dataesr/scientific-paragraphs-categorization huggingface dataset:
Strong activation of PI3K/AKT pathway has also been associated with enhanced bladder tumour proliferation  [50]. Accordingly, in our model, PI3K/AKT gain-of-functions counteract p53 pathway effects (fifth row of Figure  4c ), impairing the apoptotic phenotype. Two other important loss-of-function known to be associated with poorer prognosis in bladder cancers have been simulated. Deletions of either PTEN or p14 in our model are associated with a more aggressive phenotype (sixth and seventh row of Figure  4c ). The former is a tumour suppressor shown to inhibit AKT   S4 , which includes a summary of all modelling assumptions, references (PubMed links) and the specification of the logical rule associated with each component. The source file is further provided as supplementary Dataset S4, which can be opened, edited, analysed and simulated with the softare GINsim (www://www.
This analysis mainly consisted in cross-checking the attractors obtained in our simulations with the qualitative information retrieved from the literature, without focusing on the full dynamics of the system. A summary of the results obtained is provided in Table  1  (for more details, see the supplementary Dataset S3). Additionally, all the simulations performed can be easily reproduced using the model files available as supplementary Dataset S4. Briefly, the involvement of MAPKs in cell fate decision was assessed through perturbations of relevant components. The model accounts for the pro-apoptotic role of p38 and JNK, as well as for the promotion of growth arrest by p38, and for the proliferative role of ERK. The model also recapitulates the p21-mediated tumour suppressor function of p53, along with the impairment of this function due to epigenetic silencing of GADD45.
Predictions generated with the MAPK logical model Having shown that our MAPK model is consistent with published data, we designed additional simulations to explore novel mechanistic hypotheses. ERK-related feedback mechanisms. So far, we have described the behaviour of our model in presence of tumoural deregulations of growth factor receptors. Let us consider now what happens when the expression of such receptors is not altered (i. e. unperturbed logical rules for either EGFR or FGFR3 variables), in presence of sustained growth factor stimulation (i. e. either EGFR_stimulus or FGFR3_stimulus set to 1 throughout the simulations). The attractor reached from normal EGFR stimulation is characterised by oscillations (between 0 and 1) of the values of EGFR, ERK and p53, thus leading to oscillations of phenotype variables, in particular for ''Proliferation'' (Dataset S3 -r1).
This behaviour contrasts with the well defined phenotypes obtained following EGFR over-expression (Figure  4a ). It can be interpreted as the impossibility to obtain sustained activation (or inactivation) of the considered actors in presence of sustained growth factor stimuli. In other words, ERK oscillations in our state transition graph correspond to the transient ERK activation in the ODE-based model from Orton et al. [38]. Similar results are obtained for FGFR3 stimulation (Figure  4b  and Dataset S3 -r2), in agreement with literature  [74]. The main negative feedbacks underlying such responses are acting directly on the receptors (Figure  2 ): one accounting for GRB2-dependent ubiquitination and degradation of the receptors   [11]  In absence of inputs, constitutive activity of any one among RAF or RAS can lead to permanent ERK activation, associated with proliferation.
red3 r32 JNK might reduce RAS-dependent tumour formation by inhibiting proliferation and promoting apoptosis  [88]. In absence of other stimuli, JNK constitutive activation completely abrogates RAS-dependent proliferation following RAS over-expression (r18). Instead, apoptotic attractors are always reached. Asterisks denote facts explicitly related to bladder cancer, whereas unmarked entries correspond to generic or loosely specified mechanisms. Full simulation results can be found in Dataset S3, with the help of the identifiers provided in the first two columns. doi:10. 1371/journal. pcbi. 1003286. t001 [75] ; the other accounting for PKC-mediated negative effects  [74, 76]. Concomitant disruptions of these feedbacks in our model lead to simulation results equivalent to those obtained with receptor gain-of-function (cf.
