dataesr/scientific-paragraphs-categorization huggingface dataset:
This last example is the one representing a real-world clinical application. To study how the preformances of the algorithm are influenced by the size of the input dataset, its profiling platform and the typical class-imbalance present in biological data we divided the example in four subcases obtained by varying the transcriptomic dataset and the considered subtypes (from 3 to 5): - -Example 5. 4: BRCA Level 3 RNA-seq expression profiles downloaded from TCGA in January 2016 and consisting of 397 samples are classified into 4 subtypes: Luminal A, Luminal B, HER2 and Basal. This last test was not present in  24  and it has been added in order to compare the classification performances also on RNAseq data. The used gene signature has been constructed in  24. NTP was also applied to the same examples and the performances of the two algorithms are compared in the following sections.
The datasets, class labels and signatures used for the comparison were downloaded from http://www. broadinstitute. org/cgi-bin/cancer/datasets. cgi. The size of the signatures ranges from 19 to 956. A detailed summary is reported in Supplementary Table  1. NTP was applied with standard parameters (cosine distance and 1000 permutations) through the GenePattern analysis toolkit (www. broadintitute. org/genepattern)  35. In line with  24  , additional prediction analyses were performed with standard machine learning methods: Classification and Regression Trees (CART), Weighted Voting (WV), Support Vector Machine (SVM) and k-Nearest Neighbor (k-NN). All the algorithms have been applied with standard parameters through the GenePattern analysis toolkit (www. broadintitute. org/genepattern)  35. Differently from NTP and Hope4Genes these alternative approaches involve a training and test phases.
Training datasets were downloaded from http://www. broadinstitute. org/cgi-bin/cancer/ datasets. cgi. For Example 5. 4 the Metabric dataset, available at https://www. synapse. org/#!Synapse: syn1688369 was employed as training set. For multi-class classifications (Examples 2-3 and 5) the expression levels were z-score transformed at gene-level to adjust the range of expression of each gene between training and test datasets.
Discussion In recent years, the classification of cancer samples into subtypes has become one of the major issues of precision medicine. This is even stronger for cancer whose response to treatment regimes and disease course and severity vary going from one patient to another, even if both affected by the same tumor type. To achieve the best therapeutic performances, cancer patients need to be classified into subpopulations characterized by a common prognosis or response to treatment ("cancer subtypes"). The association of a new patient to predefined subtypes, also known as class prediction, is thus vital to precision medicine. We here proposed Hope4Genes aimed at achieving this goal using a Hopfield-like model. We tested the performances of Hope4Genes in multiple test cases, exploring datasets profiled with different platforms, of different sample-sizes and multi-class and two-class classifications.
Dataset preprocessing The third input is the gene expression dataset A which is preprocessed, in accordance with  4  , as follows: • All the genes that are not contained in any signature are filtered out. • For each remaining gene i, we associate to the corresponding node in the network the initial state x 0 i = sign(log2ratio(A[i] )), where A[i] is the expression value of gene i in the current sample. If A[i] = 0 (say, because the gene i is not present in the sample) then we set x 0 i = 0.
a dataset of 198 samples 32 is classified into 5 subtypes: Luminal A, Luminal B, HER2, Basal and Normal. we classified a dataset of 286 samples 33 in 4 subtypes: Luminal A, Luminal B, HER2 and Basal. -Example 5. 3: 53 samples of 34 are classified into 3 subtypes: Basal, Luminal A and Luminal B.
Example 5. 1 :-Example 5. 2 : 5152 a dataset of 198 samples 32 is classified into 5 subtypes: Luminal A, Luminal B, HER2, Basal and Normal. we classified a dataset of 286 samples 33 in 4 subtypes: Luminal A, Luminal B, HER2 and Basal. -Example 5. 3: 53 samples of 34 are classified into 3 subtypes: Basal, Luminal A and Luminal B.
