (protein with many interactions)  (Pei et al. , 2021; Song et al. , 2022; Stringer et al.
Methods based on text mining Text mining is a technique for exploring and transforming unstructured text into structured data (e. g. , tables). In PPI prediction, text mining allowed to extracting information about proteins and their interactions from scientific studies and reference databases. Text mining techniques try to automate the extraction of sentence-related proteins from abstracts or paragraphs of text corpora  (Papanikolaou et al. , 2015). Several text mining methods exist, some are based on statistical matches between gene names, protein names in public repositories, and online resources. Links and types of interactions between proteins are defined by action verbs, for example, interact, interfering, and reacting. He, Wang and Li (2009)  benefited from this technique through the PPI finder tool that was developed to extract human PPIs from PubMed abstracts based on their co-occurrences and interaction words, the retrieved interactions are then validated by the occurrence of Gene Ontology (GO) terms.
Horizontal integration aimed to create a PPI network from different PPI databases for many interactions  (Hibbs et al. , 2007; Subramanian et al. , 2020) , whereas vertical integration will assemble information from different omics (genomics, proteomics, metabolomics, etc. ) databases for a given interaction  (Wang and Jin, 2017; Ulfenborg, 2019; Das et al. , 2020; Welch et al. , 2021). All interactions can be modeled into a multi-layered graph structure  (Kinsley et al. , 2020)  where each layer represents a network associated with omic-specific information  (Hammoud and Kramer, 2020). PPI networks are a central layer in the multi-omics integration process  (Mosca and Milanesi, 2013; Hammoud and Kramer, 2020; Dugourd, Christoph Kuppe and Marco Sciacovelli, 2021)  (Figure  1 ). Horizontal and vertical integration took advantage of topological properties of the network to facilitate construction of different interactomes, to improve classification and evaluation of a PPI  (Peng et al.
Horizontal integration of a PPI network Horizontal integration is a solution to eliminate these false interactions and allows to find missing data, thus adjusting the resulting confidence score  (Everson et al. , 2019; Gebreyesus et al. , 2022). Horizontal integration methods have contributed to development of various types of databases based on organismspecific diseases, biological processes, and detection methods, such as the Integrated Interactions Database (IID)  (Kotlyar et al. , 2019) , IntAct  (Hermjakob et al. , 2004a) , and StringDB  (Szklarczyk et al. , 2019). PPI is usually redundant in different databases. A PPI found in one database may also be found in others such as BioGRID  (Oughtred et al. , 2021)  or Reactome  (Gillespie et al. , 2022). This communication between the different databases corresponds to horizontal data integration (Zitnik and Leskovec, 2017;  Cowman et al.
, 2020). Assembly and merging are the main algorithms for horizontal integrations (De Las Rivas, Alonso-López and Arroyo, 2018;  Amanatidou and Dedoussis, 2021). Two PPI networks are assembled by alignment algorithms. Alignment of PPI networks aimed at finding topological and functional similarities between different PPI networks  (Kazemi et al. , 2016; Ma and Liao, 2020). In a first step, the alignment algorithm looks for overlapping regions in two networks. These regions form clusters that will be assembled to make a FIGURE 1 Workflow of key steps to design a PPI network assembly. PPI networks can be integrated horizontally and/or vertically. Horizontal integration creates a PPI network by concatenating interaction information from different PPI databases (here networks 1 and 2 represent two PPI networks from two different databases), while vertical integration gathered information from different omics databases for a given interaction.
Xu et al. (2018)  drawled on these propagation methods to reconstruct a multi-level PPI network and identify protein complexes. Through these different network alignment algorithms, many PPI databases have been updated or created. The most exploited are BioGRID  (Oughtred et al. , 2021) , IntAct  (Hermjakob et al. , 2004b) , String  (Szklarczyk et al. , 2019) , and UniprotKB (The UniProt Consortium, 2019). A large set of databases is referenced in startbioinfo. org (Kshitish et al. , 2013)  and pathguide. org (Bader, Cary and Sander, 2006). Following the revolution in NGS technology and the increase in PPI datasets, the integration of a single cell with PPI networks is showing promising results. Indeed, the singlecell method coupled PPI network will allow the understanding of gene regulation, cellular heterogeneity  (Cha and Lee, 2020) , tissue-specific networks, identification of ligand-receptor interactions, functional interactions, and cell-cell communication  (Armingol et al.