Supporting Information Dataset S1 MAPK reaction map. The png (map) and txt (annotations) files were directly exported from the corresponding CellDesigner file (Dataset S2). Map components are coloured to emphasise relevant classes of proteins. The default protein colour is light green, whereas the default gene colour is yellow. MAPK cascades are coloured with different blue gradations (from light to dark blue going down the cascade). Scaffold proteins are coloured in darker green; phosphatases are coloured in red. Complete graphical notations can be found at www. celldesigner. org. (ZIP) Dataset S2 CellDesigner file (xml format) of the MAPK reaction map. Species and reactions are annotated and identifiers of the corresponding sources of information (PubMed IDs) are provided. (XML) Dataset S3 Summary of the results of the main simulations performed in this work.
The xls file includes three sheets, erresponding to a model reduction. For each simulation, we report here the simulation ID (referenced in the main text and in the model files provided in Dataset S4), the perturbations performed (e. g. ''EGFRgain; p53loss'' indicates that EGFR was set to 1 and p53 was set to 0 throughout the simulation), the inputs considered and the initials states (asterisks denote all possible combinations of initial states). The attractor types (along with the number of corresponding states within parentheses, in the case of cyclic attractors) are further reported, as well as the corresponding component values in the attractor: 1 or 0 for stable values; asterisks for oscillating values. (XLS) Dataset S4 GINsim model files. For each model version (the original large model, and the three reduced versions), a file is provided in the format (zginml) that can be opened with the software GINsim (http://ginsim.
org/beta). Simulation parameters have been encoded to ease the reproduction of the experiments referenced in the main text. (ZIP) Table  S3  Reduced MAPK models. We considered three alternative reductions of the MAPK model (columns), each preserving the input and phenotype components. Additional components (Selected observables) were kept depending on the simulations performed. The last row lists components that were conserved because they turned out to be auto-regulated at some point during the reduction procedure. Such auto-regulations arise from the compression of longer circuits. (PDF) Table  S4  MAPK model documentation. Following a brief general description, all the components of the MAPK model (comprehensive version) are reported, along with their corresponding logical rules and annotations, including modelling hypotheses and links to the main sources of information (PubMed and HGNC databases).
Dataset S3). The resulting hierarchical transition graphs (see Methods) are composed by different classes of nodes, emphasising strongly connected components (blue), and linear (non circular) pathways (pink). The attractors reached are represented at the bottom of the figures. Attractor colours refer to the corresponding phenotypes: red for proliferation, green for apoptosis, grey for no decision. Stables states are denoted by rectangles, while cyclic attractors are denoted by circles. The accompanying tables give the composition of each node of the corresponding HTG. For instance, the node cc1 of the HTG obtained for FGFR3 activating mutation corresponds to a strongly connected component of the state transition graph. The number of states belonging to it (i. e. 24), as well as the list of these states are listed in the table (asterisks denote all possible values for the corresponding variable).
When PI3K is also activated (central), a proliferative attractor is reached, characterised by activated AKT. In contrast, when PI3K is not activated (left), the cell fails to make a clear decision at the level of the MAPK network. c) Attractors reached by the model in presence of receptor alterations, coupled with additional common deregulations observed in bladder cancer. Coloured circles denote the phenotypes characterising the attractors reached in each situation (we used the same colour code as in panels a and b, while empty spaces denote the loss of the corresponding branch in the state transition graph). Identifiers in rectangles (e. g. r3, r4, etc. ) point to simulation results reported in more details in Dataset S3 and Text S2. doi:10. 1371/journal. pcbi. 1003286. g004.
The research leading to these results has received funding from the  European Community 's  Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement nu  HEALTH-F4-2007-200767  for  APO-SYS (http://cordis. europa. eu/fp7/). It has been further supported by the  Agence Nationale de la Recherche, France  (project  MI2 iSA ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
If ERK is activated first (left and central branches), then p53 is inactivated. When PI3K is also activated (central), a proliferative attractor is reached, characterised by activated AKT. In contrast, when PI3K is not activated (left), the cell fails to make a clear decision at the level of the MAPK network. c) Attractors reached by the model in presence of receptor alterations, coupled with additional common deregulations observed in bladder cancer. Coloured circles denote the phenotypes characterising the attractors reached in each situation (we used the same colour code as in panels a and b, while empty spaces denote the loss of the corresponding branch in the state transition graph). Identifiers in rectangles (e. g. r3, r4, etc. ) point to simulation results reported in more details in Dataset S3 and Text S2.
