dataesr/scientific-paragraphs-categorization huggingface dataset:
Inductive Logic Programming (ILP) ILP is a machine-learning method which uses relational data as input and has been successfully applied to various areas including bioinformatics  [28] [29] [30]. It allows us to learn a concept definition from observations, i. e, a set of positive examples (E+) and a set of negative examples (E-), and background knowledge (B)  [31]. The ILP experiments produce theories as sets of first-order logic rules. They where conducted here with the Aleph Program  [32]. Many parameters can be tuned for theory construction. The three main parameters are the min-pos, the noise and the induce-type. The min-pos parameter is the minimal number of positive examples that a rule must cover. The noise corresponds to the maximal number of negative examples that an acceptable rule may cover (in our case, one is never sure that a drug does not have a given side effect).
Direct test Theories and decision trees are also evaluated by direct test. Drugs used for testing are those present in SIDER 2 and DrugBank (v3. 0) but not present in SIDER. For these drugs all descriptors are retrieved and stored in the Net-workDB. Furthermore, the reports of FAERS from 2004 to 2011 were imported as a database and used as an external information source for checking the false positives predicted by our models. We consider that a molecule is associated with a SEP in FAERS if for each TC of the SEP there is at least one report that states that the molecule is the primary suspect of an observed side effect belonging to the TC. Our checking procedure is just an anticipation as it relies on the fact that updating the package insert of a drug (stored in SIDER) requires that sufficient amount of adverse effect incidents occur (especially for new drugs).
The amount and quality of available data may explain the observed differences of results between SEPs. It should be noted that comparison with other reported methods is uneasy due to the fact that we aim to characterize and predict SEPs rather than isolated side effects. In fact the closest study is the one of Yamanishi et al. [9]  whose objective is to predict isolated side effects using multi-class statistical methods. Therefore these authors do not produce comparable accuracy values. Table  3  shows the results obtained with the set of test molecules. Among the novel drugs present in SIDER 2, only 20 are associated with at least one of the 26 studied SEPs. These drugs have been tested with decision trees and ILP theories obtained for each SEP. The total number of drugs in the test set that are associated which each SEP is indicated (column Positives) and compared to the true positive values (TP columns) obtained with test set using either DT model or ILP theory relative to this SEP.
Conclusions Our study proposes an integrative machine-learning approach for predicting side-effect profiles (SEPs) and understanding their mechanisms. We integrate drug characteristics and background knowledge such as functional annotation, interactions and pathways in a relational database. An extensive learning set is built by associating drugs with clusters of side effects (TCs) according to SIDER information. Our first contribution consists of extracting SEPs from this complex table of fingerprints as the longest groups of TC shared by more than one hundred drugs. We also set up two machine-learning methods, namely decision trees and inductive logic programming in order to learn which combination of properties of drugs and their targets leads to a given SEP. After evaluating the learning models, our general observation is that ILP models have a higher sensitivity than DT models.
A goodness-of-fit test for normal distribution was carried out using the Kolmogorov-Smirnov-test for each dataset. To analyse the effect of language family, geography, and neighbouring countries, significant differences (p,0. 05) were tested with ANOVA, Tukey HSD test for normal distributed datasets. All statistical tests were carried out with STATISTICA 9. 1. , StatSoft, Inc. (2010) www. statsoft. com.
Acknowledgments We are grateful to  Reinhard Agerer ,  Eef Arnolds ,  J. L. Baptista-Ferreira ,  Francesco Bellu ,  Emidio Borghi ,  Frank Da ¨mmrich ,  Cvetomir M. Denchev ,  Stefanos Diamandis ,  Sally Evans ,  Marco Floriani ,  Ulf Ga  ¨rdenfors,  Vera Hajova ,  Klaus Høiland ,  Daniele Inita ,  Boris Ivanc  ˇevic, LaszloJager,  Nedim Jukic, Mitko Karadelev ,  Gordana Kasom ,  Jonas Kasparavic  ˇius,  Heikki Kotiranta ,  Alexander Kovalenko ,  Ellen Larsson ,  Victor Mukhin ,  Andrej Piltaver ,  Irja Saar ,  Dirk Stubbe ,  Catalin Tanase ,  Andy Taylor ,  Annemieke Verbeken , and  Eugene Yurchenko  for providing information relating to national list of commercialised edible mushrooms. We thank  Jean-Pierre Augst ,  ReneChalange ,  Jean-Pierre Fombeur ,  Jean-Marc Moingeon ,  Jean Pisot  and  Jean Rove  ´a for information on the French mushroom legislation.
