dataesr/scientific-paragraphs-categorization huggingface dataset:
Problem definition and example construction The first task involves identifying the problem and translating it into positive and negative examples. Here, we have limited our study to the task of discriminating deleterious/neutral mutations. The list of proteins known to be involved in human monogenic diseases was obtained from the OMIM database. 39 Based on this list, we selected the 8,117 mutations related to monogenic disease with high quality 3D models in MSV3d. In this work, the positive and negative examples are formalized as facts in the Prolog language, which represents a logical formula in predicate logic. A fact is a predicate expression that makes a declarative statement about the problem domain. The predicate for the positive examples is "is_deleterious". For example, a positive example in Prolog syntax is: is_deleterious(m_Q13496_Asn180Lys) indicating that, in protein Q13496, the replacement of the Asparagine at position 180 by a Lysine is deleterious.
Background knowledge construction The molecular consequences of missense mutations are related to the functional and structural contexts of the affected position, as well as to the physicochemical characteristics of the substitution. All these types of information are represented in MSV3d for the stored missense mutations and they are used as background knowledge in this study. Detailed descriptions of this information are available in. 31  Very recently, Kowarsch et al  40  noted that "we believe that researchers should not only look at conservation in their judgment of functional significance of residues in the protein sequence. Correlation patterns between residues clearly provide additional evidence which should not be ignored". To study the effect of neighbouring amino acid residues on a missense mutation, we enhanced the database by including the following additional features: • Neighbouring residues.
Residues are considered to be neighbours of a mutation if they occur within a sphere of radius 10 A°. For example, Figure  2  shows the neighbouring residues of the missense mutation p. Asn180Lys in protein Q13496. • Classification of amino acids. We used the amino acid classification system of Koolman  41  in which the amino acids are divided into aliphatic, acidic, basic, sulfur-containing, aromatic, neutral and imino, based on the side chain chemical features. The use of these features, together with physicochemical, functional, 3D structural and evolutionary features of the missense mutations allows us to discover hidden knowledge from different points of view of the missense mutations. The complete multi-relational data model used in our analysis is shown in Figure  3. In order to use ILP, we used SQL scripts to translate the information for each mutation stored in the MSV3d database (PostgreSQL) into Prolog facts (Fig.
Neighbor table Background knowledge expressed in Prolog modeh(1, is_deleterious(+mutationid)). modeb(1, modif_size(+mutationid, #value)). modeb(1, conservation_wt(+mutationid, -conservationwt)). modeb(1, (+conservationwt) @>= (#conservationwt)). modeb(1, (+conservationwt) @=< (#conservationwt)). modeb(1, secondary_struc(+mutationid, #secondarystruc)). modeb(*, neighbor(+mutationid, -aa)). modeb ( notes: Each mutation in the database is identified by a unique identifier 'id' and the values of each. Modeh defines the head of a hypothesised clause, while Modeb declares the predicates that can occur in the body of a hypothesised clause. The asterisk * in the mode declarations indicates that the corresponding predicate can be called many times during the construction of a hypothesised clause. Table  1.
Six noise values (0. 5%, 0. 75%, 1%, 2%, 3% and 4% of negative examples) were tested and the optimal value, ie, the value which resulted in the best performance on our data sets, was then used for the final model. In order to perform a stringent evaluation, we conducted a k-fold cross validation test. In this type of validation, the data set is randomly split into k equally sized subsets. The learning algorithm is then trained and tested k times. Each time, k-1 subsets are combined for training and the remaining one is used as the test set. In this study, we used sensitivity (Se) and specificity (Sp) to evaluate the performance of our As described earlier, we have an imbalanced data set (6,480 deleterious mutations and 1,637 neutral mutations). We therefore also considered the additional quality measure that is suitable for class imbalance learning, the Adjusted Geometric-mean  46  defined as: Gmean Se Sp = * AGmean Gmean Se Np Np = + + ( * ) 1 where Np is the proportion of positive (majority) examples in the dataset.
