dataesr/scientific-paragraphs-categorization huggingface dataset:
FRANK also generates stable or oscillatory gene expression directly produced by the simulated gene regulatory networks. The development of FRANK leads to important general conclusions concerning the design of large and stable gene regulatory networks harboring scale free properties (built ex nihilo). In combination with supervised (accepting prior knowledge) support vector machine algorithm we (i) address biologically oriented questions concerning our capacity to accurately reconstruct gene regulatory networks and in particular we demonstrate that prior-knowledge structure is crucial for accurate learning, and (ii) draw conclusions to inform experimental design to performed learning able to solve gene regulatory networks in the future. By demonstrating that our predictions concerning the influence of the prior-knowledge structure on support vector machine learning capacity holds true on real data (Escherichia coli K14 network reconstruction using network and transcriptomic data), we show that the formalism used to build FRANK can to some extent be a reasonable model for gene regulatory networks in real cells.
INTRODUCTION Gene regulation plays a key role in the control of fundamental processes in living organisms, ranging from development, to nutrition and metabolic coordination. Genes are regulated at several levels of integration but one key step is the control of gene transcription. 2] [3] [4]  Understanding GRNs has tremendous implications ranging from medicine to agriculture. Indeed, being able to learn GRNs may enable manipulating the cell as a system and potentially control and coordinate many physiological events that are related to GRN activity (diseases, biotechnological applications, crop production, and more). The quest of systems biology is thus to determine GRN structure using machine-learning algorithms applied on transcriptomic datasets [considered as the most exhaustive level measurement of the system to date (commonly assayed by microarrays or next generation sequencing)].
Furthermore, recent high throughput experimental approaches are now drafting the GRNs backbones for many different species,  5  ranging from prokaryotes, yeast, plants to humans. We can distinguish two complementary types of approaches. 4] [15] [16]  In these cases, one aims at investigating the binding activity of a particular TF across the genome or its capacity to activate its targets upon entrance in the nucleus. 8] [19] [20] [21] [22]  These approaches, TF-centered and target-centered, can be understood as the closest proxy to experimentally determining actual GRN in living organisms. 23 nterestingly, these experimental data on GNR connections are often used to validate algorithms predictions, but it can also be used as potential knowledge to train machine-learning procedures. 24 Hence, the purpose of the current work is to understand: (i) how valuable is this GRN prior-experimental-knowledge, (ii) which characteristics of this prior-knowledge are potentially better in training or supervising machine-learning procedure to learn large GRNs from transcriptomic data? In other words can we, in the near future, possibly train algorithms to decipher real regulatory connections by combining ChIP-seq, DAP-seq, eY1H, or TARGET results with transcriptomic datasets? Since no GRN is known with sufficient precision to be used as gold standard, we undertook a reverse engineering path.
30, 31   ut in our experience, their simulating engine based on ordinary differential equations (ODEs) resolution is quite slow when solving very large network dynamics and steady states. We thus undertook (i) a different and simpler formalism to routinely simulate and infer large networks, and (ii) to answer very biologist-driven questions. In this work we use FRANK to simulated GRNs and related gene expressions and use machine learning algorithms to learn back the simulated network structure to benchmark the quality of the reconstruction. However, instead of studying the machine learning algorithms themselves, we rather focused on the impact of the structure of the network, as well as the characteristics of the data needed to perform good reconstruction. In this sense, our work is a very much biologically oriented and proposes mathderived hypothesis to answer the following questions: To what extent prior-knowledge of a given GRNs would be able to improve machine-learning procedures? What amount of prior knowledge is needed to properly infer a GRN of a given size? Which kind of expression data (dynamic, steady state, mixed) are the most valuable to infer a given GRN? Which kind of prior-knowledge (TFcentered or Target-centered) would be best suited to supervise inference of GRNs? What proportion of TF or target gene expression are needed to properly infer GRNs? Are machine learning procedures resilient to bad quality prior knowledge in inferring GRNs from it? Herein, we propose answers to these questions derived from our in silico simulations.
