dataesr/scientific-paragraphs-categorization huggingface dataset:
Netatmo Data Acquisition Air temperature (T) data from CWS sold by the company "Netatmo" (https://www. netatmo. com/) were used in this study. An automatic work-flow was set up to fetch T data from Netatmo CWS for the investigation period, i. e. , from July 1, 2016 until June 30, 2017. The setup has previously been described in  Meier et al. (2017)  and  Fenner et al. (2017) , in the following a brief summary is given. Measurements by the CWS are taken by two modules, an indoor and an outdoor module. The outdoor module measures relative humidity and T (specified accuracy ± 0. 3 K in the range -40 to 65 • C) at 5-min intervals, data are uploaded automatically to the Netatmo server via private Wi-Fi connection. If the user agrees, measurement data made by the outdoor module are shared online and can be obtained freely via an application programming interface (API) provided by Netatmo.
The "getpublicdata" method of this API was used to obtain T data for all available CWS in Berlin and Toulouse (Figure  1 ) at one-hourly intervals (instantaneous values). The returned JSON objects were parsed and data written into a local MySQL database. A consistency check was carried out and all CWS that returned data with an invalid date/time were omitted. Measurements were assigned to the next full hour (e. g. , all measurements between 10:00:01 h and 11:00:00 h assigned to 11:00:00 h) since the CWS do not measure at identical times. For Toulouse, data in the months July-September 2016 were acquired using the "getmeasure" method of the API. This method provides one-hourly average values for each CWS and allows the acquisition of CWS data in retrospect. One-hourly mean values differ from instantaneous values especially during the morning and afternoon hours, otherwise differences in the data obtained through the two methods are within measurement accuracy  (Fenner et al.
, 2017). It should be noted nonetheless that CWS data are instantaneous hourly values compared to hourly mean values for the reference networks (see above). For more details concerning the CWS data acquisition the reader is referred to  Meier et al. (2017). Note that the number of available CWS at each hour varies in time (Table  1 ), which is a unique characteristic of this data set. Let f be a function, then f (. ) denotes row-wise and f. column-wise application, i. e. , parentheses refer to the temporal, and angle brackets to the spatial domain. Further, a subscript like f (. ) d denotes the application for values in a given time range. The used ranges are d for days and m for month, i. e. , a matrix containing the daily minima of each station could be created by min (T) d. If no range is explicitly stated, the range is hourly.
Main QC Levels During level M1, the data is controlled by using the available meta data. Stations with equal longitude and latitude are set to NaN. These stations are assumed to have not been properly set up by the owner, which led to automatic location assignment based on the IP address of the wireless network  (Meier et al. , 2017). This is a unique feature of the Netatmo data set, but the filter could also be relevant for other CWS data sets. In level M2 a height corrected data set (T M ′ 1 ) is first calculated. This is done to account for the natural vertical variation of T due to the different elevations of the CWS stations. We use globally available elevation data from the Shuttle Radar Topography Mission (SRTM), version 4. 1 at 0. 000833 • spatial resolution (∼90 m at the equator)  (Jarvis et al.
, 2008)  and extracted the nearest neighbor pixel value for each station. Let this elevation be denoted as z then T M ′ 1 is calculated as: T M ′ 1 = T M 1 + 0. 0065 (z -mean z ) where 0. 0065 corresponds to the usual standard atmospheric lapse rate. It should be noted that this lapse rate might not be valid in special meteorological conditions such as strong radiation inversions during night-time. The lapse-rate adjustment is an optional setting in filter step M2, which could be omitted. This height correction has very little effect for the cities studied due to the overall relatively flat topography, but ensures the transferability of the method to other regions. As an alternative to SRTM data, the elevation information provided in the meta data of each CWS could be used in this step. However, it was decided to use SRTM data instead to ensure the transferability of the method to other crowdsourced data sets in the absence of elevation information, and since Netatmo owners can manually specify the elevation information, which could be faulty or mistaken for information regarding height above ground level.