General experimental procedures Freeze-dryer was a FreeZone2. 5Plus type (Labconco, USA). TLC was carried out on precoated Macherey-Nagel Alugram silica, and spots were visualized by spraying with iodine vapor or ninhydrine reagents. The UV spectrum was recorded on a Helios Omega spectrophotometer (Thermo Scientific, France). The IR spectrum was obtained on a spectrometer IR-FT Paragon 1000 PC (Perkin-Elmer, USA). The NMR spectra ( 1 H, 13 C, HSQC, HMBC, COSY and TOCSY experiments) were recorded on a Bruker 500 with a TXI cryosonde (PRISM, Rennes University, France) and a Bruker Avance 500 (CRMPO, Rennes University, France) for in-deep 2D experiments and irradiations tests. HRESIMS data were recorded on a Micromass Zab Spec Tof (CRMPO, Rennes University, France). ESIMS data of the acetylated product was recorded on a Finnigan LCQ mass spectrometer (IFREMER, Nantes, France).
Acknowledgements We thank  Dr. Maria-Jesús Uriz ,  Research Professor  at the  Centro de Estudios Avanzados de Blanes (CEAB), Spain  for sponge identification,  Pr. Sourisak Sinbandhit  and  Pr. Philippe Jehan  from  CRMPO  and  Pr. Arnaud Bondon  from  PRISM platform at Rennes University, France  for the NMR and MS, and the  CNRST of Morocco  for the support of the mobility of the researchers of this project.
g. , the hypotensive prodrug minoxidil becomes fully active after sulfate conjugation). Further, SULTs can convert some chemicals to carcinogens or to activators of promutagens by creating highly reactive sulfate esters that can bind covalently to DNA (e. g. 7,12-dimethylbenz(a) anthracene)  [4, [6] [7] [8]. SULTs that are responsible for the metabolism of small endogenous compounds and xenobiotics are localized in the cytosol  [4, 8, 18]. Four families of human SULTs have been identified by now, SULT1, SULT2, SULT4 and SULT6, and more than 30 X-Ray structures, holo or apo, have been reported in the Protein Data Bank (PDB)  [14, 19]. Among them, SULT1, metabolizing a wide variety of compounds like phenols, thyroid hormones and drugs (e. g. minoxidil, paracetamol, 17α-ethinylestradiol), is the most expressed one (found in liver, lung, intestine, kidney, thyroid, blood or brain  [18] ).
Notably, experimental and computational approaches have been proposed to predict Absorption, Distribution, Metabolism, Excretion and Toxicity (ADME-Tox) properties of drugs or the response to environmental toxins  [1, [20] [21] [22]. ADME-Tox predictions  [12, [22] [23] [24] [25] [26]  are challenging but extremely important in prioritizing appropriate small molecules not only during the selection of potent candidates in drug discovery projects but also, to some extent, for chemical biology studies. Classical in silico ADME-Tox predictions are mostly based on statistical approaches using annotated databases, like Quantitative Structure-Activity Relationships and Quantitative Structure-Property Relationships (QSAR/QSPR)  [26] [27] [28]. However, the complexity of ADME-Tox molecular mechanisms, for instance specific interactions with DMEs or with other ADME-Toxrelated proteins, requires a deep mechanistic understanding  [10] [11] [12] 29]  of the ligand-protein interactions at atomic level.
Collecting SULT1 Ligands We decided to explore three isoforms, SULT1A1, SULT1A3 and SULT1E1, belonging to the largest SULT1 family and for which a number of substrates and inhibitors have been Visualization of the human structure sulfotransferase 1A3 (PDB ID: 2A3R). A: SULT1A3 is represented in cartoon colored according to its secondary structure, red for helices, yellow for strands and green for loops; the co-factor PAP and the co-crystallized ligand Ldopamine are shown in sticks. B: SUTL1A3 is represented in grey surface, with the co-factor PAP and the co-crystallized ligand L-dopamine in sticks. doi: 10. 1371/journal. pone. 0073587. g001 idebntified  [14, 18, 42]. We collected 157, 117 and 80 known binders (substrates or inhibitors) for SULT1A1, SULT1A3 and SULT1E1, respectively (see Materials & Methods section).