SVM are another popular method for classification by machine learning. 38, 39   onsider a two class problem and suppose that we are given a training dataset {(X 1 , y 1 ),(X 2 , y 2 ),…,(X n , y n )} where X i is a vector in R p and y i is either -1 or +1 depending on the class X i belongs to. We can state first the mathematical setting in the simplest framework. Imagine that the training dataset may be perfectly separated by a hyperplane (Sup. Fig. 1 [SvmPlot. pdf]) the sample depending on two variables x 1 and x 2. For red triangle points the class is (y = -1) and for blue circles the class is (y = +1). Here the SVM computes the equation of the straight line that splits the two groups in an optimal way. Optimal here means that the corridor (dotted lines) around the straight line is the largest possible.
The four steps above tend to remove the sparsity in the learning data at the expense of a serious decrease of the sample size. Clearly the proportion of 50% zeros/50% non-zeros may be tuned to other values. This somewhat unusual though pragmatic change turns out to enhance predictions and meet the goals of increasing the true positive rate (Sup Fig. 2 ). Part II: Biological insights using FRANK and SVM Benchmarking the role and the characteristics of prior knowledge and transcriptomic data to improve supervised machine learning of GRNs. In Part I, we defined FRANK (Fig. 1 ) as a rapid and effective large network simulator. FRANK provides (i) network modules having the characteristics that are observed in eukaryotic systems (Figs. 1  and 2 ) and (ii) simulated gene expression in a large number of conditions (Fig.
coli K-12. We retrieved the network from the database http://regulondb. ccg. unam. mx/, (Regulon v9. 3) and several thousands of transcriptomic data points (http://m3d. mssm. edu/norm/) as described in Fu et al. work. 43 The network was recoded to be compatible with our pipeline of analysis. We built a N matrix with -1. 0 + 1 influences. The reformatted network contains 171 TF, 1493 TA, connected by 4195 regulatory edges (Fig. 5a ). Following the same pipeline of analysis as before on simulated data (Fig. 4  for example), we ran SVM on real network and real transcriptomic data (Fig. 5b ). Despite the fact that here, we used the entire transcriptomic dataset and that the provided network may still holds imperfections, we observed that TA-oriented prior knowledge is indeed more useful than TF-oriented prior to train SVM (Fig.
5b ). We applied bootstrap techniques in order to evaluate the fact that % of True positive are systematically higher than the % of False positive in the case of TA-oriented prior knowledge, as reported by VUS (Fig. 5c ). This demonstrates that prior structure influences SVM learning even on real datasets. In conclusion, from simulation (Fig. 4 ) and from real network and transcriptome data (Fig. 5 ), we believe that for the same amount of data points the eY1H or similar TA oriented approaches are likely more powerful to train machine learning than TF oriented techniques. Of course, we believe that having more data using an increasing number of real gene expressions (nb experiment; y-axis) and an increasing fraction of the network as prior knowledge (alpha; x-axis). Percentage of true positive and false positive are evaluated based on predictions of the presence of an edge and its positive or negative influence.
c Bootstrap results for ten learning processes as the one described in b. For each bootstrap cycle 90% of the network has been resampled and genes expression randomized. The panel represents the boxplot of VUS for the ten learning procedures points will always improve learning when this will be applied to real datasets and that mixed (TA and TF-oriented) prior knowledge will finally be used to decipher GRNs. But if one needs to invest in having more data to supervise GRN machine learning, our results show that it might be more helpful to carry out TA oriented techniques. The first steps in gene expression preceding stable regime contain the information needed to learn GRNs. In this part, we also evaluated the kind of gene expression that contains the more information to best learn GRNs using prior knowledge.
Indeed, FRANK uses an iterative process to generate gene expression data (See part I, Fig. 1 ). Here, this iterative process starts with the randomization of the gene expression E 0 at step 0. Then the model is applied once to reach E 1 (expression of the genome at step 1). Here, we can distinguish between two ways of simulating genome expression. The first one is named "multistart" (close to Monte Carlo in spirit), where the above process is repeated n times by sorting out a new E 0 each time. The second one is named "dynamic", where E 1 is used to reach E 2 and so on and so forth up to E n (this progression is the one being displayed in Figs. 2c, d, 3b ). These two concepts will be used right below. To understand what are the characteristics of the transcriptome that may contain the most information for GRN learning, we decided to build gene expression datasets containing values of multistart process reaching the step E n (Fig.
