dataesr/scientific-paragraphs-categorization huggingface dataset:
60 581 diverse proteins were described by the 3-D cavity descriptor FuzCav,  65  which is a vector of 4834 integers reporting counts of pharmacophoric feature triplets mapped to Ca-atoms of binding site-lining residues. The use of cavity 3-D kernels showed a clear advantage (F-measure 0. 66) over sequence-based descriptions (F-measure 0. 54) in predicting target-ligand pairings for a large external test set (>14 000 ligands, 531 targets), especially in local models. This difference seems to be even more pronounced for datasets with limited ligand data (<50 ligands). Likewise, a recent study by Subramanian et al. 66  described the superimposed binding sites of 50 (unique) kinases by molecular interaction elds derived from knowledge-based potentials and Schrödinger's Water-Maps. 162, 163 Also in this example a signicant improvement for 3-D methods (r 2 ¼ 0.
Their method inducts a bagged stepwise multiple linear regression model with a subset of 1387 protein-ligand complexes extracted from the PDBbind09-CN database. 170 Subsequently a new compoundtarget interaction descriptor based upon distance-binned Crippen-like atom type pairs was introduced. The best model outperformed commercially available scoring functions assessed on the PDBbind09 database and was able to explain 48% of the variance of the external set, providing a RMSE equal to 1. 44. Although similar methods had been previously proposed,  [171] [172] [173] [174] [175]  this was the rst study where a sufficiently large validation was accomplished to ascertain model's predictive power. Additionally, the implementation of bagged stepwise multiple linear regression (MLR) and PLS enabled the evaluation of the importance of ligand and target descriptors for the PCM model.
Similarly, a subsequent study reported the development of a scoring function based upon the CSAR-NRC HiQ benchmark dataset (http://csardock. org). 176 The best model exhibited acceptable statistics with a cross-validated R 2 ¼ 0. 55 and RMSE ¼ 1. 49. 176 Finally, Koppisetty et al. 177  were able to predict for the rst time ligand binding free energies where the enthalpic and entropic contributions for a given binding event were deconvoluted. Therein, the authors demonstrated the importance of including ligand descriptors (QIKPROP and LIGPARSE calculated in Schrödinger suite)  178  to the models in addition to 3-dimensional ligand-protein interaction descriptors. As demonstrated above, PCM overlaps with methods that are originally coming from the structure-based eld due to PCM describing in principle any method to relate ligand features and protein/target features on a large scale to an output variable of interest.
The best model predicted the sensitivity on the external (blind) test with a correlation between observed and predicted of 0. 64, while a value of 0. 61 was obtained when predicting the response on a tissue unseen by the model in the training phase. Recently, the authors have integrated PCM random forest models with conformal prediction for the large-scale prediction of cancer cell line sensitivity with error bars. 17,189 Compounds were described with Morgan ngerprints, whereas a total of 16 cell line proling datasets were benchmarked for their predictive signal. Gene expression data constantly led to the highest predictive power. Interestingly, the authors found statistically signicant differences in predictive power between PCM models trained on cell line identity ngerprints (inductive transfer knowledge between cell lines)  190  and cell line proling data, suggesting that the explicit inclusion of cell line information improves the prediction of cell line sensitivity.
Other potential PCM applications As reviewed above PCM has been applied in a wide range of drug discovery settings, yet more applications remain unexplored. The prediction of compound toxicity on cell lines (toxicogenomics),  [193] [194] [195] [196]  beyond the aforesaid cancer cell line collections, is also amenable to PCM. Recently, Kaggle, 197 a crowd-sourcing platform, hosted two competitions in the eld of chemoinformatic modelling. Two pharmaceutical companies, Boehringer Ingelheim and Merck, provided structureactivity relationship datasets to the community in order to nd the most predictive machine learning algorithms. The Merck challenge consisted of 15 datasets, each of which containing the bioactivities of a series of molecules on a different target. The winners of the competition applied restricted Boltzmann machines (deep learning).