Scores based on the literature Acts as an orthogonal validation and analyzed how often a PPI is cited in publications. The main problem with implementing this method is the application of thresholds, so that only interactions with a sufficiently high score are retained  (Bozhilova et al. , 2019). Well-studied proteins will have a greater number of interactions and associated publications than proteins that are new or have little information. Hence, thresholds need to be standardized. In order to normalize thresholds among different databases, the MI-score method was created  (Villaveces et al. , 2015a). This method allows to merge data from different databases that are in the PSI-MI(Proteomics Standards Initiative-Molecular Interaction) format  (Hermjakob et al. , 2004a; Bader, et al. , 2006; Kerrien et al.
Apart from these three distinct categories, to measure the confidence of PPIs, robust measures resulting from data provenance and network topology are needed, such as the average redundancy difference between various sources, natural connectivity of the PPI network as well as the number of edges in a protein-centered sub-arrays (ego networks)  (Bozhilova et al. , 2019; Wang et al. , 2019). The main problem with all these methods is that a score is mainly specific to one database, so threshold values are highly database dependent  (Kamburov et al. , 2012; Dahiya et al. , 2019; Xu et al. , 2019). To address this issue, consensus networks appeared such as HugGan  (Huang et al. , 2022)  which is a tool that gathers 31 data sources using deep learning approaches to keep only interactions with a high confidence score resulting in a network with high coverage and quality.
, 2011; Deng, Xu and Wang, 2019) , understanding protein clusters linked to cellular function  (Zhao, Wang and Wu, 2017; Amanatidou and Dedoussis, 2021) , understanding mechanisms of action  (Jia et al. , 2021; Yuan et al. , 2021) , and drug repositioning  (Lee and Yoon, 2018; Soleimani Zakeri, Pashazadeh and MotieGhader, 2021). Larger and complex networks are more difficult to visualize. This is the case of the most popular source offering a representation of PPI networks such as StringDB  (Szklarczyk et al. , 2019). This online database is intended for the inspection of small networks or sub-networks (less than 500 interactions). Therefore, because of their size and topology, the PPI network requires specialized tools  (Bosque et al. , 2014; Freilich et al. , 2018; Aihaiti et al. , 2021).
The methods for visualizing a network can be divided into three categories (Table  2 ). The methods can be combined to take advantage of each of the benefits of these categories. This is the case with cyNeo4j  (Summer et al. , 2015)  which combines Cytoscape  (Otasek et al. , 2019)  and Neo4j  (Gong et al. , 2018)  for fast visualization of large networks based on a graph-oriented database. Cytoscape is the most widely used tool for the visualization of large networks  (Shannon et al. , 2003). Other visualization systems do not fit into these categories and are based on web-based visualization interfaces and on a relational database  (Salazar-Ciudad and Jernvall, 2013; Salazar et al. , 2014; Hayashi et al. , 2018). This is the case of the PINA 3. 0  (Du et al. , 2021)  tool, which is a consensus database containing five interactomes and offering a web visualization service allowing the identification of interacting protein pairs in different cancer types.
The weaknesses of these methods are the size of the networks, the execution time of a query, and their limited applicability  (Jeanquartier, Jean-Quartier and Holzinger, 2015; Zhou and Xia, 2018; Perlasca et al. , 2020). Visualization tools are evaluated by four criteria: compatibility (available on which OS (operating systems): Windows, Mac Os, and Linux, analytic functions (presence of functions measuring the topological properties of the network, weak interactions of external data, etc. ), visualizations (graph layout, dynamics, and parallel implementation), and the extensibility of the tool (addition of plugins, type of input, and output file) forming distinct classes  (Sanz-Pamplona et al. , 2012; Agapito, Guzzi and Cannataro, 2013; Dallago et al. , 2020). In the context of biological network analysis and in particular protein networks, one of the essential criteria is dynamic visualization tools  (Xia, Benner and Hancock, 2014; Zhou and Xia, 2018).
Tool Advantage Disadvantage Visualization through downloadable tools Cytoscape  (Otasek et al. , 2019) , Gephi  (Bastian, Heymann and Jacomy, 2009) , Tulip  (Auber et al. , 2017) , Graphviz  (Ellson et al. , 2001) , Pajek  (Mrvar and Batagelj, 2016)  Many add-on features, flexibility for network analysis, easy to handle, open source and free Difficult to set up automation interface, working with big networks requires big memory and computing power Visualization by libraries integrated with languages Igraph  (Csárdi and Nepusz, 2006) , NetworkX  (Hagberg et al. , 2008 ), graph-tool (Peixoto Tiago, 2014) , NetView  (Neuditschko, Khatkar and Raadsma, 2012)  Open source and free, well documented, accessible, import and export graphs easily, easy to implement Graphic possibilities are limited, restricted number of nodes In order to overcome the limitation of network size and consider the dynamics of the networks, several tools have been developed over the last decades  (Sanz-Pamplona et al.