Data collection Characteristics of the informal caregivers were gathered (i. e. age, gender, marital status, and relationship to the resident). Semi-structured interviews  [16]  were used. First, the interviews were pilot tested within the research team; the first interview was performed by the first author (PhD, male) and a trained research assistant (MSc, female), who performed all other interviews. There was no prior relationship between the interviewer and the participants before the interviews. Participants received Regular small-scale living facility (9 locations) Physical environment: -Small-scale living facilities can be stand-alone in a neighbourhood, or clustered on the area of a larger nursing home. - -Approximately 20 residents on a ward -Large team of nursing staff with differentiated tasks -Daily life is mainly determined by routines and rules of the organisation -Cooking in central kitchen, not in the home  [5]  information about the study on paper, and verbally before the interview.
Data analysis The data were analysed using qualitative data analysis software MAXQDA  [17]. Partly based on a phenomenological research approach aimed at understanding people's experiences  [16, 18] , a thematic analyses approach was used, following the steps identified in previous research  [19, 20]. The data were systematically searched to identify patterns in order to provide a description of the topics investigated. A combination of open, axial and selective coding was used  [16]. First the researchers read through the transcripts several times and started to create labels for chunks of data that summarised the main message. During this stage it was determined whether chunks of data belonged to 'reasons of choice' , 'positive experiences' , or 'negative experiences' , and a label was given to the chunks of data (e.
Results: We propose a new approach named TFcoop to infer the TF combinations involved in the binding of a target TF in a particular cell type. TFcoop aims to predict the binding sites of the target TF upon the nucleotide content of the sequences and of the binding affinity of all identified cooperating TFs. The set of cooperating TFs and model parameters are learned from ChIP-seq data of the target TF. We used TFcoop to investigate the TF combinations involved in the binding of 106 TFs on 41 cell types and in four regulatory regions: promoters of mRNAs, lncRNAs and pri-miRNAs, and enhancers. We first assess that TFcoop is accurate and outperforms simple PWM methods for predicting TF binding sites. Next, analysis of the learned models sheds light on important properties of TF combinations in different promoter classes and in enhancers.
First, we show that combinations governing TF binding on enhancers are more cell-type specific than that governing binding in promoters. Second, for a given TF and cell type, we observe that TF combinations are different between promoters and enhancers, but similar for promoters of mRNAs, lncRNAs and pri-miRNAs. Analysis of the TFs cooperating with the different targets show over-representation of pioneer TFs and a clear preference for TFs with binding motif composition similar to that of the target. Lastly, our models accurately distinguish promoters associated with specific biological processes. Conclusions: TFcoop appears as an accurate approach for studying TF combinations. Its use on ENCODE and FANTOM data allowed us to discover important properties of human TF combinations in different promoter classes and in enhancers.
On the other hand, studies based on ChIP-seq data require as many ChIP-seq data as the number of studied TFs, and hence are intrinsically limited by the availability of these data. Moreover, with hundreds (or even thousands) of sequences, a small co-occurrence tendency may be statistically significant, even if the effect is actually very weak and would not be biologically relevant. A few works have studied TF combinations in a more global way, above the TF pair level. For example, Teng et al. [17]  have applied the "frequent itemset" methodology to identify sets of cooccurring TFBSs on the basis of ChIP-seq data. However, many questions remain on the molecular determinants orchestrating TF binding and combinations  [18]. Notably, with the expanding coding capacity of the human genome  [19, 20] , it remains to determine whether the expression of all gene classes, in particular coding mRNAs, long noncoding(lnc)RNAs and micro(mi)RNAs, is controlled by similar TF combinations in a given cell type.
Hence, rather than using purely statistical co-occurrence analysis, we study TF combinations in the framework of a TFBS prediction problem. The approach has several advantages. First, a single ChIP-seq experiment is theoretically sufficient to identify all TFs cooperating/competing with the target TF in the target cell type. Next, if a TF is selected in the combination, this means that its predicted binding sites are indicative of the presence of the target TF, which limit the number of false positives and the problems of spurious statistical significances. Finally, the approach takes into account all TFs and can therefore identify all possible TF combinations not just TF pairs. TFBSs are traditionally modeled with position weight matrices (PWMs)  [22]. Several databases such as JAS-PAR  [23] , HOCOMOCO  [24] , CisBP  [25]  and Transfac  [26] , propose position frequency matrices (PFM, which can be transformed in PWMs) for hundred of TFs.