Several drugs (paracetamol, minoxidil), estrogens and toxic compounds (like bisphenol A used in plastic industry and recently discovered to be toxic) are present in our collection. Chemical structure clustering (see Materials & Methods section for details) resulted in 60, 50 and 33 diverse active compounds for SULT1A1, SULT1A3 and SULT1E1, respectively (examples for each isoform are shown in Table  1  and Figure  S1 ). Putative decoys were taken from the diverse chemical compound libraries ChemBridge™ PremiumSet™ and the Maybridge® HitFinder™. The actives and decoys were merged and filtered using a soft drug-like filter in terms of physicochemical properties (see Materials & Methods section). For each isoform, we obtained two validation datasets, ChemBridge and Maybridge, containing: 46556 and 13148 compounds for SULT1A1; 49546 and 13138 compounds for SULT1A3; 49529 and 13121 compounds for SULT1E1, respectively.
Indeed, the amino group of L-dopamine is located between the two carboxylic groups of Glu146 and Asp86, thus involved in two salt bridges. During the MD simulations without bound ligand, the repulsing interaction between the two carboxylic groups led to the opening of loop 1 (amino acid residues 85-90). Interestingly, the superposition of the two available X-ray structures for SULT1A3, one bound with L-dopamine (PDB ID 2A3R)  [46]  and one apo (PDB ID 1CJM)  [47] , shows a displacement of loop 1 of 4. 9 Å. Thus, the substrate-binding pocket is largely open in the X-ray apo structure likewise in the modeled MD apo structures. The conformational change of loop 1 of SULT1A3 accompanying the ligand binding  [14, 44]  is strongly supported by the holo and apo X-ray structures and the MD apo conformations.
Identifying the Best Multiple Receptor Conformations by Virtual Screening Virtual Screening (VS) experiments were performed using docking-scoring approach in order to identify the protein conformations, which can better discriminate known binders from putative decoys. We ran 56 VS for all isoforms. A total of 24 VS were carried out for SULT1A1 on the 11 representative MD conformations and the X-ray structure with two compounds datasets, ChemBridge and Maybridge. Similarly, we performed 16 VS for SULT1A3 and 16 VS for SULT1E1. The best results obtained for the MD centroid conformations and the X-ray structures for each isoform are shown by enrichment graphs in Figure  5. Protein-ligand interaction energies for active centroids as predicted by docking (averaged for the X-ray and the two best performing MD structures) and compared to the experimental affinities are given for illustration in Table  S1  (see the Supporting Information).
As it is widely accepted, docking approaches cannot achieve an exact prediction of the binding affinities, rather they help to prioritize active molecules among a large number of compounds. Two MD structures for SULT1A1 achieve better enrichment results than the X-ray structure on the ChemBridge™ dataset (Figure  5A ). Indeed, the structures of clusters 4 and 10 show Area Under Curve (AUC) for the Receiver Operating Characteristic (ROC) curve (not shown) larger than the X-ray structure. The early enrichments are also better for these two MD structures than for the X-ray one. Similar results are obtained on the Maybridge® dataset (Figure  5B ). The best performing is the cluster 10 structure adopting slightly larger binding pocket than the holo X-ray structure allowing thus better prediction for the larger actives.
In the case of SULT1A3 (Figure  5C  and 5D) the VS experiments did not suggest a MD structure performing better than the holo X-ray one, yet early enrichments are slightly better for two MD structures than for the X-ray one in the case of the ChemBridge™ dataset. The MD structures and the holo X-ray for SULT1A3 show similar AUC, lower than those for SULT1A1. The best MD structures for SULT1A3, clusters 3 and 7, have binding sites twice larger than the holo X-ray one, thus, not allowing to correctly rank the small actives and prioritizing large ligands, actives or decoys. Our analysis showed a balanced representation of small and Overall considering the flexibility of the binding sites via multiple receptor conformations improved the enrichments. We achieved better discrimination for binders of SULT1A1 and SULT1E1 than for SULT1A3.
We should note that SULTs have not been extensively experimentally screened and we did not find real decoys in publicly available chemistry databases and in several commercial collections (see the Methods section). Therefore, developing in silico models, structure-based or QSAR, to predict ligand binding for SULTs is challenging, it might be possible that some of the used putative decoys are active for some SULT isoforms. Yet, our results demonstrate that the developed approach can be successfully employed to predict ligand binding to the three SULT1s isoforms studied here. Resulting in accurate QSAR models based on structure-based methodology, our approach constitutes a major advance in the in silico prediction of ADME-Tox properties of small molecules related to interactions with phase II DMEs.