Analysis of Red Blood Cell Morphology and Dimensions Cells were fixed with PBS-paraformaldehyde (1%) for analysis. Image acquisition and data analysis were done as described  [8]. Briefly, images were acquired on the ImagestreamH imaging cytometer (Ideas v4. 0, Amnis Corp. , Seattle, WA). At least 10,000 images were collected for each sample. Post-acquisition data analysis was performed using IDEASH image analysis software package (Inspire v4. 0, Amnis Corp. ). Morphological (circularity and aspect ratio) and dimension (projected surface area, diameter and perimeter) features of red cells (Pf-iRBCs or uninfected RBCs) were calculated using bright filter images of RBC by IDEAS software. Imagestream technology will estimate one half the total cell surface area (projected surface area), since one-side of the cell's image is captured.
This work was supported by the '' Fonds dedieCombattre les maladies parasitaires ,''  Sanofi Aventis-Ministere de l'Enseignement Superieur et de la Recherche-Institut Pasteur , the  Centre National de la Recherche Scientifique , and the  Institut Pasteur. I. S. was funded by a fellowship from the  Dim Malinf of the Region Ile de France. The research leading to these results received funding from the  European Union   Seventh Framework Program  ( FP7/2007-2013)  under grant agreement  242095  (Evimalar) and from the Project:  Mechanisms of erythrocytic Infection & Anemia in Malaria PI Kasturi Haldar , and the  National Institutes of Health  (Award Number  5P01HL078826-06 ). The ImageStream apparatus (Amnis) was acquired by a grant from the  Conseil de la Region Ile de France , program Sesame 2007 nu I-08-1090 -Imagopole.
Metabolite identification is a major bottleneck in metabolomics due to the number and diversity of the molecules. To alleviate this bottleneck, computational methods and tools that reliably filter the set of candidates are needed for further analysis by human experts. Recent efforts in assembling large public mass spectral databases such as MassBank have opened the door for developing a new genre of metabolite identification methods that rely on machine learning as the primary vehicle for identification. In this paper we describe the machine learning approach used in FingerID, its application to the CASMI challenges and some results that were not part of our challenge submission. In short, FingerID learns to predict molecular fingerprints from a large collection of MS/MS spectra, and uses the predicted fingerprints to retrieve and rank candidate molecules from a given large molecular database.
The fact that the same elemental composition and the same mass-to-charge ratio can arise from various different structures, as well as noise, adducts and fragments, hampers the identification of metabolites from MS data  [4]. Tandem mass spectrometry (MS/MS) facilitates metabolite identification by fragmenting the detected compound and measuring the m/z ratios of the resulting fragment ions. Querying measurement spectra against spectral reference databases  [5]  and manual curation by domain experts dominate the current approaches via MS/MS. The reference database method is reliable as long as the database contains the corresponding spectrum, and the query and reference spectra are measured with compatible, or ideally, identical mass spectrometers with closely matching operating parameters.
Due to the general similarity of spectra, misleading false positives can occur even if the database does not contain the correct spectrum. Indeed, conventional methods are often only able to identify a minority of the detected compounds, as low as 10%  [6]  to 30%  [7]. Unsurprisingly, a recent survey posed to MS experts found metabolite identification as the most important bottleneck in metabolomics today  [3]. To alleviate the shortcomings of the reference database methods, computational approaches to model the fragmentation processes have been undertaken. Current state-of-the-art methods are based on combinatorial algorithms and database searches. Computation of fragmentation trees is tackled with several approaches  [3, [8] [9] [10] [11]. The MetFrag software filters the compound databases by the precursor mass of the query mass spectra and for every candidates after filtering, the fragmentations are simulated and compared with the observed peak list  [10].