Hence, we devised a simple non ab initio strategy names TFcoop that predicts if a target TF binds a sequence of interest using two kinds of variables: i) the binding affinity (i. e. PWM affinity score) of the target TF as well as any other TF identified as cooperating with the target TF; and ii) the nucleotide composition of the sequence. TFcoop is based on a logistic model. The set of cooperating TFs and the model parameters are learned from ChIP-seq data of the target TF via LASSO penalization  [48]. Learning can be done using a moderate amount of data, which allows us to learn specific models for different types of regulatory sequences. Using ChIP-seq data from the ENCODE project, we applied TFcoop to investigate the TF combinations involved in the binding of 106 different TFs on 41 different cell types and in four different regulatory regions: promoters of mRNAs, lncRNAs and miRNAs, and enhancers  [19, 20, 49, 50].
Namely, every promoter sequence with a ChIP-seq peak is considered as a positive example, while the other sequences are considered as negative examples (see below). In the experiments below, we used 409 ChIP-seq datasets from ENCODE and different models. Each model targets one TF and one cell type. Given a ChIP-seq experiment, the learning process involves selecting the PWMs and (di)nucleotides that can help discriminate between positive and negative sequences (this is done by way of the LASSO penalization  [48] ), and estimate the model parameters that minimize prediction error. Note that the learning algorithm can select any predictive variable including the PWM of the target TF. See "Methods" section for more details on the data and logistic model. Note also that, while several classification approaches are available in the literature, all methods are not suitable for our problem.
Because our aim is to identify TF combinations, only methods implementing a feature selection procedure are eligible. To this aim, LASSO penalization is often considered as a method of choice  [52]. An alternative would be to use classification trees, but this method is known to suffer from stability issues  [53]. We used two different procedures for selecting the positive and negative sequences. Each procedure actually defines a different classification problem. In the first case, we kept all positive sequences (i. e. promoters overlapping a ChIP-seq peak in the considered ChIP-seq experiment), and randomly selected the same number of negative sequences among all sequences that do not overlap a ChIP-seq peak. In the second case, we used an additional dataset that measures gene expression in the same cell type as the ChIP-seq data.
TFcoop assessment We ran TFcoop on the 409 ChIP-seq datasets and for the two prediction problems. The accuracy of each model was assessed by cross-validation by plotting the Receiver Operating Curve (ROC) and measuring the Area Under the Curve (AUC). For comparison, we also measured the accuracy of the classical approach that discriminates between positive and negative sequences using only the affinity score of the PWM associated with the target TF. In addition, we estimated the accuracy of the TRAP method, which uses a biophysically inspired model to compute PWM affinity  [54]  and that of the approach proposed in  [37] , which integrates DNA shape information with PWMs. As shown in Fig. 1a  and Additional file 1: Figure  S1  and Figure  S2 , TFcoop outperforms these PWM-based approaches on many TFs (t-test p-values 1.
Lastly, we compared TFcoop accuracy to that of the deep learning approach DeepSea  [45]  and observed very close results (see Fig. 1b ; t-test pvalue 0. 048). Hence, TFcoop performances appear to be in the range of that of classical ab initio methods. Next, we sought to assess the TF cooperations inferred by the models. If true, they should be apparent in the ChIP-seq experiments. Namely, if the PWM of TF B is among the selected variables for predicting the presence of TF A, then we should observe many common targets among the ChIP-seq experiments of TFs A and B. To test this, we first randomly selected one model for each different TF, and restricted our analyses to the PWMs associated with TFs with ENCODE ChIP-seq experiments. Then, for each model A, we measured the Jaccard index between promoters bound by TF A and promoters bound by a TF B whose PWM has been selected in model A (cases B = A were not considered), and we compared these scores to the same scores computed on TFs whose PWMs have not been selected in model A (see "Methods" section).