Compound datasets preparation Small molecules known to bind to SULT1A1, SULT1A3 and SULT1E1, substrates or inhibitors, were collected (Figure  7 ) from the databases BRENDA  [59] , Aureus Sciences (http:// www. aureus-sciences. com/), TOXNET (http:// toxnet. nlm. nih. gov/), PubChem  [60]  and literature  [6, 17, 18]. We did not found experiment data for inactive molecules, thus we took putative decoy molecules those from two diverse chemical compound collections, ChemBridge™ PremiumSet™ (http:// www. chembridge. com/) and the Maybridge® HitFinder™ (http://www. maybridge. com/) composed of 50000 and 14000 compounds, respectively. In order to select only drug-like molecules, all datasets of actives and putative decoys were filtered using the FAF-Drugs 2 server  [61]  using "soft" drug-like physicochemical properties (see Text S1 in supporting information) while the search utility to remove toxic/reactive groups was turned off.
After filtering, 49496 and 13088 decoys remained from the ChemBridge™ and Maybridge® datasets, respectively. In order to select chemically diverse molecules for validation of our approach and to avoid possible overrepresentation of a chemical series, we performed several initial tests to cluster the actives using the fingerprints ECFP_4, ECFP_6, FCFP_4 and FCFP_4 as implemented in Pipeline Pilot v. 7. 5 (SciTegic, Inc/Accelrys). Finally, the filtered drug-like actives were clustered using FCFP_4 with a Tanimoto similarity criterion of 0. 6. We then created two compound datasets for each SULT1 isoform taking the active centroids of each cluster for the corresponding isoform, and the decoys from the Chembridge™ et and the Maybridge® filtered datasets, respectively.
Protein structures preparation For the three most important SULT1 isoforms, SULT1A1, SULT1A3 and SULT1E1, we selected X-ray structures with cocrystallized ligands and as complete as possible. For SULT1A1, 5 holo PDB structures are available, all sharing very similar conformations (all atom RMSD between the 5 structures vary between 0. 170 and 0. 239 Å). The visual analysis of these 5 structures did not show striking differences in the binding sites. For SULT1A3, only 1 holo structure is available. The second one is an apo-form with many missing residues around the cofactor binging site. For SULT1E1, only 1 holo structure is available. Thus, we took the X-ray structures co-crystallized with the ligands: p-nitrophenol (PDB ID: 1LS6  [6] ), L-dopamine (PDB ID: 2A3R  [46] ) and 3,5,3',5'-tetrachloro-biphenyl-4,4'-diol (PDB ID: 1G3M  [62] ), for the isoforms SULT1A1, SULT1A3 and SULT1E1, respectively.
Virtual screening experiments First we performed preliminary docking experiments with DOCK6. 0  [73] , AutoDock  [74]  and Vina 1. 1. 1 [75] , to dock several small and large ligands, dopamine, 4-nitrophenol, pentachlorophenol, estradiol and 3,5,3',5'-tetrachlorobiphenyl-4,4'-diol, into the three isoforms. We obtained the best docking results using Vina with a RMSD of the bioactive conformations within 1Å. Thus, for the subsequent prediction of the SULTs binders, we performed VS experiments using Vina. We used grid resolution of 1 Å, number of binding modes of 10 and exhaustiveness of 8. Fifty six VS runs were performed on X-ray structures and protein structures extracted from MD for the two datasets, the ChemBridge™ and the Maybridge®, containing 49496 and 13088 compounds, respectively. For SULT1A1, the grid size was of 24 Å for the three X, Y, Z axes and the center coordinates were set to 22.
Background: There is no functional pharmacovigilance system in Cambodia to our knowledge. Mobile phone-based tools, such as short message service (SMS) text messages, are increasingly used for surveillance purposes. Objective: To pilot-test the FrontlineSMS mobile phone-based tool for notification of adverse events, using Cambodia's only International Vaccination Center at the Institut Pasteur du Cambodge as a field site. Methods: People receiving vaccinations, aged over 18 years, and who owned a cell phone were recruited in the study following informed consent. The names and mobile phone numbers of the participants interviewed were entered each day into the FrontlineSMS software. Two days after being vaccinated, participants received an automatically generated SMS text message asking whether any adverse events had occurred.
