dataesr/scientific-paragraphs-categorization huggingface dataset:
DATA COLLECTION AND MANUAL CURATION Our data collection relied on the text mining of AAg-related PubMed abstracts. Bio-entity recognition and extraction from these abstracts for human AAg candidates were performed by our custom developed ontology-based bio-entity recognizer. After evaluation against the CRAFT corpus for gene/protein recognition based on Protein Ontology (PR)  (20, 21) , the F-measure, the point where precision and recall are equal  (23) , of our recognition tool is 0. 845, which is on a par with that of current state-of-the-art biomedical annotation system, BeCAS, 0. 76  (24). We compiled a list of human AAgs together with their related diseases as well as the evidence from PubMed abstract using the following three steps. All PubMed abstracts were fetched through the NCBI E-utilities API  (25)  and the AAg-related abstracts were extracted by our bio-entity recognizer with the keywords of either 'autoantigen' or 'autoantibody' or their lexical variants like 'auto-antigen', 'auto antigen', 'autoantigens',' auto-antigens', 'auto antigens', 'auto-antibody', 'auto antibody', 'autoantibodies', 'auto-antibodies' or 'autoantibodies'.
45,830 AAg-related abstracts and 94,313 sentences were obtained. Then, a dictionary of human gene/protein names and synonyms was built by integrating all terms and their cor-respondent synonyms from the Protein Ontology. These were mapped to all the official symbols of human genes provide by HUGO Gene Nomenclature Committee (www. genenames. org/) (26). The human genes that were recognized and extracted from the previously generated set of sentences were considered as candidate AAgs. All 3984 candidates from 25 520 original abstracts and 43 253 evidence sentences in which the gene and keyword co-occurred were validated through three rounds of manual curation. First, all sentences with AAg names were checked and selected by two experienced researchers independently; second, these selected sentences were submitted to an internal review, in which all AAg names were manually reviewed and approved one by one by a reviewer panel consisting of three experts; third, we asked all co-authors to randomly check AAgs from our website to make sure that all genes loaded into our database are bona fide AAgs.
Disease terms were extracted from the abstracts by our recognition tool based on the Human Disease Ontology  (22, 27). The logic relation type between child disease term node and parent node in the ontology has been taken into consideration as in NCBO Annotator (28), i. e. if a child disease term is associated with an AAg, then its Human Disease Ontology parent disease terms are supposed to be associated as well. Associations between AAg genes/proteins and human disease terms were generated by sentence-level co-occurrence and scored by co-occurrence count. All extracted AAg genes/proteins, human disease terms as well as their associations and evidence abstracts with biomedical keywords highlighted were stored in the our database for user query and navigation (Figure  2A ). We obtained 1126 human AAgs and 1071 related diseases and constructed the human AAg database (AAgAtlas 1.
0 database) (Figures  2 3 4 ). All evidence sentences that have been manually curated for AAgs are regarded as validated evidence and included on the top of each AAg page (Supplementary Figure  S1 ). Considering all AAgs and their related diseases were documented from previous publications that might not be fully validated, we added a simple community curation function to the evidence phrases with which the users can easily provide their feedback by simply clicking the green 'Yes' or red 'No' button after login as registered users (Supplementary Figure  S1  and Figure  S2 ). With this function, the users can confirm or reject that the evidence phrase has the correct AAg and related disease.
DATA SEARCH AND NAVIGATION We provide a user-friendly web interface that facilitates searching, exploring information and updating the AAgAtlas 1. 0 database (http://biokb. ncpsb. org/aagatlas/) (Figure  4 ). The website interface comprises six sections including 'Home', 'Browse & Download', 'FAQ', 'Contact', 'Feedback' and 'Log in'. From the 'Home' page, the users can see the introduction to AAgAtlas 1. 0 database and search for their gene or disease of interest. By selecting 'Browse & Download' in the navigation bar, the complete list of human AAgs, their related diseases and supporting evidence can be browsed and downloaded in one page. From the 'FAQ' page, the users can see the general questions and answers. From the 'Feedback' page, the users can submit new genes to our database manually.