The used implementation of Q n is from  Maechler et al. (2017). The z-score Z is calculated as: Z = T M 1 ′ -median T M 1 ′ Q n T M 1 ′ Values that would lead to rejection of the null hypothesis for α = 0. 05 on the upper tail and α = 0. 01 at the lower tail of the distribution are considered faulty and therefore masked. Formally: T M 2 i, j = T M 1 i, j , if -2. 32 < Z < 1. 64 T M 2 i, j = NaN, otherwise where i and j correspond to the time step and each of the CWS, respectively. We treat the upper tail stricter since  Meier et al. (2017)  showed that most common error sources in CWS data are related to indoor locations of stations or radiative errors in nonshaded areas, increasing T. It should be noted that for areas where only a small amount of CWS are available (i. e. n < 200) the use of the t-distribution for calculating the critical values would be beneficial.
In section Sensitivity Tests the robustness of the QC to different cut-of values of Z is investigated. In level M3, all data of a station within 1 month is removed if step M2 flagged more than 20% of the considered month. It is assumed that if too many values were removed by level M2, the station is too erroneous to be kept. The time frame of 1 month could be shortened if shorter time periods are to be investigated with CWS data. Finally, in level M4, the Pearson correlation coefficient (R) between each individual station and the median of CWS is calculated for each month. If the correlation is lower than 0. 9, the data in month m of the considered station j are set to NaN, such as: T M 4 i, j = T M 3 i, j , if R T M 3 , median T M 3 > 0. 9 T M 4 i, j = NaN, otherwise The indoor stations are targeted here, since it is assumed that they are less correlated to outdoor stations as their diurnal cycles are shifted in time or otherwise non-representative of outdoor environments.
EVALUATION OF THE QC AND DISCUSSION In the following, we will refer to the different data sets as: T REF for T measured by the reference stations, T RAW for raw T from CWS, T ME17 when the quality control method from Meier et al. (  2017 ) is applied to CWS data (after level D, being the highest in those procedures), and T QC when the method developed in this study is applied (after level O3). To evaluate the new QC, a qualitative evaluation is firstly carried out, followed by a comparison with the method developed by  Meier et al. (2017). Then, we propose two specific tests designed to evaluate the ability of the QC to identify the indoor CWS as well as the CWS affected by radiative errors. A sensitivity analysis of the main statistical parameters in level M2 of the QC is then carried out.
CONCLUSION AND OUTLOOK In this study we developed a QC procedure to automatically filter out potentially erroneous data from crowdsourced T measured by CWS. Even though the QC is statistically based, it addresses common error sources in crowdsourced T data as identified by previous works  (Chapman et al. , 2017; Meier et al. , 2017)  and effectively filters out data that are affected by such errors. Moreover, the QC does not need reference data from professionally operated weather stations but uses information from the crowdsourced data set itself. It is easily transferable to other urban regions. Thus, it provides a homogeneous data set that can be used for further analyses. Small deviations between quality controlled CWS and reference data were found when considering the spatial average of CWS in close proximity to a reference station.
However, single stations show considerable larger errors, highlighting the fact that analyses relying on data from single CWS are to be treated carefully. A positive MD remained in the quality-controlled CWS data set, notably for daily minimum T. This issue is likely related to the siting of the CWS (i. e. , close to walls) compared to the siting of standard meteorological observations at urban sites and could be investigated in detail in a future study. Despite the positive deviation, CWS data provide valuable information of T by capturing local-scale variations. These evaluations permit to justify the construction of a T map in the city of Paris using direct measurements. Apart from the positive MD mentioned above, it could be investigated in more detail whether or not CWS data are able to capture hot or cold spots in urban areas at a finer spatial scale (i.
