dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We thank colleagues from the animal facility and flow cytometry platform of the IGR for technical support as well as Dr. Stephen R. Mattarollo  ( Diamantina Institute, University of Queensland, Australia ) for fruitful discussion. G. K. and L. Z. are supported by the  European Commission (ArtForce) ;  European Research Council ;  Agence National de la Recherche (ANR) ;  Ligue Nationale contre le Cancer ;  Fondation pour la Recherche Médicale (FRM) ;  Institut National du Cancer (INCa) ;  Association pour la Recherche sur le Cancer (ARC) ,  LabEx Immuno-Oncologie ;  Fondation de France ;  Fondation Bettencourt-Schueller ; AXA Chair for Longevity Research;  Cancéropôle Ile-de-France ;  Paris Alliance of Cancer Research Institutes (PACRI)  and  Cancer Research for Personalized Medicine (CARPEM).
Results: We propose to learn a Markov Logic network, e. g. a set of weighted rules that conclude on the predicate "regulates", starting from a known gene regulatory network involved in the switch proliferation/differentiation of keratinocyte cells, a set of experimental transcriptomic data and various descriptions of genes all encoded into first-order logic. As training data are unbalanced, we use asymmetric bagging to learn a set of MLNs. The prediction of a new regulation can then be obtained by averaging predictions of individual MLNs. As a side contribution, we propose three in silico tests to assess the performance of any pairwise classifier in various network inference tasks on real datasets. A first test consists of measuring the average performance on balanced edge prediction problem; a second one deals with the ability of the classifier, once enhanced by asymmetric bagging, to update a given network.
It will be especially meaningful when the biologist wants to increase the corpus of existing knowledge. This paper deals with the latter prediction problem. We assume that a directed graph of regulations is known partially for a target set of genes. For instance, it is the result of the biologists's experience and careful mining of the literature. Besides the graph structure, we also suppose that a set of various descriptors of genes and their products are available for the target set of genes, such as gene expression data, Gene Ontology (GO) annotation, protein-protein interaction and also genes location on chromosomes. Our goal is to build a decision function that predicts if an ordered pair of regulator and regulee candidates belongs to the class Regulation or No Regulation. In this work, we address four issues raised by supervised edge prediction and implement the whole approach on a new experimental dataset related to the ID2 genetic regulatory network in human keratinocytes.
It is worth noticing that we do not solve the issue of false negative, e. g. the fact that among the "no regulation" examples, there might be "regulation" examples that have not been validated yet. The reader interested by this issue is invited to study the works of Cerulo et al. [7]  and Mordelet & Vert  [6]. Finally, as a fourth contribution, we define and perform three typical numerical studies that can be drawn in order to test a machine learning method devoted to edge prediction: one is a basic test with artificially balanced samples in which we just test the ability of the learning method to obtain good performance; the second one consists of building a regulation predictor in a realistic setting from unbalanced datasets using asymmetric bagging and measuring its ability to discover regulations that were not known before; in the third last study, we proceed in the same way but test the ability of the classifier to label correctly pairs of genes with genes from the training network and genes coming from a new candidate set.
In order to assess the performance of the MLN-based approach, we define a pairwise Support Vector Machine (SVM) devoted to ordered pairs of genes and use it as a baseline using a straightforward simplification of the tensor product pairwise kernel. Kernel-based methods as well as first-order logic provide a framework to take into account different sources and features of the data: in this study, two simple definitions of pairwise kernels that combine multiple pairwise kernels expressing heterogeneous information are proposed. While the goal of the study is to take advantage of the heterogeneity of features to describe a pair of genes, we also study the behavior of MLN compared to pairwise SVM in the case of single source of quantitative information such as gene expression. In order to show the interest of solving these four issues, we have applied our approach to the ID2 genetic regulatory network in human keratinocytes and a new dataset of gene expression using RNA interference.