, 2012; Winkler et al. , 2021). The success of Cytoscape is due to the large number of plugins/features that can be added directly from the tool  (Saito et al. , 2012; Lotia et al. , 2013). The calculation of overrepresented GO terms in a network can be performed by Bingo  (Maere, Heymans and Kuiper, 2005) , a widely downloaded Cystoscape plugin. Through Cytoscape, we also find plugins allowing the understanding of the dynamic organization of biological networks such as TVNViewer  (Curtis et al. , 2011) , KDDN  (Tian et al. , 2015) , and Dynetviewer. Another downloadable software offering a visual representation of PPI networks is the Gephi  (Bastian, Heymann and Jacomy, 2009). Downloadable network visualization tools have difficulties with the implementation of data  (Villaveces et al. , 2015b; Li et al.
, 2016). Visualization libraries such as igraph  (Csárdi and Nepusz, 2006)  and NetworkX  (Hagberg et al. , 2008)  will make it easier to import and export networks but are limited in terms of adding new functionality and graphic possibilities  (Pandey, 2018; L. Wu et al. , 2022a). Network visualization tools are specific to the detection method  (Ashtiani et al. , 2018). HPIminer  (Subramani et al. , 2015)  extracts information from human PPIs and PPI pairs in biomedical literature and provides a visualization of interactions, networks, and associated pathways using two databases, namely, HPRD  (Goel et al. , 2012)  and KEGG  (Kanehisa et al. , 2016). Another area of improvement for online or general-purpose visualization tools and libraries is the addition of a visualization engine or search engine  (Chisanga et al.
, 2017). Tools integrating visualization engines such as NAViGaTOR  (Brown et al. , 2009)  and MIST  (Hu et al. , 2018)  have been developed. These tools allow the acceleration of the visualization of large PPI networks  (Yu and Zhang, 2008; Gerasch et al. , 2014; Zaki and Tennakoon, 2017). It is also possible to improve the speed of visualizations by connecting directly to graph databases such as Neo4j  (Gong et al. , 2018, p. 4)  and ArangoDB  (Touré et al. , 2016; Timón-Reina, Rincón and Martínez-Tomás, 2021; ArangoDB NoSQL Multi-Model Database: Graph, Document, Key/Value, 2022). Since graph databases store data directly in a graph form, they are becoming a preferred resource for storing complex relationships of heterogeneous biological data  (Yoon, Kim and Kim, 2017; Jupe et al. , 2018; Castillo-Arnemann et al.
, 2021). Flexibility of multi-omics integration offered by graph databases facilitates data mining to support different hypotheses  (Lysenko et al. , 2016; Brandizi et al. , 2018; Wandy and Daly, 2021). All these tools for the visualization of PPI networks are based on different visualization algorithms  (Koutrouli et al. , 2020; Sandoval and Orlando, 2021). Visualization algorithms can be based on simplistic approaches such as adjacent matrices  (Fekete, 2009) , circular layouts  (Suderman and Hallett, 2007) , or complex approaches such as force-directed algorithms  (Liu et al. , 2021). The main differences between simple and complex algorithms for visualization depend on the size of the network, the topology of the network, and the dimensionality of the information  (Heberle et al. , 2017; Becker et al.
Summary and outlook In this review, different computational strategies for predicting PPI, from integration to visualization to methods for validating interactions have been studied. Many computational prediction approaches rely on experimental methods to predict a PPI interaction  (Rao et al. , 2014; Peng et al. , 2017; Ding and Kihara, 2018; Tanwar and George Priya Doss, 2018). Although this increases the coverage of the network, it can disrupt the horizontal integration process  (Browne et al. , 2010; Ngounou Wetie et al. , 2013). Sets of PPI interactions from different datasets are constructed and transformed independently, which can lead to information gaps, redundant information, and poor identifier compatibility when aligning two PPI networks. Ideally, at any point in the overall integration process (including vertical and horizontal), each omics data set should be evaluated in the context of the other datasets, so that complementary information can be fully exploited, and added information can be identified  (Bozhilova et al.