Query by gene symbol For the query by gene symbol, the users can enter an NCBI Entrez gene symbol in the 'Gene Symbol' search box, and a drop down menu will provide auto-completed gene symbols present in the AAgAtlas (Supplementary Figure  S1 ). After selecting one of them and clicking the 'Search' button, the search engine will return the queried results containing a table showing the gene name, related diseases as well as the supporting literature evidence. By clicking the hyperlink of the gene symbol for an individual AAg in the 'Gene' column, users can obtain the basic information for the AAg gene and the cross references to external databases (i. e. Ensembl  (29) , NCBI Gene  (30) , UniProtKB  (31) , neXtProt  (32)  and Antibodypedia (33)). For example, p53 is perhaps one of the most famous AAg whose antibodies are frequently up-regulated in serum or plasma of cancer patients  (34).
Query by disease term For the query by disease term, the user can enter a disease term in the 'Disease Term' search box, and a drop down menu will provide disease terms that were extracted From the 'Home' page, the users can see the introduction of AAgAtlas 1. 0 database and search for the gene or disease of their interest. From the 'Browse & Download', the users can see and download the human AAgs, their related diseases and supporting evidences. The 'FAQ' page shows the general questions and answers. The 'Contact' page shows the emails of web managers that are used for further communications. With the 'Feedback' page, the users can submit new genes to our database manually. From the 'Log in' page, the users can make the registration and start do the manual curation on our website. Our database will be updated periodically according to these information provided by the users.
The users can click the hyperlink of evidence to see the original abstract in which the key words, i. e. gene and disease names, are also highlighted. For example, as the sixth most common cancer and the second leading cause of cancer death in the world, liver cancer is closely associated with the body's immune system that could either evacuate the abnormally growing cells or attack healthy liver cells causing inflammation and liver damage  (35, 36). We searched our database with 'Liver cancer' D774 Nucleic Acids Research, 2017, Vol. 45, Database issue and found 53 AAg genes. The analysis with Reactome (http: //www. reactome. org/) (37) reveals that these genes participate in the DNA repair, signal transduction, gene expression, immune system, metabolism of proteins, cell cycle, programmed cell death and cellular responses to stress (Figure  5A , Supplementary Table  S2  and S3 ).
DISCUSSION AND CONCLUSION Since the first AAg paper appeared in 1945, more than 120 000 papers have been published and 1000 AAgs have been identified. Some of them have been shown to be important regulators or biomarkers in both healthy people and patients of different diseases. Reports of these AAgs occur in thousands of paper scattered through the literature (Figure  1 ) and construction of a systematic database for AAgs would be valuable to study the generation, evolution and function of AAb as well as to elucidate the mechanism of our human immune system in maintaining homeostasis and evacuating abnormal growing cells. In this work, we searched and extracted the abstracts of all publications from Pubmed with 'Autoantibody', 'Autoantigen' and their synonyms using text mining and a self-developed ontology-based bio-entity recognizer.
We evaluated our data set against the CRAFT corpus for gene/protein recognition based on Protein Ontology (PR)  (20, 21) , and the results indicate that the F-measure (0. 835) of our recognition tool is comparable to that of the state-ofart biomedical annotation system, BeCAS (0. 76)  (24). We compiled a list of 1126 human AAgs followed after a series of manual curation and constructed the AAgAtlas 1. 0 database (Figures  2 3 4 ). We compared AAgAtlas 1. 0 (1126 antigens) database to CTdatabase (276 antigens) and found that only 26 antigens overlapped (Figure  2B ). This might be due to that many antigens from CTdatabase were selected based on computational prediction  (19). The website of HPtaa database is not available and thus the comparison to AAgAtlas was not performed. The results here suggest that AAgAtlas 1.