198 Interestingly, the winning team noted that the similarity between the datasets (targets) could be exploited by inducting a single neural network with all datasets, which output a layer with een different units (neurons). On the other hand, Boehringer Ingelheim provided a dataset with 1776 compound descriptors. The response variable was binary, 0 corresponded to a compound not eliciting the expected activity whereas 1 corresponded to a compound showing activity. In this case, the highest predictive ability was obtained with model ensembles (random forests, gradient boosting machines, and K-nearest neighbors). ] [201]  As described in this review, a large variety of protein targets have been modelled using PCM. Beyond the modelling of the activity of compounds on targets of diverse nature, the interaction between nucleic acids and proteins is also amenable to PCM modelling.
In this context, Bellucci et al. predicted protein-RNA interaction based upon the physicochemical properties of both the polypeptide and the nucleotide chains. 202 owever, to date few studies have been published in this area. 50, 202 he usefulness of PCM in computational drug design has been extensively proven in silico (see Section 2. 7) and in prospective experimental validation. Nevertheless, there are a number of limitations that should not be overlooked. Publicly available bioactivity databases contain a non-negligible degree of experimental uncertainty,  [108] [109] [110] [111]  which should be certainly included in the modelling phase, as recently proposed by Cortes-Ciriano et al. 75  Similarly, intervals of condence for individual predictions should be reported, which can be calculated with algorithm-dependent approaches, e.
g. Gaussian processes,  75  or with algorithm-independent techniques, such as conformal prediction. 17, 189 n addition to being informative for biologists, these condence intervals constitute a valuable source of information about the applicability domain (AD) of a given model. 75 The AD is dened as the amount of ligand and target space to which a given model can be reliably applied. Thus, in addition to the model validation schemes presented above, an estimation of model AD should accompany any reported model in order to be of practical usefulness. Another limitation which is oen inherently related to bioactivity data is that of data skewness. Some datasets mostly report active  203  or inactive molecules,  204  and thus compoundtarget combinations untested experimentally are normally considered as inactive or active interactions, respectively.
Moreover, public data in general tend to favor a relatively small number of proteins classes that have been extensively explored (e. g. 5] 205  As such, for some targets the available data might not be sufficient for PCM projects given that imbalanced datasets can lead to models with high negative or false positive rates. Nevertheless, the modelling of cell line sensitivity has shown that PCM displays high interpolation power, as the accuracy of prediction reached a plateau when 20% of the whole compound-cell line matrix was included in the training set. 29 eyond the quality of the data, the descriptor choice still constitutes a eld of active research, specially with respect to protein descriptors, which development will deeply inuence the success of PCM in the coming years. 45 A recent paper by Brown et al.
190  suggested that PCM mostly relies on inductive transfer knowledge and that protein descriptors mostly act as labels and do not account for structural differences among them. However, we have recently shown that both amino acid descriptors and cell line proling datasets account for structural information of eukaryotic, mammal and bacterial DHFR, and cancer cell lines, where the difference in performance on the test set between inductive transfer and PCM models was statistically signicant. 17, 56 CM requires the concatenation of ligand and target descriptors, and sometimes also cross-terms, which substantially increases the dimensionality of the input space with respect to QSAR. Although this higher dimensionality might lead to overtting in PCM,  206  in practice, PCM has been shown to exhibit higher predictive power on the test set than QSAR.
Acknowledgements ICC thanks the  Pasteur-Paris International PhD Program  and  Institut Pasteur Paris  for funding. QUA thanks the  Islamic Development Bank and Cambridge Commonwealth Trust  for funding. VS thanks the  Finnish National Doctoral Program in Informational and Structural Biology  for organizing graduate studies and  Helsinki University Research Foundation  for funding. API and EBL thank the  Dutch Research Council (NWO)  for nancial support (NWO-TOP # 714. 011. 001 ). OML is grateful to  CONACyT  (no. 217442/312933 ) and  Cambridge Overseas Trust  for funding. TM thanks the  Institut Pasteur Paris and CNRS  for funding. GJPvW thanks EMBL (EIPOD) and  Marie Curie (COFUND)  for funding. AB thanks Unilever and the  European Research Commission  (Starting Grant  ERC-2013-StG 336159 MIXTURE ) for funding.