, 2019; Bajpai et al. , 2020). Implementation of validation scores based on topological properties allows to limit the redundancy of edges and will allow to filter the PPI network  (Pietrosemoli and Dobay, 2018; Sardiu et al. , 2019). Information redundancy is the repetition of information without adding additional information in different databases. The increase in omics data and PPI integration methods has contributed to the growth of many PPI databases. However, this increase in the number of databases increases the redundancy of information, making it difficult for the user to choose a PPI database  (Rabbani et al. , 2018; Hawe, Theis and Heinig, 2019; Zahiri et al. , 2020). In addition, information redundancy slows down the calculation time for the construction and visualization of networks  (Chen et al.
-H. Liu et al. , 2020b). The visualization of networks is a problematic issue for networks and especially for PPI networks  (Du et al. , 2021). Visualization tools depend mainly on the size of our networks  (Summer et al. , 2015; Zou et al. , 2017). Currently, multilayer network visualization is limited to small networks and requires a consequent preformatting of the data  (Smith-Aguilar et al. , 2019; Hammoud and Kramer, 2020; Sebestyén, Domokos and Abonyi, 2020). The study of multilayer networks based on the PPI network is constantly evolving and will become more powerful with advancement of more powerful mathematical models offering better predictions  (Kapadia et al. , 2019; Karatzas et al. , 2021; Cuenca et al. , 2022). Different perspectives on the integration of PPI networks can be imagined.
The visualization of multilayer multi-omics networks and creation of consensus networks for each omics dimension to understanding new mechanisms of multi-omics integration. A consensus network is the result of the horizontal integration of different databases  (Berto et al. , 2016; Mosca et al. , 2021). Through this network, it will be possible to homogenize the different thresholds of the different databases and to eliminate the recurrence of information  (Leblanc et al. , 2013; Affeldt et al. , 2016; Zohra Smaili et al. , 2021). Recently,  Woo and Yoon (2021)  created a Monaco aligner that can find multiple alignments with high accuracy to identify functional modules. In the era of big data and NGS (next generating sequencing) technologies, it is difficult to know which information is needed to build a PPI network.
, 2021; Hütter et al. , 2022). Moreover, the notable advances in the prediction of the structure of proteins from their sequence in amino acids with alphafold  (Jumper et al. , 2021) , which could lead to a revolution in the PPI prediction algorithm. In view of the generous size of PPI networks, visualization tools focus on specific networks, including Mechnetor  (González-Sánchez et al. , 2021) , a tool for visualization of biological mechanisms. At the moment, there are no tools available to visualize the interactome protein specific to a tissue, but there are different databases on this subject  (Islam et al. , 2013; Basha et al. , 2018).
Summary table of tool for visualizing of protein-protein interaction network. Visualization methods to analyze network are grouped into three distinct categories: visualization through downloadable tools, visualization by libraries integrated with languages, and visualization through graph-oriented databases. The user has to choose his tools according to his study context. For analysis of high dimensional data containing a large amount of information, it is advisable to manipulate tools based on graph databases. Conversely, if the user wants to have a quick representation, we recommend the user to turn more to visualization libraries or downloadable software.
The mathematical model is implemented into the scientific open-source framework OpenGeoSys. We compared the model calculations with the measured long-term heave records at the study site. We then designed a sensitivity analysis to achieve a deeper insight into the swelling phenomena. The synthetic database obtained from the sensitivity analysis was used to develop a machine learning (ML) model, namely least-squares boosting ensemble (LSBoost) model coupled with a Bayesian optimization algorithm to rank the importance of parameters controlling the swelling. The HM model reproduced the heave observed at Staufen with sufficient accuracy, from a practical point of view. The ML model showed that the maximum swelling pressure is the most important parameter controlling the swelling. The other influential parameters rank as Young's modulus, Poisson's ratio, overburden thickness, and the initial volumetric water content of the swelling layer.
2017; Schädlich et al. 2013; Wittke 2014). Although these models successfully simulated the swelling deformations, their generalization capability remains limited because of the existing gaps in the overall understanding of the swelling process  (Butscher et al. 2016 (Butscher et al. , 2018)). A lack of knowledge still exists with respect to coupling between hydraulic, mechanical, and chemical processes due to a lack of long-term experiments that would deliver a database for the development of a coupled model  (Wanninger 2020). Machine learning (ML) has been successfully applied for a wide range of problems in the field of geotechnical engineering. ML models are non-parametric statistical tools that do not require any pre-assumptions between inputs and outputs and are capable of handling complex nonlinear input-output relationships.