SIRIUS used the analysis of isotopic patterns to give additional data on the metabolite's elemental composition  [11]. Fueled by public mass spectral databases such as MassBank  [5] , the use of machine learning now represents a promising and so far under-utilized approach to improve the accuracy of metabolite identification and to decrease the burden of manual tuning of metabolite identification methods. A machine learning approach for metabolite identification through molecular fingerprints was very recently introduced  [12]. FingerID  [13]  relies on a two-step scheme. Instead of directly learning a mapping from the spectrum to the metabolite, a set of characterizing fingerprints of the metabolite is first predicted from its tandem mass spectrum using a kernel-based approach. The fingerprint prediction model is learned from a large set of tandem mass spectra obtained from the public mass spectral database MassBank  [5].
In the next step, the predicted fingerprints are matched against a molecular database such as KEGG  [14]  or PubChem  [15]  to obtain a list of candidate metabolites. The metabolite identification model is thus generalized to metabolites that are not presented in reference spectral databases. Due to the machine learning approach, data from any type of mass spectrometer is supported. In this paper, approaches used in the Critical Assessment of Small Molecule Identification (CASMI)  [16]  challenge, the first small molecule identification challenge in computational mass spectrometry community, are explained. In Section 2, the main methods underlying the FingerID framework  [12]  and the new user interfaces are presented. In Section 3 the experimental data and methods are described followed by results in Section 4.
Metabolite Identification through FingerID In this section, the FingerID metabolite identification framework  [12]  is described. An overview of the framework is shown in Figure  1. It consists of two main modules: • A molecular fingerprint prediction module that relies on support vector machines (SVM) equipped with a probability product kernel representation of mass spectra. • A molecule scoring and ranking module uses the predicted fingerprints to retrieve the best matching candidate molecules from a molecular database such as KEGG or PubChem. Scoring & ranking score(M ) = ! j y j (M )= ŷj ! " # $ (1%! j ) y j (M )& ŷj ! " # $ j=1 m ' ŷj (! ) = sign " ij y ij K(! i , ! ) + b j i=1 n ! " # $ % & ', (j = 1,. , m Support vector classifier { ŷ1 ,. , ŷm } Predicted fingerprints K(!, ! i ) = p ! (x)p ! i (x) R 2 ! dx Probability product kernel {! 1 ,.
Candidate Retrieval Given the predicted fingerprint vector corresponding to a tandem mass spectrum, the candidate molecules matching these fingerprints are retrieved from a molecular database such as KEGG or PubChem. As a preprocessing step, one needs to generate the true fingerprint vectors of each molecule in the database. In matching the predicted fingerprints to the observed ones in the database, it is sensible to give more weight to fingerprints that can be predicted reliably from the mass spectrum. To implement that idea, FingerID uses a probabilistic model that exploits the cross-validation accuracies (γ j ) m j=1 ∈ [0. 5, 1] m of the fingerprints as the reliability scores. Given the reliability scores and the predicted fingerprints ŷ, the model assigns the Poisson-binomial probability for the fingerprint vector y as follows: P P B (y|γ, ŷ) = m j=1 γ [y j =ŷ j ] j (1 -γ j ) [y j =ŷ j ] The above can be interpreted as measuring the likelihood of a fingerprint y to be the source generating the observations ŷ.
For a molecule M , the probability of its fingerprint vector y(M ) gives its score used in candidate retrieval: score(M ) = P P B (y(M )|γ, ŷ) It is useful to reduce the number of candidates by filtering the molecular database by the exact mass of the molecule. If the mass is not known, it is estimated from the MS2 or MS1 spectrum. A a small search window is set [µ M -∆, µ M + ∆] around the estimated mass µ M of the unknown molecule, and the records in the molecular database that exceed the allowed mass difference are filtered out. The size of the candidate set is obviously dependent on the width of the search window. A smaller width gives a smaller candidate set but has a higher risk to miss the true molecule, if the exact mass was estimated incorrectly. Based on the fingerprint scores in Equation (3), a ranked list is generated for the molecules within the mass window.