The ID2 protein (Inhibitor of Differentiation 2) acts as a negative regulator of basic helix-loop-helix transcription factors. Previous studies have suggested a potential role for ID2 in epidermis homeostasis reflected by the high expression level of ID2 in proliferating keratinocytes and its down-regulation upon the onset of differentiation  [32]. However, the precise implications of ID2 in the process, and in particular its genetic interactions, remain largely unknown. In an attempt to decipher the ID2 genetic regulation network in human keratinocytes, we conducted a transcriptomic analysis by microarray experiments of HaCaT cells presenting stable overexpression or transient knock-down achieved by RNA interference of ID2 expression. As a starting point, we retrieved the regulatory networks associated with the differentially expressed genes in cells with high and low level of ID2 from the Ingenuity Pathway Analysis (IPA) database.
Learning the candidate rules with Aleph The system Aleph, developed by Srinivasan  [35] , is a well known ILP learner that implements the method proposed in  [36]. Aleph, like other relational learners, takes as input ground atoms corresponding to positive and negative examples and background knowledge. It also needs language biases, which restrict the set of clauses that can be generated, thus allowing to reduce the size of the search space. These restrictions can correspond to information specified on the predicates, like the place where they occur  in the rule, the types of their arguments or the way they will be used (instantiated or not). In our case, we specified that the predicate Regulates occurs in the head of the rule, and the other ones in the body of the rule. Other constraints, such as the maximum number of atoms in a clause or the number of variables, can be defined in order to restrict the form of the rules that can be learned.
The main learning method developed by Aleph, called induce, is sketched in the following: 1. Select a positive example not yet covered by a rule 2. Build the most specific clause r that covers this example and that satisfies the language biases from the background knowledge. This clause is called the "bottom clause". 3. Search a clause more general than the bottom clause: perform a top-down search (from the most general to the most specific clause) in the search space bounded by r. 4. Add the clause with the best score to the current theory and prune redundant clauses. 5. Repeat until all positive examples are covered.
In order to characterize regulatory interactions, we used other features describing genes. Some of these features concern proteins and not directly genes. • Physical interaction between proteins: Physical interaction between proteins can provide a hint about the role played by the genes coding for these proteins. In our study, we used the protein interaction data from the IntAct database  [45]. We encoded the information of a physical interaction by a predicate containing the name of the genes that are assumed to code the proteins: Interprot(G 1 name, G 2 name).
• Chromosomal location of genes: We extracted the genes location on chromosomes and chromosomal bands from the Entrez Gene database  [46]. This information is encoded by the predicates Locchro(Gname, Chro) and Locband (Gname, Arm_begin, Band_begin, Arm_end, Band_end). From these predicates, we built two other predicates that we used instead: Samechro(G 1 name, G 2 name) and Sameband(G 1 name, G 2 name). These predicates provide information on the proximity between the gene locations of G 1 and G 2.
Study Positive set Negative set Protocol 1 R + 1 R - 1,i , i = 1,. , 30 10-CV on R + 1 ∪ R - 1,i 2 R + 1 R - 1,i , i = 1,. , 30 AB on R + 1 ∪ R - 1,i , test on R + 2 \ R + 1 3 R + 2 R - 2,i , i = 1,. , 30 AB on R + 2 ∪ R - 2,i , test on R 3 We conducted three experimental studies on the gene regulatory network associated with ID2 in human cells. In the table, 10-CV means cross-validation 10 times and AB means Asymmetric Bagging. dataset will provide biased classifiers. To build a classifier appropriate for an unbalanced prediction task, we used asymmetric bagging  [30, 31]. In supervised classification, asymmetric bagging consists of performing random sampling only on the overrepresented class, such that the number of examples in the subsample is equal to the number of examples in the under-represented class.
This way, each generated predictor was trained on a balanced dataset. Their predictions on the test set were combined to provide a single prediction. Studies described in  [30, 31]  have shown that asymmetric bagging provide better results than normal bagging on unbalanced datasets. In the last study, we solved a network completion task in real conditions. We selected a new set of genes G B and tried to infer the known regulations between the genes of G B and G A. Asymmetric bagging was also applied. The lists of genes in G A and G B are given in the Additional file 1 and details on Aleph parameters are available in the Additional file 2. Regarding Alchemy, we used the implementation of the discriminative weights learning procedure and tested different values of the regularization parameter λ.