6 ). For instance for n = 10 the experiments provided to the SVM learning are a compilation of E 10 genome expression for many different E 0. What has been observed here is that the smallest increments in gene expression are the most useful to learn the network. Indeed, Fig. 6a  shows that the learning capacity of a supervised SVM is clearly more efficient if one uses E 1 instead of E 10. This is clearly exemplified by the VUS progression in response to increasing n values presented Fig. 6b. Indeed, we recorded a very marked decrease in the SVM capacity to learn the GRN from n = 1 to n = 10 that is manifest at the same time because of a decrease in the true positive as well as an increase in the false positive (Fig. 6b ). This means that the most useful information in the transcriptomic dataset for GRNs learning lies in the fast response following gene expression perturbation.
When the gene expression reaches its steady state it would be very difficult to learn the underlying GRNs. This is a pretty intuitive result but we believe that our approach provides a clear measurement and simulation of such phenomenon. Starting from this above observation, we wanted to simulate gene expression that may resemble more to the dynamics that are provided by real transcriptomic datasets. In other words, it is quite unusual to perturb cellular networks and harvest a particular time point many times (such as in Fig. 6 ). 5] [46] [47]  This means that perturbation is applied once and then samples are harvested across time. This is what we wanted to explore next. Thus, in Fig. 6  we performed dynamics of different sizes and measure what is the most useful to train supervised SVM. Again we observe that dynamics can be used to solve the network with a pretty good accuracy (Fig.
Simulated gene expression data kind: dynamic (E 1 to E n ); prior knowledge data kind rows 8 Target genes (TA) are important for the accurate reconstruction of the network. a Progression of the volume under the surface computed for a progression in the number of TA for FRANK generated networks having a constant number of 100 TF. b Progression of the volume under the surface computed for a progression in the number of TA for FRANK generated networks having a constant number of genes (TA + TF = 1100). Red, % of True positive and blue, the % of false positive predicted connections in the network. Transcriptomic data kind: A 1 ; prior knowledge data kind rows. c Example of surfaces exploring the SVM accuracy (% of true positive on left and % false positive in right panels) to predict the connections in a FRANK generated network (25 TF, 1075 TA for the top 2 surfaces and 250 TF, 850 TA for the bottom two surfaces) using an increasing number of simulated gene expressions (nb experiment; y axis) and an increasing fraction of the network as prior knowledge (alpha [rows are used as prior knowledge]; x-axis).
Percentage of true positive and false positive are evaluated based on predictions of the presence of an edge and its positive or negative influence. Simulated gene expression data kind: A 1 observe during transcriptomic analysis following treatments: genes are regulated and sometimes display an overshoot which then reaches a stable state. In nature this overshoot in gene expression can be observed within ~20 min with a stability phase happening within a couple of hours. 44 This can vary according to the biological model, the perturbation of the network, and the GRN studied. This means that E 20 (Figs. 2c, d ) is likely to correspond to hours of treatment. Consequently we evaluate the simulated step in our iteration process to be equivalent to ~5-6 min of cell response (120 min/20 steps). But this constitute a rough calibration that will deserve further work.
When it comes to study of our capacity to learn the GRN by using supervised SVM, we wanted to draw some general conclusions that we believe can help to design future machine learning procedures on real datasets. We found that (i) short dynamics are more powerful to teach machine learning algorithms than longer ones (Figs. 6, 7 ), (ii) prior knowledge greatly helps SVM to define real underlying GRNs even if it contains errors (Figs. 1-10), (iii) prior knowledge is far more efficient when it represents Target-oriented results (Figs. 4  and 5 , such as Y1H for instance), (iv) studying the whole network connectivity in particular by studying a lot of passive genes (TA) is important for the learning process (Fig. 8 ). From these observations, even if the cell system is over-simplified in our model, we really believe that by using this approach to teach the machines the actual true connections in the network, we will be able to accurately reconstruct GRNs in a near future.