ACKNOWLEDGMENTS The authors thank the owners of the Netatmo stations who shared their data. They also thank  Guillaume Poujol  from  CNRM , and  Hartmut Küster  and  Ingo Suchland  from the  Chair of Climatology  for setting up and maintaining the professional weather station networks in Toulouse and Berlin, respectively, funded by the  CNRM  and  Toulouse Métropole  and the  Technische Universität Berlin , respectively. DF is funded by the  Deutsche Forschungsgemeinschaft (DFG)  as part of the research project ' Heat waves in Berlin, Germany-urban climate modifications ' (Grant No. SCHE 750/15-1 ). TG is funded by the  German Ministry of Research and Education as part of the research programme 'Urban Climate Under Change ([UC] 2 ) ' (funding code:  01LP1602A ).
Click-it reaction was added for 30 min to locate EdU in order to detect cells in the S-phase. The drug screening was performed in two independent replicates for each cell line. Images were acquired using the IN Cell 2000 automated imaging system (GE Healthcare, Pittsburgh, PA) using a 10× 0. 45 NA objective (Nikon, Tokyo, Japan). For the BBBC021 dataset, details are available in Caie et al. 16.
A total of 36 measures were obtained using the IN Cell Analyzer Workstation 3. 7 software (GE Healthcare). Then the same normalization as that for the mesothelioma assays has been applied. For the BBBC021 dataset, Ljosa et al. 10  provided the raw features (453 features) as supplementary data. We followed the same feature processing as described in Ljosa et al. 10  for data normalization and the mean profile method. In more details, we averaged features of cells from the same well (four FOVs per well) in one feature vector. Then we computed the median of the feature vectors corresponding to the same drug compound condition (two to three biological replicates per condition). This procedure gives a unique feature profile for each drug-concentration condition.
Confusion Matrices MOA annotations for the BBBC021 dataset are available online at https://data. broadinstitute. org/bbbc/BBBC021/ and as supplementary data in Ljosa et al. 10  We used a leave-one-out cross-validation approach; that is, for a left-out compound at a particular concentration, we took its feature profile and determined the nearest-neighbor profile with respect to cosine distance. The predicted MOA for the left-out condition is decided to be the nearest neighbor's MOA label. To avoid overfitting when the nearest-neighboring profile is determined, we excluded other concentrations of the same compound.
Target Prediction The prediction task was performed with random forests as weak base classifiers. During training, we constructed a given number of decision trees  24  that we optimized at 30 after testing a range of values between 20 and 40. More precisely for each tree, a random subset of features is selected and the best split of input data points based on this subset is determined to construct the decision tree. To generate predictions, we built an ensemble classifier made of these random forests, with each forest constructed on one assay independently. For this step, we used the MATLAB machine learning toolbox: https://www. mathworks. com/ help/stats/classificationensemble-class. html. For a left-out compound, we summed the probabilities to belong to a particular class over all individual random forest classifiers and assigned the maximum class as the predicted class (top 1 prediction).
General Fluorescent Markers Seem Redundant for MOA Prediction We used the BBBC021 dataset  10, 16  where cells have been stained for DNA, actin, and tubulin as general markers of the cell phenotype, and treated with 38 drugs at two to three concentrations. An image processing step was performed to detect all individual cells and their nuclei. Following this step, a set of 453 features quantifying intensity, morphology, and texture were computed per cell on all channels using CellProfiler 17 (see Materials and Methods section). Similarly to Ljosa et al. ,  10  we used the guilt-by-association approach,  18  which associates an MOA to an unknown compound based on the similarity of their morphometric profiles. Such morphometric profiles were constructed by taking the average value of each feature for all cells of a given treatment, thus producing 453-dimensional vectors when features computed on all channel were considered.
Combining Cell Lines Improves Target Prediction We reused image sets of screens already performed by the Biophenics platform at the Curie Institute (Paris, France). They included nine patient-derived malignant pleural mesothelioma cell lines and one prostate cancer cell line. We selected those screens mainly because they were all performed with the Prestwick library. This library is a commercially available collection of 1200 Food and Drug Administration (FDA) and European Medicines Agency (EMA) approved drugs that have been extensively studied and for which we could retrieve the known targets through the ChemBank and DrugBank online databases (see Material and Methods). After removing the gene targets for which we had only one corresponding drug or not enough information, we ended with a total of 614 compounds Combining fluorescent markers does not significantly improve MOA prediction accuracy.