Average cross-validation measurements on balanced samples We first tested the performance of an MLN and compared it to that of a pairwise SVM on a well-balanced classification task. To do that, we subsampled the negative example set and generated subsamples of negative examples of the same size as the positive examples set. The dataset contains a set R + 1 of 106 positive examples of regulations between the genes of G A. We randomly sampled  30 on R + 1 ∪ R - 1,i. In each experiment, we first used Aleph, the ILP tool previously described, as a structure learner on the training set. With Alchemy  [42] , we learned the weights of the MLN defined by the structure obtained with Aleph and then we performed inference on the test set. For the SVM we used Lib-SVM that we fed with the right definition of the two pairwise kernels computed in Matlab.
Prediction on the updated graph In this second study, we addressed a network completion task while keeping the same set of nodes. Two years after the dataset described previously was obtained, the tool Ingenuity was used again to provide an updated set R + 2 of regulations between the 63 genes of interest on this date. We noticed that 51 new regulations were discovered by Ingenuity between these two dates. We were therefore interested in the prediction task of the updated graph, i. e. to see if we could retrieve these new regulations from the data of 2007. We used the dataset R + 1 from 2007 containing 106 regulations as positive training set and tried to infer the 51 new regulations in  The table reports the averaged AUC values and standard deviations obtained with SVMs for thirty ten folds cross-validation experiments conducted on a regulatory network between the genes in G A.
Prediction with a new set of genes For the third statistical analysis, we addressed a network completion task when new candidate nodes are added. We used a dataset refined in the biology laboratory. 209 high confidence differentially expressed genes in prcID2 versus the corresponding control cells were identified. From these genes, we selected 37 genes that were not part of G A and for which we had an annotation for each predicate. These genes were also chosen from the ones that had at least one regulation link with one of the genes from G A or with one gene of this new set. From these 37 genes, we selected a subset of 24 genes, called G B , that had at least a biological process annotation from GO in common with genes from G A. The goal of this study was to try to complete a known network using an additional set of candidates genes, which is usually the problem of interest for the biologists.
We used Ingenuity to retrieve the known regulations between genes from G A and G B , being aware that when no regulation is mentioned in the literature, it does not mean that it does not exist but only that it has not been discovered yet. We called this set R + 3. We used the set R + 2 of 157 regulations from the dataset G A as the positive examples training set and used asymmetric bagging with 30 samples on R 2. For each sampling we applied the predictor on the sets R + 3 and R + 3 , using descriptors on both set of genes G A and G B. We obtained score predictions for each interaction between one gene of G A and one gene of G B. Table  5   2  and 3. These results were obtained using asymmetric bagging with 30 samples on the set R 2. statistics developed by  [51]  and the implementation provided by the R package pROC  [52].
Resulting logical rules In addition to the capacity of the built classifier to suggest new regulations, MLNs present the advantage of providing a set of weighted rules that the biologist can check. In general, Aleph learned between 30 and 50 rules for each run, these rules being composed of up to five predicates. This comes from the choice of the parameters of Aleph as described in the Additional file 2. The analysis of the rules that have obtained a high weight shows that some of them exhibit relevant patterns. Among the rules inferred by Aleph, here is an example of four rules which were associated to a high weight in the numerical tests: The first rule means that a gene overexpressed in transient knock-down of ID2 regulates overexpressed genes in the same condition and that code for proteins in plasma membrane.
reports the AUC values computed for bagged MLNs and bagged SVMs. The ROC and PR curves obtained for the hyper-parameters associated with the best values of AUC-ROC are represented in the Additional file 3. Although each predictor was trained on a balanced dataset, with same numbers of positive and negative examples of regulation, this test was made under real conditions: we considered the whole set of positive (|R + 3 | = 55) and negative examples (|R + 3 | = 2969) to assess the performance in prediction. On the test-training interactions, the predictor with bagged MLNs performed quite well, showing an AUC-ROC of about 0. 73. This was really a very good result which implies low degradation in performance especially for the false positive rate that only slightly increases. The AUC values obtained with bagged MLNs are above the values obtained with the two bagged SVMs.