Isidro Cortés-Ciriano  received a MSc in Biology and a MSc in Biochemistry from the  Qurrat Ul Ain  is an  IDB-CCT (Islamic Development Bank-Cambridge Commonwealth Trust ) scholar for PhD in University of Cambridge since 2012. Her research focuses on bioactivity modelling approaches. She received her BS (HONS) in Bio-aer an internship at Johnson and Johnson (Belgiumhe leads a research group comprising about ca. 20 members performing research on various aspects of chemical and biological data integration and analysis. He received his PhD from the  University of Cambridge  in 2005, and returned aer a Presidential Postdoctoral Fellowship with Novartis in Cambridge/.
317 human dataset 214 kinases (12 046) CSAR-NRC 346 protein- HiQ 176 ligand complexes Gold standard 313 diseases set (1933) (OMIM) 216 Sc-PDB 218 (2882) 581 targets GLIDA 317 GPCRs database 119 and 143 (5207) and GVK kinases kinase database (15 616) d , K This journal is © The Royal Society of Chemistry 2015 Med. Chem. Commun. , 2015, 6, 24-50 | 27 Review MedChemComm Open Access Article. Published on 07 October 2014. Downloaded on 02/10/2017 08:51:55. This article is licensed under a Creative Commons Attribution 3. 0 Unported Licence.
The HyMeX program The Mediterranean region is regularly affected by heavy precipitation often causing devastating flash floods. Floods and landslides in the Mediterranean basin cost lives and lead to expensive property damage. Improving the knowledge and forecast of these high-impact weather events is a major objective of the HyMeX program  (Ducrocq et al. , 2014). As part of this 10-year program, the first Special Observation Period (SOP1) HyMeX field campaign was conducted during 2 months from 5 September 2012 to 6 November 2012 over the northwestern Mediterranean Sea and its coastal regions in France, Italy and Spain. The instrumental and observational strategy of the SOP1 campaign was set up to document and improve the knowledge of atmospheric processes leading to heavy precipitation and flash flooding in that specific Mediterranean region.
A large battery of atmospheric research instruments were operated during the SOP1 including, among others, mobile weather Doppler and polarimetric radar, airborne radar, in situ microphysics probes, lidar and rain gauges  (Ducrocq et al. , 2014; Bousquet et al. , 2014). These measurement platforms were deployed at or near super sites where dedicated research instruments are gathered to document specific atmospheric processes  (Ducrocq et al. , 2014). The research lightning sensors operated during the HyMeX SOP1 were located in the Cévennes-Vivarais (CV) area in southeastern France. Additionally, various operational weather forecasting models were used as detailed in  Ducrocq et al. (2014). The HyMeX program  (Ducrocq et al. , 2014)  and its intensive observation period of autumn 2012 was an interesting opportunity to implement multi-instrumental observations for documenting the various processes related to electrification of thunderstorms in a region prone to thunderstorms and high-precipitation events.
Slow antennas Two solar-powered slow antennas were deployed to measure the electrostatic field changes from lightning in the SOP1 area. One SLA was deployed a few tens of meters from the MBA/MPA (see Sect. 3. 2. 3) near the Uzès airfield, and the second was deployed near the HyLMA station at La Grande-Combe airfield. Each SLA consisted of an inverted flat-plate antenna connected to a charge amplifier with a 10 s decay constant. The output of the charge amplifier was digitized at a rate of 50 000 samples per second with a 24 bit A/D converter synchronized to a local GPS receiver, and the data were recorded continuously on SD cards.
ctbto. org). The microphone is an encapsulated BK4196 microphone. Its sensitivity is about 10 mPa through a band pass of 0. 1-70 Hz. In order to minimize the noise due to surface wind effects, each sensor is connected to a noise-reducing system equipped with multi-inlet ports (eight for the microbarometers and four for the microphones) that significantly improve the detection capability above 1 Hz. To further reduce the wind noise, microbarometers were installed under vegetative cover (i. e. , pine forest). The signal from these sensors was digitalized at 50 Hz for the MBA and 500 Hz for the MPA. The dating was GPS tagged. Data were stored on a hard disk. No remote access was possible during the SOP1. To avoid power blackouts, each measurement point was supplied with seven batteries. Those batteries needed to be recharged in the middle of the campaign, meaning that the MBA and MPA were unavailable from 9 to 12 October.