ACKNOWLEDGEMENTS The authors wish to thank  Dr. Sandrine Ruffel ,  Dr. Stéphane Mari  and  Dr. Milos Tanurdzic  for their feedback on the manuscript. This work has been supported by the  CNRS PEPS-BMI (SuperRegNet)  and the  Labex Numev  to G. K. and A. M. that funded C. C. post-doc.
MTE-GRASS model In order to estimate monthly gridded global grassland GPP, the model tree ensemble (MTE) approach, which is a machine learning algorithm, was applied. Model trees were developed from, but has proven superior to, classification and regression tree  (Wang & Witten, 1997; Vens & Blockeel, 2006; Jung et al. , 2009) , given that target variable (GPP in this study) is predicted by a set of multiple linear regressions from explanatory variables. Each regression model is only valid within certain conditions defined by a model tree. In general, two steps are required to produce global GPP from flux tower local measurements: (1) The MTE is trained against eddy covariance GPP estimates at site level using satellite-derived fraction of absorbed photosynthetically active radiation (f PAR ) and meteorological data as explanatory variables and (2) using gridded information about all these explanatory variables in combination to the trained MTE GPP to predict global GPP pattern.
Data processing First, the observed GPPs with different time resolutions (30 min, 1 h, and weekly) from eddy covariance flux tower (www. fluxdata. org), which vegetation type is grass according to International Geosphere-Biosphere Program definition (IGBP GRA), were aggregated to monthly values. Then, they were used to parameterize the models together with the drivers either obtained at the site (precipitation and temperature) or extracted at the flux tower location (gridded data). In total, 30 explanatory variables of five general types from 45 stations (1416 site-months) were used to train MTE (Table  1 ). These data were randomly divided into five groups (four for training and one for test). Here, we evaluated the performance of MTE based on fivefold cross-validation. The observed GPP for each of these five groups was predicted based on the training using the remaining four groups.
Global grids of annual grassland management intensity dataset (0. 5°9 0. 5°) used for training were from Chang Jinfeng (personal communication) (Fig. S1 ). This dataset includes the proportions of extensively as well as of intensively managed grasslands (grazing and cutting) from 1982 to 2011, constrained by the total forage requirement of grass-fed livestock numbers. More details about this dataset, including the calculation of the forage requirement and the fraction of animals that receive complementary crop feed products, can be found in  Chang et al. (2015a). The management intensity data at each flux tower site used for training are not available; alternatively, we extracted these data at each grid where these sites are located.
Site selection for training Grassland GPP observations at site level were derived by eddy covariance measurements from 45 flux sites (1416 sitemonths) (Fig. 2 ), spanning years from 1998 to 2006. These observed data can be obtained from the open access flux database (www. fluxdata. org). The data were processed with standard methodologies, including quality filtering and partitioning of NEE into the two components (GPP and respiration)  (Anav et al. , 2015). Details about the geographical and climate information of these sites are listed in Table  S2.
Other data Vegetation-related variables (f PAR ) with a spatial resolution of 1/12 degree and a temporal resolution of 15 days during the period 1982-2011 are obtained from  Zhu et al. (2013). Similar to management data, we extracted grassland f PAR data at each grid where flux sites are located. World map of K€ oppen-Geiger climate classification, which is one of the most widely used climate classification systems, was obtained from  Kottek et al. (2006). This classification combines average annual and monthly temperature and precipitation, and the seasonality of precipitation  (Mcknight & Hess, 2000). It further divides climates into five main groups, namely tropical, arid/semiarid, temperate, continental, and polar/alpine climate zones (Fig. 2 ), and each has several subtypes (Table  S1 ).
potential evapotranspiration calculated from these meteorological variables with spatial resolution of 0. 5°9 0. 5°during the period 1982-2011 were interpolated from weather station data from Climatic Research Unit (CRU) TS3. 1 (Mitchell & Jones, 2005)  and the CRU National Centers for Environmental Prediction (NCEP) product (http://dods. extra. cea. fr/data/ p529viov/cruncep). CRU-NCEP is a climate dataset produced by adding to the CRU TS3. 1 monthly climate data from the submonthly normalized variability of the NCEP reanalysis (Kanamitsu et al. , 2002).