Photomicrographs were captured at five predetermined positions in each culture using a motorized microscope stage. The exposure length and gain was maintained at a constant level for all samples, and the image brightness was within the dynamic range of the camera. Two blinded and independent investigators assessed expression of the various markers in five photomicrographs in each culture ( = 8 (repeated twice, 4 each)). For the RPE65, PCNA, and caspase-3 markers, the number of positive cells/total number of cells × 100%  was calculated. Assessment of observer agreement between the two investigators demonstrated high reliability of the phenotypic data (Table  1 ).
Datasets for developing the CDOM absorption algorithm The IOPs datasets used for developing our semi-analytical CDOM absorption algorithm are documented in  Matsuoka et al. (2007 Matsuoka et al. ( , 2011)). Briefly, sampling was conducted in the western Arctic Ocean in the area from approximately latitudes 65 • to 77   (i. e. , between the surface and the euphotic depth, z eu , where 1 % of the surface irradiance subsists). Absorption coefficients of particles (a p (λ), m -1 ) retained on GF/F filters were determined from 350 to 750 nm using a MPS-2400 spectrophotometer (Shimazu Corp. ). Phytoplankton pigments retained on the filters were extracted using methanol  (Kishino et al. , 1985) , absorption coefficients of NAP (a NAP (λ), m -1 ) were measured on the bleached filter, and phytoplankton absorption coefficients (a ϕ (λ), m -1 ) were obtained by subtracting a NAP (λ) from a p (λ).
CDOM absorption and DOC determination CDOM absorption datasets for evaluating our CDOM algorithm are documented in  Matsuoka et al. (2012). Briefly, measurements were performed in the southern Beaufort Sea during the France-Canada-USA joint Arctic campaign, MA-LINA, in the area from approximately latitudes 69 • to 72 • N and longitudes 125 • to 145 • W (Fig. 1 ). Sampling was conducted from 30 July to 26 August 2009 aboard the Icebreaker CCGS Amundsen, and 37 stations were visited. A barge and/or zodiac were also deployed (32 stations out of 37) during the same time periods of the CTD deployment to obtain surface water samples (see Fig. A1  of  Matsuoka et al. , 2012). The absorption of CDOM in the sample filtrate was measured over the spectral range 200-735 nm using an UltraPath liquid waveguide system (World Precision Instruments, Inc.
Ocean color data Three-weeks composite images of R rs (λ) at 412, 443, 488, 531, 551, and 670 nm in August 2009 were generated us-ing Aqua/MODIS ocean color data. After Level 1A data were downloaded from the ocean color website (http:// oceancolor. gsfc. nasa. gov/cgi/browse. pl?sen=am), geometric correction was made using the SeaWiFS Data Analysis System (SEADAS) software. Because the use of MODIS nearinfrared bands (748 and 869 nm) for atmospheric correction (i. e. , NASA standard atmospheric correction:  Gordon and Wang, 1994; Gordon, 1997)  often yielded pixels having negative values, especially in R rs (412) in coastal areas of the southern Beaufort Sea (which were flagged), a new method that can be applied to turbid ocean waters, proposed by  Wang and Shi (2007) , was used to obtain R rs (λ) images in this study.
, 2011). In this study, Arctic-specific parameters are used for estimating CDOM absorption (Sect. 3. 2). To generalize R rs (λ) spectral shapes, k-means cluster analysis was performed. The best number of classes (CN) was determined based on a minimum value of the Calinski-Harabasz index, which was found to be equal to 4 (Fig. 3b  and Table  1 ). This index is one of the best methods to objectively obtain an optimal number of classes and may be applied to a variety of datasets  (Calinski and Harabasz, 1974). Turbidity of waters tended to increase from cluster 1 to 4, with the clusters roughly corresponding to those observed in the Chukchi Sea, except for cluster 4  (Matsuoka et al. , 2007). Cluster 4 was observed especially in the Mackenzie River mouth. Similar clusters were also identified when a CN of 2 was set (gray solid and dashed lines for oceanic and coastal waters, respectively, in Fig.