The above confusion matrices for MOA prediction were obtained using a leave-oneout cross-validation approach. The results are based on features from (A) only DAPI, (B) DAPI + actin, and (C) DAPI + actin + tubulin from the BBBC021 dataset. 16 The predicted MOA for the one-compound concentration condition is the MOA label of its nearest neighbor with respect to cosine distance between feature profiles, as reported in Ljosa et al. 10  Two to three concentrations of 38 drugs form a total of 103 conditions, each related to 1 out of 12 known MOA classes. A value in these confusion matrices gives the number of conditions of a given true label (specified in row) that is predicted as a label (specified in column). The color map is normalized such that each matrix row sums to 1. The accuracy is computed as the ratio between the number of conditions on the diagonal (correctly predicted) and the total number of conditions (103).
This observation, combined with the fact that the number of markers that can simultaneously be imaged is technically limited, led to the idea that instead of using multiple dyes on a single cell line, it could be more relevant to use multiple cell lines with few markers. Furthermore, this is precisely the type of screen that can be abundantly found in the screening history of a typical high-throughput platform Figure  2. Some MOAs can be distinguished by eye using DNA only. Selected images from each of the 12 MOAs from the BBBC021 dataset. 16 Here, only the DAPI channel is displayed. While it is obviously impossible for the naked eye to precisely sort these images by MOA, differences are clearly discernible in some of the images from the shape and texture. Scale 50 µm. facility. Accordingly, we gathered data to test this idea.
Such simple assays are usually cheap, and therefore abundantly available across research institutes and hospital screening facilities. Some previous work described methods where data from high-throughput screening assays stored on PubChem were aggregated to build compound biological fingerprints. 21 They were notably used for biological hit extension by phenotype similarity. However, that approach would not allow us to obtain information on previously uncharacterized compounds that are newly tested. In our approach, we focus on predicting the functions of drugs in order to prioritize a list of hits from HCS assays. We developed a machine learning framework that constructs a classifier from weak learners, each trained on individual assays. As random forest classifiers are independently trained on each assay, our approach can straightforwardly combine assays with a very different nature, number of markers, and quantitative features.
Acknowledgments The authors would like to thank  Didier Jean  for providing the nine cell lines used for the mesothelioma screens, and  Adele Soria ,  Aurianne Lescure , and  Sarah Tessier  for their screening technical assistance. We also thank  Shantanu Singh ,  Vebjorn Ljosa , and  Anne Carpenter  for helpful discussions;  Mary Ann Letellier  and  Don D. Nguyen  for valuable comments on the manuscript; and  Felipe Delestro  from the  Bioinformatics Platform of IBENS  for the conception and design of the figures.
Funding The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work has received support under the program " Investissements d'Avenir " launched by the  French Government  and implemented by  ANR  with the references  ANR-10-LABX-54   MEMOLIFE  and  ANR-10-IDEX-0001-02   PSL* Research University. Prostate screening was supported by the grant  INCAPAIR PROSTATE program n2010-1-PRO-03  from the  French National Institute of Cancer.
We propose a new method of regularization for network models of signaling pathways based on the local density of the inferred parameter values within the parameter space. Our method reduces the complexity of models by creating groups of cell line-specific parameters which can then be optimized together. We demonstrate the use of our method by recovering the correct topology and inferring accurate values of the parameters of a small synthetic model. To show the value of our method in a realistic setting, we re-analyze a recently published phosphoproteomic dataset from a panel of 14 colon cancer cell lines. We conclude that our method efficiently reduces model complexity and helps recovering context-specific regulatory information.