Novel hypotheses related to monogenic diseases A set of mutations known to be involved in human monogenetic diseases was identified by performing keyword searches in the OMIM database. These mutations were then mapped to the MSV3d database and pertinent structural, functional and evolutionary features were extracted and formalized as Prolog facts, as described in the Materials and Methods section. The examples of 6,480 deleterious mutations and 1,637 neutral mutations constituted the initial knowledge base. We then used the ILP system implemented in the Aleph program to learn rules that define whether a mutation is 'deleterious' or 'neutral', based on its structural, functional and evolutionary characteristics. Table  2  shows the average sensitivity, specificity and AGmean for 3 fold cross-validation for each of the noise parameter values tested.
This information is used only to identify the rules in our experiments. The two next columns contain the most important information: the "if" and "then" clauses of the induced rules. The two rightmost columns indicate the number of positive examples (deleterious mutations) and negative examples (neutral mutations) covered by the if-then rule in each row. A filter is available to facilitate the exploration, validation and interpretation of the rules. To illustrate how to transform ILP rules (expressed in the Prolog language) into English sentences, we can consider the fourth rule in Figure  5  (mutation67_97),.
6% of the dataset): i. the subfamily containing the 4 rules: 67_96, 67_140, 67_58 and 67_210 is_deleterious(A):-conservation_class(A, sub_family_conservation) and modif_charge(A, charge_opposite) and The "sub_family_conservation" predicate was found in all the rules in these two subfamilies and was highly predictive for the deleterious state. This supports a well known hypothesis that conserved positions in a protein are more likely to be functional and thus, that mutations of conserved residues are often deleterious. Nevertheless, the fact that the rules are complex and integrate other factors indicates that residue conservation alone does not determine the effect of the mutation. As an example, the first subfamily highlights another important factor, represented by the "modif_ charge" predicate, which characterizes many deleterious mutations.
Prediction service Based on the rules learnt by the ILP algorithm described above, a function aimed at estimating nsSNP effects related to human monogenic disease has been added in the KD4v server. It can be accessed via the Prediction link on the KD4v web interface in the main menu. KD4v allows users to specify the amino acid position and substitution of a given protein to be predicted. This includes the Uniprot accession number of the protein, the mutation position, the wild type residue, the mutant residue and the knowledge base (proteins related to human monogenic disease) to be used for rule inference. The wild type residue must correspond to the current protein sequence. Given the input mutation, KD4v sends a request to MSV3d to automatically generate a multi-level characterization. The process starts with the generation of mutant 3D models.
conclusions The ILP approach is consistent with the database perspective where KDD is organized around queries aimed at either selecting datasets for mining, or transforming these datasets, or querying and exploring large sets of patterns extracted from the data. This study presents a novel application of ILP in the bioinformatics field, namely, the characterization of the effects of a mutation on protein function and the corresponding human monogenic disease. Using MSV3d, a database of annotated mutation and phenotypic data, we identified mutations related to human monogenic disease and constructed background knowledge and a training set of positive and negative examples. The resulting mutation knowledge base contains a set of rules for distinguishing deleterious and neutral mutations. The rules confirmed previous findings concerning the physico-chemical and evolutionary features that characterize a deleterious mutation, such as the importance of the conservation of the mutated residue or the detrimental effects of modification of the amino acid charge, volume and hydrophobicity.
Funding The work was performed within the framework of the  Decrypthon program , co-funded by  Association Française contre les Myopathies [AFM ,  14390-15392 ];  IBM  and  Centre National de la Recherche Scientifique (CNRS) ;  ANR [Puzzle-Fit :  09-PIRI-0018-02 ,  BIPBIP :  ANR-10-BINF-03-02 :  FRISBI :  ANR-10-INSB-05-01 ]; Institute funds from the  CNRS, INSERM , the  Université de Strasbourg  and the  Vietnam Ministry of Education and Training  ( CT 322 ).
Population For this analysis, we pooled individual-level questionnaire data from the six case-control studies in the IMMC that had collected information on alcohol consumption (1,567 cases, 7,296 controls). Methods of previously published studies have been described. 1, 4, 5, 15, 16 nrollment period, age eligibility, study design (source of controls: hospital, population), number of cases and controls, and the centers within each study are summarized in Table  1. The European EpiLymph collaboration included studies that used both population and hospital-based controls, and Utah included spouse and population controls which we considered as population controls for our analysis. Four of the studies were conducted in North America and the remaining in Europe. Participants with histologically confirmed incident primary diagnoses of MM (ICD-O-3 diagnostic codes of 9731.