FingerID Web Server The FingerID web server  [19]  was initially built for tackling the CASMI challenges. The server provides easy access to user who wishes to try the machine learning framework for metabolite identification. The underlying database on the server is KEGG. A screen-shot is shown in Figure  2. The FingerID web server has two modes. In the query mode, the user can submit an http form to the server with the information related to the unknown molecule. The server will send back the result in a table. In the batch mode, the user can write the queries in pre-defined format files and compress them in a package. Then the user can upload the package to the server and the server will send back a package of results to the user. Both modes share the same set of input search parameters.
Exact mass is used in the beginning of database matching. Search PPM specifies the width of the mass window. It should be adjusted according to the assumed precision of the exact mass. Precursor is required for the mloss feature. Peaks are simply the list of masses and intensities of the MS/MS. Device type determines which trained model are used for molecular fingerprint prediction. The user should set the Device type according to his own mass spectrometer type. Mode tells the ionization mode of the mass spectrometer and it is useful in both estimating the exact mass and aligning the peaks.
FingerID Software Distribution When using the FingerID web server, the user can only use the trained model provided by the web server administrators. The FingerID package  [13]  allows the user to select his own training mass spectra to train the prediction models. The training spectra should be in MassBank  [5]  format. Model parameters and database search parameters are specified in a configuration file. Training process could be minutes to hours or even more, depending on the size of the training data. More detailed instructions can be found on sourceforge project home page 5 and in the readme file of the package.
Mass Spectral Training Data Due to the differences in the CASMI challenges we decided to train different fingerprint prediction models for the different setups. In particular, we define three variables for our datasets: instrument type, ionization type and fragmentation method. According to these variables, the challenge data can be categorized to 5 models, which are (  1  The summary of training data obtained from MassBank is listed in the first part (MS2) of Table  1. The molecules in Model (1) are a subset of Model (2), the molecules in Model (3) are a subset of Model (4). The molecules in Model (  5 ) are quite different, with at most 5 molecules overlapping with the other models. Models (1) and (  2 ) have 5 molecules also in Models (  3 ) and (  4 ). An auxiliary dataset named QqQ, which contains spectra of collision energies of 10 eV (491 molecules), 20 eV (502 molecules), 30 eV (502 molecules), 40 eV (490 molecules) and 50 eV (449 molecules), is used to analyze the effect of different collision energies on the fingerprint prediction.
Molecular Fingerprints OpenBabel  [21]  was used to generate molecular fingerprints, FP3 (55 bits), FP4 (307 bits) and MACCS (166 bits), all together 528 bits. However, in each dataset many of the fingerprints are either present in all molecules (+1) or absent (-1), which means they provide no information for training. We removed these ineffective fingerprints from each dataset. The number of the remaining effective fingerprints are shown in Table  1.
CASMI Challenge Results The FingerID results submitted to the CASMI contest is shown in Table  2. The model that was used for each challenge to predict fingerprints is also included in this table. Some challenges can map to several models, such as Challenge 13 where both CID and HCD data are available. In this case, the model with better cross validation performance was used, which is Model (4) LC-ESI-ITFT-HCD. The results for the molecule identification (Category 2) were computed first, and the chemical formula identification is directly taken from molecule identification. As a result, it is more meaningful to discuss the results of Category 2. Expectedly, molecule identification proves to be a harder problem than chemical formula prediction. As an overall finding, most of our failures in the challenges are due to the limited molecular database used for retrieval: the molecules in challenges 3,4,10,12-17 were not in our version of KEGG.
If the CASMI organizers took the rank of the score as criterion, this would have been the winning entry for this challenge. • In Challenge 6, half of the candidates had better scores than the correct one, which means the molecular fingerprint prediction was not perfect. However, comparing the absolute rank to the other participants, this was sufficient to win this challenge. • In Challenge 11, the highest intensity peak in MS1 is not the molecule with an adduct. Thus, the exact mass of the molecule was estimated incorrectly. The question of how the challenge molecules differ from the training molecules and molecules in the database is answered by comparing the fingerprints , as shown in Appendix A. A simple similarity function between fingerprints is defined and histograms of the similarity scores are presented.