0 database is the largest collection of human AAgs to date. In addition, the comprehensive collection of AAgAtlas 1. 0 database allows us to have an overview of human AAgs' protein classes and their related diseases. The gene ontology analysis reveals that the largest class of autoantigen proteins is nucleic acid binding, followed by signalling molecule, hydrolase, cytoskeletal protein, receptor, enzyme modulator and transcription factor (Figure  3A ). The disease ontology analysis results suggest that human AAg proteins are actively involved in major human diseases including immune system, hypersensitivity reaction, musculoskeletal system and cancer (Figure  3B ). All these results demonstrate the importance of our AAg collection and show the value of AAgAtlas 1. 0 database in human AAg studies.
However, several issues should be considered for AAgAtlas 1. 0 database. First, some AAgs in the full texts of the literature are not shown in abstracts and might not be included in our database. Currently there is no universal or centralized system capable of screening full text articles  (39) , especially for the journals from most publishers that are not freely available. The published abstracts are still the main resource for text-mining to date  (40). To obtain AAgs from full text, we have to figure out a systematic plan to obtain as many of these full-text papers as possible and extract AAgs from them, which will be executed in the next version of AAgAtlas database. Second, some AAbs recognize the post-translational modifications instead of protein epitopes. In this work, all AAgs were selected based on the proteins of AAb targeted regardless of post-translational modifications (3).
This may be due to the lack of high throughput AAb screening platforms that display proteins with modifications, although one was recently published  (41). Third, AAbs that recognize non protein biomolecules such as lipids, DNA or small molecules (3), were not considered in our database. Fourth, our curation process only confirmed that authors reported them as AAg and did not validate the supporting data from the experiments. To address this issue, we added a manual curation function to the end of each evidence phase with which the users can simply click the green 'Yes' or red 'No' button to confirm or deny our evidence (Supplementary Figure  S1  and Figure  S2 ). Our database system will be updated periodically. In conclusion, we identified 1126 AAgs, covering ∼5% of the human genome, and 1071 related human diseases by text mining and manual curation.
It is noticed that the PSO with POD gives good results with high accuracy and short computation time for locating damage than PSO with FEM. The calculations were carried out using MATLAB program in a PC with characterization Intel(R) core (TM) I3-2328 CPU2. 20 GHz. Performing such a test with traditional approach, where system responses are computed FEM, to solve this current damage detection problem, would require about one hour on an average computer. Moreover, sometimes the desired results may not be obtained. However, using previously calibrated POD-RBF procedure, the results are obtained in a bit more than 50 s for the first iteration. a b Fig. 9  Fitness convergence of PSO using FEM and POD (D3): a -damage 5%; b -damage 25%.
Moreover, technical challenges posed by the instability and ionization bias observed for sialylated glycans have been successfully overcome by permethylation as well as by selective stabilisation of sialic acids by e. g. ethyl esterification  [25] [26] [27] [28] [29] [30]. The latter method introduces a mass difference between α 2,3-and an α 2,6-linked sialic acids, allowing their discrimination by mass spectrometry, and is well suited for high-throughput N-glycan profiling using MALDI-TOF-MS  27. An important issue that arises when analysing larger sample sets by MALDI-TOF-MS is data processing, which is often the rate-limiting step. The recently developed processing software package, MassyTools, offers a valid solution to this problem by rapidly integrating a predefined list of analytes from any number of mass spectra  31.
The samples were mixed with 20 μL ACN and then pipetted through the pre-equilibrated cotton material extensively. The tips were sequentially washed three times with 20 μL 85% ACN + 1% trifluoroacetic acid and three times with 85% ACN. Glycans were eluted in 10 μL water. Afterwards, 1 μL of the TSNG SPE eluate was applied to an AnchorChip MALDI target plate, mixed with 1 μL of 5 mg/mL super-DHB in 50% aqueous ACN containing 1 mM NaOH, and dried at room temperature. Data acquisition. Derivatised glycan samples were measured using the reflectron positive ion mode of an Ultraflextreme MALDI-TOF-MS (Bruker Daltonics), equipped with a Smartbeam-II laser, and operated by flex-Control 3. 4 build 135. Prior to the measurement, external calibration was performed using a peptide calibration standard.