3, 9732. 3, 9734. 3, ICD-9 of 203, and the equivalent) that occurred during the respective enrollment periods were eligible for inclusion in this analysis. However, one study, Utah, enrolled both prevalent and incident cases of MM identified at a major regional myeloma clinic. In our sensitivity analysis we examined associations for incident and prevalent cases separately, and found the associations were not measurably different, therefore we included both types of cases in this analysis. The original data for each study were collected in-person by trained interviewers or by self-administered questionnaire. Protocols for the participating casecontrol studies were approved by the respective local institutional review boards, and study participants provided written informed consent.
Alcohol Data For this pooled analysis we requested data from each participating study, including variables on alcohol consumption, case/control status, and various demographic and lifestyle factors. We examined several measures of alcohol consumption, including usual alcohol drinking status (never, ever), former or current drinker, frequency (drinks per week), duration (years) and cumulative intake (drinks in lifetime). Alcohol drinking status was based on question(s) about their usual drinking behaviors prior to any current illness. Among ever drinkers, former/current status was calculated from questions regarding duration of drinking, therefore, we were unable to determine former/current status for subjects who did not provide drinking duration (229 cases, 1140 controls). In our sensitivity analysis we examined the associations between ever drinking and MM for subjects with and without current/former drinking status, and found the associations were not measurably different.
7, ≤21. 4, >21. 4, women: <1. 6, ≤7. 1, >7. 1 drinks per week); duration (men: <30, ≤43, >43, women: <23, ≤40, >40 years); cumulative intake (men: 16. 882, ≤48,400, >48,400, women: <2,743, ≤12,600, >12,600 drinks). These cut points were also used to categorize beverage-specific consumption measures. All analyses were performed using SAS unless otherwise specified. Within each study, we computed sex-specific odds ratios (ORs) with 95% confidence intervals (95% CI) relating the aforementioned alcohol variable categories to MM risk through unconditional logistic regression, with adjustment for age group (<50, 50-59, 60-69, 70+ years), race (white, black, other), and study center. Tests of trend for alcohol frequency, duration, and cumulative intake were performed by linear regression analysis.
We used the R package MiMa  18  to conduct a meta-analysis of study-specific findings using both random and fixed effects models. Heterogeneity between studies in the meta-analysis was assessed using the Q statistic. We also conducted a pooled analysis in a single harmonized data set, with ORs and 95% CIs for alcohol measures computed using unconditional logistic regression with adjustment for age, race, and study center. We evaluated potential confounding in analyses additionally adjusted for education level (<12 years of study; 12+ years or high school graduate; some attendance at college, technical, or vocational school after high school; graduation from college without further studies; or, other), body mass index (BMI; <18. 5, <25, <30, <35, 35+ kg/m 2 ), and smoking status (ever, never).
73 that we observed in this study. Another strength of our analysis was the availability of extensive data on other putative demographic and lifestyle risk factors for MM with which we were able to evaluate confounding and effect modification. An inherent limitation of this study is the retrospective case-control design of the participating studies, in which information on alcohol consumption was collected after disease diagnosis for cases. Although alcohol drinking status was based on participants' usual drinking behavior prior to illness, the critical window of exposure may not be captured in retrospective case-control studies since the effects of alcohol are based on consumption close to time of diagnosis. Not having a clearly defined reference period, such as one to three years prior to diagnosis, across all studies limited our ability to rule out potential disease effects prior to diagnosis.
Acknowledgments Funding for the Utah study was in part from the  Leukemia and Lymphoma Society   6067-09  (NJC) and the  NCI CA152336 (NJC). Data collection for the Utah resource was made possible by the  Utah Population Database (UPDB)  and the  Utah Cancer Registry (UCR). Partial support for all datasets within the UPDB was provided by the  University of Utah Huntsman Cancer Institute (HCI)  and the  HCI Cancer Center   Support grant ,  P30 CA42014  from the  NCI. The UCR is funded by contract  HHSN261201000026C  from the  NCI SEER program  with additional support from the  Utah State Department of Health  and the  University of Utah. The work was supported by the  European Regional Development Fund  and the  State Budget of the Czech Republic (RECAMO ,  CZ. 1. 05/2. 1. 00/03. 0101 ). The work conducted by  Brenda Birmann  was supported in part by grants from the  NCI  ( K07 CA115687 ,  CA149445 ) and the  American Cancer Society  ( RSG-11-020-01-CNE ).