In these analyses, we used the model learned on one ChIP-seq experiment A to predict the outcome of another ChIP-seq experiment B, and we compared the results to those obtained with the model directly learned on B. More precisely, we measured the difference of AUC between the model learned on A and applied on B and the model learned and applied on B. To avoid any effect driven by the over-representation of CTCF in ChIP-seq data, we randomly selected only 10 ChIP-seq experiments targeting this TF in these analyses. As shown in Fig. 1d  and e , models learned on the same TF (whether or not on the same cell-type) have overall smaller AUC differences than models learned on different TFs. For sake of comparison, we also ran the same analysis on non-ENCODE ChIP-seq data targeting 10 different TFs (see "Methods" section).
e. 519 PWMs). As shown in Additional file 1: Figure  S5 , results are slightly less accurate than with the complete (redundant) database, illustrating the fact that alternative motifs provide important information unveiled by TFcoop. We further analyzed the most selected PWMs. To avoid any bias linked to the number of CTCF ChIP-seq experiments, we only considered 10 CTCF models that were randomly selected for the analyses. We ranked the PWMs by the number of models in which they appear, and look for enrichment of certain JASPAR structural families (bHLH, Zinc finger,. ). A gene set enrichment analysis (GSEA, see "Methods" section)  [55]  shows that "tryptophan cluster factors" (FDR q-val< 10 -4 ), "C2H2 zinc finger factors" (FDR q-val< 10 -4 ) and "basic leucine zipper factors" (FDR q-val = 2 • 10 -3 ) are the most represented classes of PWMs selected in the models (Additional file 1: Figure  S7 ).
For each pair of ChIP-seq experiment A-B, we measured the difference between the AUC achieved on A using the model learned on A, and the AUC achieved on A using the model learned on B. AUC differences were measured on the expression-controlled case. Bottom: Promoter models are interchangeable. For each ChIP-seq experiment, we computed the AUC of the model learned and applied on mRNAs (pink), learned and applied on lncRNAs (yellow-green), learned and applied on pri-miRNAs (blue), learned on mRNAs and applied to lncRNAs (green), learned on mRNAs and applied to pri-miRNAs (purple) we did not apply any control on their expression, while in a second case, we used CAGE expression data in the different tissues to only select expressed enhancers. As observed for promoters, TFcoop outperforms classical PWM-based approaches on many TFs (see Fig.
Conclusions On the whole, studying TF combinations by the way of a TFBS prediction problem appears as a promising approach. We showed here that, despite its simplicity, the TFcoop method is accurate and allows identifying complex combinations on the basis of a single ChIP-seq experiment. We used it on ENCODE and FANTOM data and identified important properties of TF combinations in human. Specifically we showed that combinations governing TF binding on enhancers are more cell-type specific than that governing binding in promoters. Moreover, for a given TF and cell type, TF combinations are different between promoters and enhancers, but similar for promoters of mRNAs, lncRNAs and pri-miRNAs. Finally, analysis of the TFs cooperating with the different targets show over-representation of pioneer TFs and a clear preference for TFs with binding motif composition similar to that of the target.
Promoter, enhancer, long non-coding RNA and microRNA sequences We predicted TF binding in both human promoters and enhancers. For promoters, sequences spanning ±500 bp around starts (i. e. most upstream TSS) of protein-coding genes, long non-coding RNAs and microRNAs were considered. Starts of coding and lncRNA genes were obtained from the hg19 FANTOM CAGE Associated Transcriptome (CAT) annotation  [19, 49]. Starts of microRNA genes (primary microRNAs, pri-miRNAs) were from  [20]. For enhancers, sequences spanning ±500 bp around the mid-positions of FANTOM-defined enhancers  [50]  were used. Lastly, our sequence datasets are composed of 20,845 protein coding genes, 1250 pri-microRNAs, 23,887 lncRNAs, and 38,553 enhancer sequences.
PWMs We used vertebrate TF PFMs from JASPAR  [23] , including all existing versions of each PFM, resulting in a set of 638 PFMs with 119 alternative versions (i. e. 519 different TFs). We also used the non-redundant version of the JASPAR vertebrate database (519 PFMs) and the two alternative PFM libraries CisBP  [25]  and HOCOMOCO  [24]. CisBP is a meta-library gathering PFMs from various sources, which contains up to 972 human PFMs (http://cisbp. ccbr. utoronto. ca). We collected, for each TF, all directly determined motifs indicated in TF_Information. txt of the Homo sapiens archive. To avoid redundancy, we selected only one model for each TF by arbitrarily selecting the longest PWM. Moreover we also excluded all TRANSFAC PWMs that are not publicly available (this reduces the set of TFs associated with a PFM to 625).