MassyTools data processing. Using the flexAnalysis Batch Process program (3. 3, build 65), the obtained spectra were exported into a text-based format ((x,y) file) consisting of an m/z value coupled with an intensity value per line, separated by a tab. In order to perform processing by MassyTools; software freely available at https://github. com/Tarskin/MassyTools), we applied the 'free' reducing end mass modifier and 'sodium' charge carrier as general parameters  31. Additionally, a single letter code was utilized by the program to refer to monosaccharides: H for hexose, N for N-acetylhexosamine, F for fucose, E for an α 2,6-linked N-acetylneuraminic acid and L for an α 2,3-linked N-acetylneuraminic acid. Internal calibration was performed using a minimum of 5 out of a list of 7 potential calibrants passing a signal-to-noise (S/N) ratio threshold of 9.
8 × 10 -4 and an average S/N of above 6 (Supplementary Fig. S2 ) were retained. A total of 77 analytes passed these quality criteria and were used for further analysis. Derived traits calculation & statistical analysis. Glycans were grouped according to their compositional features. All derived traits are based on single enzymatic steps (e. g. AG for the attachment of a galactose to an N-acetylhexosamine). The total set of 42 calculated derived traits can be found in the Supplementary Table  S2. Furthermore, statistical analysis was performed using IBM SPSS Statistics 20 (IBM, New York, USA). All derived traits were tested for normality using a Shapiro-Wilk test  45. As part of the derived traits were not normally distributed, a Wilcoxon signed-rank test was used to test pairwise differences observed in the cohort  46.
Introduction Structural bioinformatics is the part of bioinformatics (see for example  [35, 36] ) which deals with the analysis and prediction of the three-dimensional structure of biological macromolecules. It is closely related to computational chemistry and computational biology. Although the production of new 3D structural data is fascinating, one needs qualitatively new approaches to extract structurally and (consequently) biologically relevant information from such a huge amount of data. Such information can then be used in searches for biologically active compounds, including drugs. Part of our research focus is oriented in this direction. We have developed a collection of software tools that contribute to 3D data analysis and its subsequent implications. Among these are PatternQuery  [37]  for the quick definition and extraction of biomacromolecular fragments, SiteBinder  [38]  for fast and accurate comparison of these fragments, and MotiveValidator  [39]  and ValidatorDB  [40]  for the validation of ligands and non-standard residues.
In order to step forward towards biology, one needs to characterise the above extracted and validated data subsets. For these purposes, we offer AtomicChargeCalculator  [41]  for the fast calculation of partial atomic charges on small molecules, biomacromolecules and their complexes. We also offer MOLE  [42, 43] , a software tool for the detection and characterization of channels and pores in biomacromolecules. All these software tools are accessible from the website  [44]. The majority of the software is also available through Protein Structure Database in Europe (PDBe), and through PDBsum, a Web-based database of summaries and analyses of all PDB structures. Both platforms are operated by EMBL EBI, the European Bioinformatics Institute in Hinxton, UK.
Fragment Detection Tool PatternQuery PatternQuery  [45]  is a molecular language based on the Python programming language. This language describes biomacromolecular structural fragments using the nature and relationship between atoms, residues, and other structural elements. The individual fragment descriptions in the language (so-called queries) define the composition, topology, connectivity, and geometry of a fragment. Therefore, the PatternQuery language enables us to operate at the 1D, 2D and also 3D structure level. PatternQuery contains close to 120 keywords; examples of several PatternQuery keywords are given below:.
• Atoms (X) returns all atoms with the element symbol X • Residues (R1, R2) returns all residues with the two-letter code R1 or R2 • ConnectedAtoms (F, r) returns all atoms within distance r from fragment F • Authors (F) returns the authors of the structure containing fragment F • Weight (F) returns the molecular weight of fragment F These queries can be also combined into more complex ones. The PatternQuery language describes a fragment in such a way that it can be easily translated into a set of rules. In parallel, our methodology for finding fragments represents a biomacromolecular structure as a molecular graph, where atoms are vertexes and bonds are edges. Searching for a fragment is therefore realised as the detection of sets of atoms which meet the criteria defined in the PatternQuery language description.