The data from each sensor of the arrays were compared using cross-correlation analysis of the waves recorded. The azimuth and the trace velocity were calculated for each detected event when a signal was coherent over the array. Using the time of the lightning discharge and these parameters, a 3-D location of acoustic sources generated by the thunder was possible (e. g. ,  MacGorman et al. , 1981; Farges and Blanc, 2010; Arechiga et al. , 2011; Gallin, 2014). Gravity waves generated by thunderstorms  (Blanc et al. , 2014)  could also be monitored by MBA. When a convective system goes over an array, a large pressure variation was measured.
The field mills used at three of the stations were Previstorm models from Ingesco Company and were initially used by  Montanya et al. (2009). The measurement head is oriented downward to avoid rain disturbances and is fixed at the top of a 1 m mast that reinforces the electrostatic field on the measuring electrode. The measuring head of the fourth field mill was orientated upward and flush to the ground thanks to a hole dug in the ground. The field mills were calibrated to zero by using a shielding and by considering the fair weather conditions that correspond to the theoretical value of 130 V m -1. The data from each sensor were recorded with a time resolution of 1 s. This time resolution readily revealed the major discontinuities in the electrostatic field caused by the lightning flashes without the distracting effects of much faster individual processes within a flash.
If the drop collides with the induction cylinder, the pulse signal exhibits a slow exponential decay (MacGorman and  Rust, 1998)  that is easily recognizable in the data post-processing. In this case, the raindrop charge that is fully transferred to the induction cylinder is determined by a specific calibration. The charge measurement sensitivity ranges from about ±2 to ±400 pC. Furthermore, the charge signal duration at mid-height can be used to determine the size of the charged raindrops, providing the relationship between size and fall velocity is a function of the actual temperature and pressure  (Beard, 1976). Such a measurement provides key information on the electric charge carried by the rain at the ground to validate numerical modeling. It documents the spectrum of charged drops and helps deduce the proportion of charged drops within the whole drop population by comparing its spectrum with the one measured by a disdrometer.
The VFRS was transportable with a car and independent of any external power supply. A detailed description of the used VFRS can be found in  Schulz et al. (2005)  and in  Schulz and Saba (2009). For the typical observations during SOP1, the VFRS was operated in the manual trigger mode using an adjustable pre-and posttrigger. To ensure capturing the entire lightning discharge we typically recorded 6 s of data with 2 s of pre-trigger data per observed flash. During some storms (e. g. , low-visibility conditions) the VFRS was operated in the continuous recording mode. Due to memory limitations we only recorded the electric fields in continuous recording mode. All observation days during SOP1 were chosen based on weather forecasts with sufficient thunderstorm risk over the region of interest. As the real situation could be different to the forecast scenario -e.
Instrumentation during EOP and LOP The only instruments operated so far during EOP and LOP are the OLLSs due to their operational design. For instance, ZEUS observations are continuously delivered in real time to the HyMeX LOP website, while EUCLID and ATDnet produce daily maps of the lightning activity over the Mediterranean basin that are delivered to the HyMeX database. During spring 2014, a network of 12 LMA stations was deployed permanently in Corsica to contribute to the HyMeX LOP efforts in that specific region of the Mediterranean Sea.
The analyses focus not only on HyMeX SOP1 priority cases  (Ducrocq et al. , 2014)  but also on non-SOP1 events as HyLMA data cover June 2012 to the end of November 2012. The analysis will eventually provide key lightning-related indexes to describe the electrical nature of thunderstorms in southeastern France and which will be used in multi-disciplinary studies carried out within HyMeX. The combination of HyLMA and OLLS records will provide a set of basic products -e. g. , flash rate, flash type, flash properties and flash density -to populate the HyMeX database. The HyMeX case studies are not only observationally oriented but are also intended to provide material for verification and validation of kilometer-scale electrified cloud simulations (e. g. ,  Pinty et al. , 2013). Indeed, successful simulations are already performed and comparisons of simulated and observed parameters -e.
g. , vertical distribution of the charge regions, flash location, flash rate and flash extension -are already showing promising results. The HyLMA data should then help to identify objectively which non-inductive charging process treatment ("Takahashi" versus "Saunders") leads to the best simulation results. An objective debriefing of SOP1 preparation, operation and data analysis will be performed in the near future to identify the successes and the failures. This will help us to refine the preparation of a dedicated atmospheric electricity field campaign in early autumn 2016 over Corsica because a permanent LMA was established there in May 2014 for a minimum of 5 years. Another region of interest is the eastern Mediterranean Sea during autumn, when electrical activity takes place over the sea but ceases when the thunderclouds are landing.