ChIP-seq data We collected ChIP-seq data from the ENCODE project  [61]  for human immortalized cell lines, tissues, and primary cells. Experiments were selected when the targeted TF were identified by a PWM in JASPAR. Thus we studied 409 ChIP-seq experiments for 106 distinct TFs and 41 different cell types. The most represented TF is CTCF with 69 experiments, while 88% of the experiments are designed from immortalized cell lines (mainly GM12878, HepG2 and K562). The detailed list of all used experiments is given in Supplementary materials. For each ChIP-seq experiment, regulatory sequences were classified as positive or negative for the corresponding ChIP targeted TF. We used Bedtools v2. 25. 0  [62]  to detect intersection between ChIP-seq binding sites and regulatory sequences (both mapped to the hg19 genome).
Each sequence that intersects at least one ChIP-seq binding region was classified as a positive sequence. The remaining sequences formed a negative set. The number of positive sequences varies greatly between experiments and sequence types. Mean and standard deviation numbers of positive sequences are respectively 2661(±1997) for mRNAs, 1699(±1151) for lncRNAs, 216(±176) for microRNAs, and 1516(±1214) for enhancers. For sake of comparison, we also used non-ENCODE ChIP-seq data collected from the Cistrome database  [63]  (http://cistrome. org). Note that Cistrome provides hg38 ChIP-seq peaks, not narrow peaks as provided by ENCODE. We collected data corresponding to GSM2224586 (ELF1), GSM1056931 (ETS1), GSM894076 (MAX), GSM1423725 (MYC), GSM1698353 (USF1), GSM1614036 (JUN), GSM2042914 (JUND), GSM1917774 (ATF3), GSM1708340 (YY1) and GSM1334010 (ZBTB33).
Pioneer factors We used the classification of  [9]  to distinguish pioneers, settlers and migrants TFs. 'Pioneer' TFs occupy previously closed chromatin and, once bound, allow other TFs to bind nearby. 'Settler' designate TFs whose binding is predominantly dependent on the openness of chromatin at their motifs. 'Migrants' bind only sporadically even when chromatin at their motifs is open. Comparison of AUCs achieved when using nucleotide and dinucleotide frequencies only (x-axis) and when using nucleotide, di-, tri-, and quadri-nucleotide frequencies (y-axis). Figure  S5. Comparison of AUCs achieved with the JASPAR (complete), JASPAR (non-redundant), CisBP and HOCOMOCO databases of PWMs. Figure  S6. Comparison of AUCs achieved on ENCODE and non-ENCODE data. Each column corresponds to a TFcoop model learned on a specific ENCODE ChIP-seq experiment.
Funding This work was supported by funding from  CNRS ,  Plan d'Investissement d'Avenir # ANR-11-BINF-0002   Institut de Biologie Computationnelle (young investigator grant  to C-H. L. and post-doctoral fellowship to J. V. ),  Labex   NUMEV (post-doctoral fellowship  to J. V. ),  INSERM-ITMO Cancer project  " LIONS "  BIO2015-04 , and  CNRS International Associated Laboratory  "miREGEN".
6 to 1 and 35. 3 to 1 respectively for the R90 and C75 surveys. Nevertheless, the advantage of the LQAC-5 lies in two fundamental aspects: first, it gives a sample of objects duly recognised spectroscopically as quasars, excepted a very small population consisting of VLBI sources (among which ICRF2 ones) kept for their obvious astrometric quality. Second, as is illustrated by the acronym LQAC, it gives the best a priori determination of the celestial coordinates of each object, which is, for a large majority of objects, considerably more accurate than the determination of coordinates coming from the original catalogues. The LQAC is continuously improved and updated at the fast rhythm of large survey observations, for example, the next SDSS public release containing new eBOSS data is scheduled for the summer of 2019 and will contain spectroscopic data after four years of observations, which should represent more than 700 000 quasars.
Acknowledgements. This work has been supported by a  CNES  post-doctoral grant. BC acknowledges support from the  Advanced EU Network of Einfrastructures for Astronomy with SKA (AENEAS) , funded by the  European Commission   Framework Programme Horizon 2020 RIA  under grant agreement n. 731016 and from the  ENGAGE SKA RI , grant  POCI-01-0145-FEDER-022217 , funded by  COMPETE 2020 and FCT, Portugal.