Comparison Tool SiteBinder This software  [46]  tool is focused on a comparison (superimposition) of molecules with identical (or very similar) 2D structures, as this type of comparison is very helpful in processing biomacromolecular fragments. The SiteBinder superimposition consists of two interdependent stages. First, it is necessary to find the correspondence (atom pairing) between the atoms coming from different structures. In the second step, the sets of paired atoms (3D points) are fitted together as tightly as possible by a geometrical transformation (optimal fitting). Our methodology provides two superimposition approaches-a combinatorial approach and a subgraph matching approach. The combinatorial approach first generates a set of all chemically meaningful atom pairings. These pairings are generated in such a way that first the atoms in both fragments are divided into subsets according to their properties.
Validation Tool MotiveValidator and Database ValidatorDB MotiveValidator  [48]  is a tool which enables the validation of an individual ligand or sets of ligands. ValidatorDB  [49]  is a database of validation results for all ligands from the Protein Data Bank, updated weekly. These software applications implement the validation methodology called the validation of annotation, which is essential for the validation of ligands and biomacromolecular fragments. The goal of this approach is to evaluate whether the ligand or non-standard residue is denoted (annotated) correctly, i. e. , if its structure corresponds to the three-letter code it was assigned in the PDB file format. Specifically, the topology and stereochemistry of the validated molecule are compared to those of a correct molecule (reference molecule, model), and any differences found are reported.
Channel Detection and Characterization Tool MOLE MOLE is a software tool focused on the detection of channels and pores in biomacromolecules. A channel is a pathway connecting a point inside the biomacromolecule (e. g. , an active site) to an exterior one. A pore is a tunnel that passes through the biomacromolecule from one point on the surface to another. Channels and pores play significant roles in many biologically relevant systems. The algorithm for finding channels implemented in MOLE involves seven steps: (i) computation of the Delaunay triangulation/Voronoi diagram of the atomic centres; (ii) construction of the molecular surface; (iii) identification of cavities; (iv) identification of possible channel start points; (v) identification of possible channel end points; (vi) localization of channels; and (vii) filtering of the localised channels.
Big Data Problem in Drug Design and Structure-Property Studies Chemistry attempts to find the rules that control the behaviour of chemical compounds. Preferably for the universal laws, this refers to a whole population of molecules and/or substances, e. g. , conservation energy law. On the other hand, classical QSAR (QSPR) is used to describe a small series of congeneric compounds. With the enlargement of the chemical space we could have modified the questions asked. For example, we got interested in if a general rule exists that differentiates drugs from non-drugs, which molecular descriptors decide this or what does drug-likeness mean. At the same time, the availability of computers resulted in the explosion of information. Accordingly, we realised that data became big recently. There are many definitions of big data but generally what decides the difference between the conventional and big datasets are volume, velocity and variety, where volume refers to a massive size of datasets, velocity to the rate of the information increase, and variety to the diverse data forms here  [143].
Actually, however, we need economic considerations to fully understand the fate of drugs on the market. We have recently reported for the first time quantitative structure-economy relationships (QSER) for a large dataset of a commercial building block library of over 2. 2 million chemicals that shows that, on average, what we are paying for is a quantity of matter. Synthetic availability scores and selected atom counts also matter here  [150]. The structure-price relationship modelled differs from the classical QSAR in the fact that it is valid for the incredibly high numbers of objects; therefore, it is hard to believe that can be as precise and predictive as the classical QSAR relationship involving small data sets. In fact, this method is a statistic probing the whole population of the molecules available.
Third, it should enable user-controlled security. It should allow users to define fine-grained security settings to control the protection level of their cloud resources. For instance, to meet legal requirements that prohibit transfer of particular data types across jurisdictional boundaries, users might need to con-trol where their U-Cloud data is physically stored and processed. It must also protect user privacy by preventing cloud providers from accessing user data without the user's explicit consent. Finally, the architecture should guarantee integrity and availability of services and data. It should allow specification and enforcement of measures related to integrity, redundancy, and disaster recovery of data resources as part of a user-provider SLA. Performance guarantees might also be required, namely on response times for critical accesses to some data resources.