To our knowledge, there is no functional pharmacovigilance program in Cambodia. Pharmacovigilance has been conducted using complex and rigorous notification procedures in developed countries, but it faces challenges in developing countries because of issues with clinician awareness, clinical expertise, or nonfunctional reporting systems  [11, 13, 14]. Therefore, some programs or countries have circumvented these challenges by implementing mobile phone-based and Web-based tools using short message service (SMS) text messaging for immediate notification of adverse events  [15, 16]. FrontlineSMS is one such reporting tool  [17]. It has been used in Cambodia for various health-related projects, but always for information exchange among health providers and stakeholders  [18] [19] [20]. An Epidemiology Master student helped pilot its use for surveillance in direct link with health users in a vaccination center.
Method The research project received ethical approval from the national ethics committee on health research on February 17, 2012. Data collection began March 12, 2012, and ended May 31, 2012. During that period, a research assistant from the epidemiology and public health unit at IPC (a native Khmer speaker) spent several hours each day at the International Vaccination Center to recruit participants. Participants were eligible to be included in the study if they were aged over 18 years, came to the center to be vaccinated themselves (ie, not for their children), agreed to participate, owned a cell phone, and knew how to send SMS text messages. If so, the research assistant informed them about the study, read through the protocol, explained the objectives of the pharmacovigilance project in Khmer, and asked the participants to complete an informed consent form.
Information about their name, age, place of residence, type of vaccine, and mobile phone service provider were collected. The names and mobile phone numbers of the participants interviewed were entered each day in the FrontlineSMS software. Two days after being vaccinated, participants received an automatically generated SMS text message. This message thanked them for participating and asked whether any adverse events had occurred. Their SMS reply was number-coded as follows: 0=no adverse event, 1=mainly redness and/or pain at the injection site, 2=mainly fatigue and/or weakness, 3=mainly headaches, 4=mainly fever, 5=mainly runny or congested nose, 6=mainly muscle pains, 7=mainly abdominal pain, 8=seizures or neurological problems, and 9=severe allergic reaction. Only 1 code was allowed per reply.
Messages received were exported from the software daily to an Excel spreadsheet and examined before being saved. Upon receiving the text reply, a software-generated message was sent. If the codes corresponded to no or a moderate adverse event (codes 0-7), the reply was: "We have received your message. Thank you for having participated. " If the participant sent back a code for a severe adverse event (8 or 9), the reply read: "You have reported a severe adverse event. Please consult the nearest doctor as soon as possible. If in Phnom Penh, we recommend that you refer to Hôpital Calmette. " (The Hôpital Calmette is a national reference hospital where the emergency department team had been informed of the study. ) In cases when a severe adverse event was reported, the research assistant called the participant's cell phone number directly to follow-up on the participant.
Discussion The objective of this pilot study was to assess the feasibility of an SMS text-based reporting tool for pharmacovigilance in Cambodia. According to the International Communications Union database, there were 69. 9 mobile-cellular telephone subscriptions per 100 (%) inhabitants in Cambodia in 2011 (slightly less than India), up from 7. 95% in 2005  [21]. This translates to approximately 10 million cellular telephone subscriptions in Cambodia in 2011, a country with an estimated population of approximately 14 million  [22]. To our knowledge, this pilot SMS text-based system for active detection of adverse events following vaccination is the first functional pharmacovigilance system in Cambodia, the first SMS text-based surveillance system relying on health users' participation in Cambodia, and the first to use SMS text-based surveillance for adverse event detection anywhere  [20].
Acknowledgments We wish to gratefully acknowledge the health users at  IPC 's international vaccination center for agreeing to participate in this pilot study. This study was conducted thanks to a grant from the  Pierre Ledoux Foundation  and additional support from the  Centre de coopération internationale en recherche agronomique pour le développement (Cirad). We also want to acknowledge the  Social Impact Lab Foundation  for developing and allowing free access to the open-source FrontlineSMS software package.
Acknowledgements R. S. B. G thanks the  FAPERJ, CNPq , the  Marie Curie program , the  Farmaguinhos/FioCruz , and  PGQu  for financial support. Central Glass Co. Ltd. is gratefully acknowledged for kindly providing fluoral hydrate and HFIP, as well as the  IT department from Université Paris-Sud  for providing computing resources. The authors would like thank  M'bokeno Gomes Lowa , Erasmus student. We also thank the  French Fluorine Network (GIS Fluor ).
Statistical analysis All experiments were performed at least in triplicate, and results are expressed as mean ± SEM. Statistical analysis was performed using the statistical software XLSTAT (version 2012. 1. 01, Addinsoft, Paris, France). The results are analyzed by the univariate ANOVA test followed by Dunnett/Tukey test for multiple comparisons and determination of significance level. Group means were considered to be significantly different at P < 0. 05.