Acknowledgments This work was funded by  Key Research and Development Program of China  ( 2016YFC0501601 ),  National Natural Science Foundation of China  ( 41571130083 , 41390464 ),  National Special Program on Basic Science and Technology Research of China  ( 2014FY210100 ), and  Fundamental Research Funds for the Central Universities  ( GK201603072 ). We greatly thank  Jinfeng Chang ,  Martin Jung , and  Zaichun Zhu , who provided data on grassland management intensity, GPP, and f PAR data, respectively. We also thank  Shushi Peng  for helpful discussion on the manuscript. Thanks are also given to three anonymous reviewers and editor for their constructive comments. The authors declare no conflict of interest.
Acknowledgements We would like to acknowledge  F. Gbahou ,  A. Hegron ,  M. Luka ,  D. Ndiaye-Lobry ,  J. Dam  and all the other members of the  Jockers laboratory  and  M. Scott ,  F. Herit , and  A. Zahraoui  ( Institut Cochin, Paris ) for their numerous help and precious advice and  S. Laporte  ( McGill University, Montreal ) and ENDOCELLS SARL for providing the SII compound and the immortalized human EndoC-βH1 beta cell line, respectively. We are grateful to  Anne Pecquery  from  DiscoverX Corporation, Ltd. (Birmingham, UK ) for her help in obtaining the ß-arrestin1 HEK293 parental cell line. We would also like to acknowledge  T. Iiri ,  M. Nangaku ,  N. Makita ,  M. Yamamoto ,  H. Tomita  and  M. Taniguchi  for the continuous encouragement and support. This work was supported by the  Fondation de la Recherche Médicale  (Equipe FRM DEQ 20130326503),  Institut National de la Santé et de la Recherche Médicale (INSERM) ,  Centre National de la Recherche Scientifique (CNRS)  and the " Who am I?" laboratory of excellence  No.
Abstract. Over Saint-Sorlin Glacier in the French Alps (45 • N, 6. 1 • E; ∼ 3 km 2 ) in summer, we study the atmospheric surface-layer dynamics, turbulent fluxes, their uncertainties and their impact on surface energy balance (SEB) melt estimates. Results are classified with regard to largescale forcing. We use high-frequency eddy-covariance data and mean air-temperature and wind-speed vertical profiles, collected in 2006 and 2009 in the glacier's atmospheric surface layer. We evaluate the turbulent fluxes with the eddycovariance (sonic) and the profile method, and random errors and parametric uncertainties are evaluated by including different stability corrections and assuming different values for surface roughness lengths. For weak synoptic forcing, local thermal effects dominate the wind circulation.
This study on the Saint-Sorlin Glacier (French Alps, Grandes Rousses massif; 45 • N, 6. 1 • E) compares turbulent flux measurements with observed large-scale weather patterns (WPs) over Europe. We investigated the impact of turbulent flux measurement errors on glacier SEB calculations for different prevailing atmospheric circulation patterns. The study covers two melt periods during the summers of 2006 and 2009, for which dominant atmospheric conditions were contrasted. Atmospheric circulation patterns were characterized with a daily time series of WPs derived from the analysis of  Garavaglia et al. (2010). Surface-layer flow and turbulence data were obtained from two field campaigns deployed in the ablation area of the glacier. We used data from a 6 m mast measuring vertical profiles of wind speed and temperature in 2009, a high-frequency eddy-covariance (EC) system in 2006 and radiation, temperature, wind speed and humidity measured by automatic weather stations during both campaigns.
Field campaigns We used data from two field campaigns focused on the ablation area of the glacier at around 2800 m a. s. l. (Fig. 1 ). The first campaign was undertaken between 9 July and 28 August 2006, featuring a tower-mounted EC system measuring the three wind-speed components, air temperature and specific humidity at 20 Hz. The second campaign was staged between 13 June and 4 September 2009. We installed five cup anemometers and nine ventilated thermocouples on a 6 m mast, measuring mean temperature and wind-speed profiles. During both campaigns there was an adjacent automatic weather station (AWS-G) consisting of instruments measuring mean aspirated air temperature and humidity, mean wind speed and direction, and incoming and outgoing shortwave and longwave radiation fluxes. All masts were fixed into the ice.