Experimental set-up and data sample The experiment uses the naturally polarised muon beam at CERN. The experimental set-up consists of two major components: a polarised target and a magnetic spectrometer. A detailed description of the experiment can be found elsewhere  [23]. A major upgrade of the COMPASS spectrometer was performed in 2005. For this analysis the most relevant improvement was a new target magnet which extended the angular acceptance. The average beam muon momentum is 160 GeV/c and the average beam polarisation is P b = -0. 80 ± 0. 04. The target consists of two cells in 2002-2004 and of three cells in 2006, located along the beam one after the other and filled with 6 LiD. Lithium-6 can be regarded as a quasi-free deuteron and a helium-4 core. The average deuteron polarisation |P t | is about 0.
5 and the average dilution factor of the target f is 0. 36. The latter is the ratio of the cross-section for all polarisable nucleons in the target material (deuterons) to that for all nucleons and includes radiative corrections. The relative uncertainties of |P t | and f are 5% and 2%, respectively. The data were collected during four years: 2002 to 2004 and in 2006. Selected events have an interaction vertex located in the target fiducial volume and contain both a beam muon and a scattered muon. The DIS region is selected by the requirement Q 2 > 1 (GeV/c) 2 and by a selection depending on the energy fraction y carried by the exchanged virtual photon, which leads to an invariant mass squared of the hadron system of W 2 > 5 (GeV/c) 2. Events with y < 0. 1 and with y > 0. 9 are rejected because the former are more sensitive to experimental biases, while the latter are strongly affected by radiative effects.
The final relation between the gluon polarisation and A 2h LL can be written as: ∆g/g(x av g ) = A 2h LL (x B j ) -a corr λ 1 -λ 2 , with a corr = A incl LL (x B j ) R LP R incl LP + A incl LL (x C ) 1 R incl LP a QCDC LL D R QCDC - a incl,QCDC LL D R incl QCDC R LP R incl LP (5) -A incl LL (x ′ C ) a incl,QCDC LL D R incl QCDC R incl LP R QCDC R incl LP a QCDC LL D. In the extraction of ∆g/g we use a method similar to the one used in Ref. [26]. Data from different cells j = u, d, u ′ , d ′ are combined so that beam flux, apparatus acceptance and spinaveraged cross-section cancel. The gluon polarisation is measured by solving the second order equation: p u p d ′ p u ′ p d = (1 + A corr u w + Λ u w ∆g/g(x av g ))(1 + A corr d ′ w + Λ d ′ w ∆g/g(x av g )) (1 + A corr u ′ w + Λ u ′ w ∆g/g(x av g ))(1 + A corr d w + Λ d w ∆g/g(x av g )) , (6) where p j is the sum of event weights w in sample j and A corr j w and Λ j w are weighted means of f P b P t a corr and f P b P t (λ 1 -λ 2 ), respectively.
Monte Carlo optimisation and Neural Network training In the present analysis the NN package from Ref. [27]  is used. Many results derived from a Neural Network approach strongly depend on the Monte Carlo sample on which the NN is trained. Thus, a good description of the experimental data by MC simulations is essential for the analysis. The LEPTO event generator  [28]  (version 6. 5) is used to generate both an inclusive DIS sample and a sample which already contains at least two high-p T hadrons. The generated events were processed by the detector simulation program COMGEANT and reconstructed in the same way as real events by the reconstruction program CORAL. Finally, the same requirements are used in the analysis of real and MC events. Prior to the MC generator studies, an extensive effort was made to improve the detector simulation in COMGEANT.
This pioneering phylogenetic result implied the inclusion of the small and poorly-known deep-sea mussels from organic falls in the Bathymodiolinae. Recently, an evolutionary scenario was proposed in Figure  1. Dendrogram obtained from Bayesian analyses of the combined dataset of COI mtDNA and the 28S rRNA. Asterisks correspond to nodes with posterior probabilities (PP) obtained from BA analysis and bootstrap values obtained from ML analysis that are higher than 0. 90 and 50%, respectively. (modified from  [7] ). doi:10. 1371/journal. pone. 0069680. g001 One of the most striking feature discovered with these 'extreme' environments is symbiotic interactions with chemosynthetic bacteria  [8] , interpreted as a key adaptation of bathymodioline mussels to the particular environmental conditions of deep-sea vents and seeps.