Acknowledgements. This project was sponsored by grants  MISTRALS/HyMeX  and  ANR-11-BS56-0005 IODA-MED. LEFE-IDAO ,  Université de Toulouse  and the  GOES-R visiting program  also supported the  PEACH  project during its preparation and the field campaign. The Greek contribution to PEACH objectives is partially funded by the  TALOS  project , in the frame of "ARISTEIA II ", by the  Greek General Secretariat for Research and Technology. We are grateful to  R. Blakeslee  and  NASA  for lending the MSFC LMA during the SOP1. We would like to thank the team of the local Météo-France weather office in Nîmes for its strong support during the site survey, deployment, operation and dismantlement of the instruments and for letting us deploy four of the HyLMA stations on Météo-France land. We also thank  Mr.
INTRODUCTION MicroRNAs (miRNAs) are small single stranded noncoding RNAs (21-22 nt) found in eukaryotic cells. MiRNAs function by regulating gene expression either by translational inhibition or message degradation, and thus are key to many biological processes. Dysregulation of miRNAs is known to cause a wide range of diseases (miR2Disease database  Jiang et al. 2009 ) such as hereditary progressive hearing loss  (Mencía et al. 2009) , growth and skeleton defects  (de Pontual et al. 2011) , various cancers  (He et al. 2005; Mraz et al. 2009 ), heart diseases  (Thum et al. 2007) , and Alzheimer's disease  (Maes et al. 2009). Mature miRNAs are made from precursors (pre-miRNAs) of ∼90 nt in length, characterized by a hairpin-like structure. More than 18,000 miRNAs have been discovered in ∼140 species, of which >1500 are in Homo sapiens  (Kozomara and Griffiths-Jones 2011).
We evaluated the method on human and cross-species data. The positive data sets were taken from miRBase (version 18)  (Kozomara and Griffiths-Jones 2011) , followed by a refinement. For the negative ones, firstly, we generated, from exonic regions of protein-coding genes, sequences that could be folded into hairpin-like structures and verify several pre-miRNA characteristics. Secondly, we took the noncoding RNAs that are not pre-miRNAs from different databases. The evaluation was conducted through a fivefold cross-validation and a prediction on novel pre-miRNA and non-miRNA sequences. With the boosting technique, using the appropriately selected features, our method has shown favorable performance in comparison with state-of-the-art tools, on both prediction results as well as on running time.
The software miRBoost is available on our web server http://EvryRNA. ibisc. univ-evry. fr. The paper is organized as follows. In the two next sections, we present and discuss the results obtained with miRBoost compared with several existing pre-miRNA classification methods, in terms of classification performance, sensitivity and specificity in predicting new pre-miRNAs, and execution time. Then, we describe the materials and methods, including the human and cross-species training and prediction data sets we use, our feature selection process, and the algorithm. The existing tools that we compared with miRBoost are detailed afterwards.
Predictive sensitivity and specificity on new sequences We evaluated the predictive sensitivity of miRBoost and the other methods on 690 novel pre-miRNAs from the difference of miRBase versions 19 and 20 and version 18, and the predictive specificity on 8246 non-miRNA sequences (see "Data sets" section). We applied the model trained on the whole data set of cross-species for the prediction, with 18 features identified on this set through our feature selection process (see Pre-miRNA Feature Selection). Similarly, the models of CSHMM, triplet-SVM, and microPred were trained on that whole data set. We also compared with MiPred, HeteroMirPred, and mirExplorer, for which we used the provided models, as they do not allow retraining of their models with other data. The predicting results are given in Figure  2  and Supplemental Data 1.