, 2006). Classically, dynamical systems like regulatory networks of mammalian cells are modeled with systems of ordinary differential equations, describing in detail the status of chemical species like proteins or membrane receptors over time. Alternatively, logical models  (Morris et al. , 2010; Hill et al. , 2012; Le Novère, 2015)  were introduced several decades ago for the modeling of regulatory networks  (Kauffman, 1969). As they are simpler in their formulation, they are easier to handle computationally, scale better to large models and datasets, and are easier to interpret. The prior knowledge used to construct logical network models frequently comes from reviewing the literature of a certain mechanism, disease or signaling pathway, and may be summarized in a database like STRING, Reactome or WikiPathways  (Joshi-Tope et al.
Network update is performed according to the laws of probabilities. There is, however, a number of impediments to successful biomolecular modeling. Firstly, the prior knowledge used to build the model could be inaccurate, or more frequently, incomplete, or both. In other words, compared to the true network, databases likely contain additional edges, as well as miss others. Secondly, the information contained in databases is often generic, collected across cell types, genetic backgrounds, and experimental conditions. Given an interaction graph and a series of contexts (cell types, patients), the task of determining which interactions are context-specific and which ones are context-independent rapidly becomes intractable. This task is however essential to reduce the model complexity, as overly complex models are prone to overfitting (thus less generalizable), computationally expensive, and might be less interpretable than simpler ones.
This strategy is however problematic for several reasons. Firstly, the number of possible subsets grows very fast with the number of variables, leading to the infeasibility of testing them all. Secondly, repeatedly optimizing a model structure using the same dataset violates the central assumptions of the F-tests or χ 2 -based statistics used for comparisons, which are designed to test a single hypothesis. Strategies like forward-selection, backwards elimination, or combinations of both are consequently affected by numerous problems, notably biased parameter estimation and artificially low p-values  (Harrell, 2001; Burnham and Anderson, 2002). Fitting an overspecified model first and clustering the parameters in a second step is not a sound method to achieve sparsity, as the parameter estimates might not be stable, resulting in inaccurate clustering.
Furthermore, the two objectives are not coupled, which is problematic: a small difference between the values of two parameters might or might not be supported by the data. It makes more sense to specify our assumptions about the distribution of the parameter values as part of the objective function. Regularization is a technique for adding prior information to a regression problem. It consists in adding to the loss function a function of the parameters alone. More formally, when attempting to learn the parameter set θ from dataset X = [x 1 , x 2 ,. , x n ] with a model M, the objective function O takes the form: O = f (M(X, θ ), X) + λg(θ ) (1) where f is the loss function, for example the sum of squared errors. The hyperparameter λ is used to balance goodness-of-fit with the regularization objective g(θ ).
Modeling Experiments Modeling experiments in this paper used the toolbox FALCON  (De Landtsheer et al. , 2017) , a Matlab-based versatile tool to contextualize logical models of regulatory networks. Briefly, FALCON uses a Dynamic Bayesian framework  (Lähdesmäki et al. , 2006)  in which Boolean operations are explicitly defined as arithmetic, continuous logical functions. FALCON emulates a Probabilistic Boolean Network with user-defined topology and uses experimental data from perturbation assays to optimize the weights of the network, which represent the relative activating and inhibiting influences of the network components with respect to the logical functions. For the large-scale analysis of biological data, we used a custom implementation of FALCON running on a high-performance computing platform which specifications are detailed in section 2.
3. 3. O = 1 n n i=1 (Y i -Ŷi ) 2 + λU(θ ) (9) where Y is the vector of measurements for the observed nodes, Ŷ is the vector of corresponding predictions and U(θ ) is the uniformity of the parameter set θ across contexts, as defined by Equations 5-8 above, with λ being a scalar that controls the relative contribution of the penality to the objective function. The code and data files used for both the synthetic model and the biological example are available at the address https://github. com/sysbiolux/FALCON. Additional driver scripts are provided in the Supplementary Materials.