Data Analysis Genetic structure of mussel populations. Sequences were edited using Sequencher 4. 1. 4 and aligned using the Clustal W module implemented in Mega 4. 0  [36]. Sequences were deposited both in BOLD and GenBank (COI: KC861674-KC861725; 28S: KC861726-KC861749 and KC904226-KC904234). For the COI dataset, for each locality, the number of haplotypes (Hap), the number of segregating sites (Seg), haplotype (Hd) and nucleotide (Pi) diversity were determined using Arlequin 3. 1. 1. 1  [37]. Phylogenetic relationships among haplotypes were constructed using the Median-Joining algorithm  [38]  and maximum parsimony post-processing calculation  [39]  implemented in Network 4. 5. 1. 6 (http://www. fluxus-engineering. com). F ST was estimated among sampling locations using the formula of  Weir and Cockerham (1984)   [40]  to assess population genetic structure.
These calculations were based both on haplotype frequencies and on their pairwise nucleotide differences. A distribution of F ST values under the null hypothesis of spatial genetic homogeneity was drawn from 10,000 datasets simulated by permutations of specimens between south-western localities. Genetic structure was also analysed with an exact test of differentiation using a Markov chain process with a burnin of 1000 steps. An Analysis of Molecular Variance (AMOVA;  [41] ) was performed among populations of the south-western part of the sampling region (Papua New Guinea, the Solomon Islands, Vanuatu and New Caledonia). An AMOVA was also assessed in order to evaluate hypothesised patterns of bathymetric genetic structure by using two depth categories (sampling,and. 600 m). All tests were performed in Arlequin 3.
Under the coalescent model modified from  Hudson (1990)   [45]  1,000 replicates were carried out to calculate parameters, to define 99% confidence intervals, and to calculate the sum of squared deviations (SSD) of these parameters between the observed and estimated mismatch distribution. Phylogenetic relationships. Phylogenetic relationships at the intra-specific level were inferred from COI sequences. A    sequence of the species I. macdonaldi (AY649804), which belongs to the same clade as I. iwaotakii, was added to the dataset as an outgroup. A phylogenetic tree based on the neighbour-joining (NJ) method of Kimura two-parameter (K2P) distances was built using MEGA version 4. 0  [36]. The bootstrap analysis was based on 1000 replicates. Symbiont diversity. The sequences obtained for the bacterial 16S fragment were aligned using the Clustal W module implemented by Mega 4.
0  [36]. Sequences with $99% identity were considered as a single phylotype and a single representative clone from each phylotype was chosen for phylogenetic analysis. Sequences were deposited on GenBank (KC904235-KC904238). Sequences were compared with the NCBI (http://www. ncbi. nlm. nih. gov/) database using BLAST  [46]. The most similar BLAST sequences were included in phylogenetic analysis. Phylogenetic reconstructions were performed using maximum likelihood (ML) and Bayesian (BA) analyses. The most appropriate evolutionary model was estimated using jModelTest 0. 1. 1. The GTR+I+C model was chosen based on AIC. The ML analysis was performed using the software Treefinder  [47]  and node support was assessed with 1,000 bootstrap replicates  [48]. BA was performed using MrBayes v3. 1. 2 [49].
Genetic Diversity and Phylogenetic Analyses A total of 50 COI mtDNA sequences of 579 bp were obtained. Moreover, 53 sequences available on Genbank were added to this new dataset for the analyses. Because the three sequences available  on Genbank for Japan specimens are 430 bp (AB257520, AB257521 and AB257523)  [51]  and haplotype network reconstructions are sensitive to missing data, this analysis was performed on a reduced dataset of 430 bp long, in order to include as many specimens as possible. For each locality, the number of haplotypes (h), the haplotype (Hd) and nucleotide (p) diversities and the number of segregating sites (S) are given in Table  2. Indexes of diversity were low except for nucleotide diversity in populations from Japan, the Philippines and Papua New Guinea (4. 467, 2.