, the step 2 in the Fig. 2 ; see also Table  2 ), which are often set as constants  (Lee et al. , 2002; Maritorena et al. , 2002) ; these can actually be parameterized as functions of R rs (λ) (see Appendix A). Since CDOM absorption values were significantly different between CTD/Niskin and Barge samples (see Fig. A1  of  Matsuoka et al. , 2012) , the performance of the algorithm was evaluated separately for each category (i. e. , CTD/Niskin or Barge; CTD/Niskin and Barge correspond to oceanic and coastal waters, respectively). Note that while three empirical algorithms for estimating a CDOM (443) using SBI spr and SBI sum datasets (MR dataset was not used because of lack of R rs (555) data) were evaluated, there were almost no correlations between a CDOM (443) and each R rs (λ) ratio (see Table  A2 ).
to note that this evaluation was conducted using independent datasets (i. e. , MALINA datasets) that were not used for developing the algorithm (i. e. , SBI spr, SBI sum, and MR aut datasets were used for this development). While parameterization of IOPs was made for typical oceanic waters (not directly influenced by river discharge) of the Arctic Ocean  (Matsuoka et al. , 2011) , the evaluation results suggest this parameterization can also be applied for Arctic coastal waters directly influenced by the discharge of the Mackenzie River. This idea is partly supported by the fact that phytoplankton absorption properties, which were used for tuning the algorithm (see step 1 in Fig. 2 ), are similar between oceanic and coastal waters for the same season in the Arctic Ocean  (Matsuoka et al. , 2009).
Regression analyses between a CDOM (443) and R rs (λ) band ratio using SBI spr and SBI sum datasets. CDOM (443) vs. rs 412/R rs 555 0. 19 92 a CDOM (443) vs. R rs 490/R rs 555 0. 05 92 a CDOM (443) vs. R rs 531/R rs 555 0. 02 92.
Acknowledgements. We are grateful to the captain and crews of the  Canadian Icebreaker CCGS Amundsen, USCGC Healy , and  Japanese R/V Mirai. Instrument development of C-OPS by  J. H. Morrow  is much appreciated. Datasets from SBI2002 cruises were provided by  V. Hill  on the basis of the data policy (http://www. eol. ucar. edu/projects/sbi/). Sampling for these cruises was supported by the  Arctic System Sciences program  of the  National Science Foundation   OPP-0125049  and  0223375  and the  National Aeronautics and Space Administration's (NASA) Sensor Intercomparison and Merger for Biological and Interdisciplinary Studies (SIMBIOS)  program  NAG5-10528. DOC concentrations during MALINA cruise were provided by  R. Benner  using funding from the  US National Science Foundation  ( 0713915 ).
Background: Gene clustering algorithms are massively used by biologists when analysing omics data. Classical gene clustering strategies are based on the use of expression data only, directly as in Heatmaps, or indirectly as in clustering based on coexpression networks for instance. However, the classical strategies may not be sufficient to bring out all potential relationships amongst genes. Results: We propose a new unsupervised gene clustering algorithm based on the integration of external biological knowledge, such as Gene Ontology annotations, into expression data. We introduce a new distance between genes which consists in integrating biological knowledge into the analysis of expression data. Therefore, two genes are close if they have both similar expression profiles and similar functional profiles at once.
In these methodologies, gene clusters are obtained on the basis of expression data only and biological knowledge is a posteriori used to make the most of the clusters. The limits of such procedures are clear: clustering genes on the basis of expression data only allows to isolate coexpressed, however not necessarily biologically coherent units  [7, 8]. Indeed, a clustering structure can only be as good as the distance/similarity matrix it is based on. Hence, the idea of actively integrating biological knowledge into expression data, to isolate more meaningful biological entities. In other contexts, this issue of actively integrating biological knowledge into expression data has been covered. In the purpose of biological networks inference, Kashima et al. [9]  proposed a semi-supervised learning method.