dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion: Comparison to other methods As mentioned in Section 1. 2, a few methods have been proposed to predict interactions between proteins and ligands. We compared the prediction performances of the proposed NN-MT method to those of two state-of-the art methods: a recent Matrix Factorization method called Neighborhood Regularized Logistic Matrix Factorization (NRLMF)  [24] , and the Kronecker (kernel) Regularized Least Square regression method KronRLS (a kernel-based method, as NN-MT)  [18, 19]. The KronRLS and NRLMF methods were published based on their prediction performance on four protein family datasets, Nuclear Receptors (NR), GPCR, Ion channels (IC), and Enzymes (E) that contained respectively 90, 636, 1476 and 2926 interactions  [10]. The KronRLS method uses a kernel K molecule for molecules that is defined by: K molecule ¼ 1 2 ðK SIMCOMP þ K GIP;m Þ ð5Þ where K SIMCOMP is a structure similarity kernel  [65] , and where K GIP,m is a Gaussian kernel that compares the interaction profiles of molecules against the proteins of the dataset  [18].
For proteins, the kernel K protein is defined by: K protein ¼ 1 2 ðK sequence þ K GIP;p Þ ð6Þ where K sequence is a protein sequence similarity kernel also based on the Smith-Waterman score  [44] , and K GIP,p is a Gaussian kernel that compares the interaction profile of proteins against the molecules of the dataset. A specific feature of the NRLMF method is that it integrates a neighborhood regularized method which allows to take into account only the K nearest neighbors to predict a given (protein, ligand) interaction (in practice, the authors used K = 5). We performed benchmark experiments on these family datasets using the PyDTI package. This package initially contained the KronRLS, a variant of it called KronRLS-WNN, and the NRLMF methods, and the kernel matrices K protein and K molecule calculated for the four family datasets.
In all experiments, we compare the intrinsic performances of the algorithms: the similarity measures used are the same for all methods. More precisely, the three methods used the kernels available in PyDTI: the structure-based K SIMCOMP for molecules, and a kernel K sequence based on the Smith-Waterman score. In addition, KronRLS also used the K GIP,m kernel, leading to the K molecule kernel for molecules defined in Eq 5, and the K GIP,p kernel, and to the K protein kernel for proteins defined in Eq 6. The two other methods do not use the K GIP kernels because they do not take into account information about interaction profiles. We also performed benchmark experiments on a dataset gathering more diverse protein and ligands. To this end, we used the DrugBank-based dataset S 0 described in Section 2.
All cross-validation experiments were performed building test sets from S 0,2000 and using all remaining data points in S 0 for training. Table  3  presents the performance of the three considered methods on the protein family datasets. Globally, the performance of all methods are high and close, with AUPR scores above 0. 9 in most of the cases. On average, the NRLMF and NN-MT methods are on par and lead to the best results. These results are consistent with those reported in  [24]. Table  4  confirms the tendencies observed in Table  3. Although the performances are slightly lower on this more diverse S 0,2000 dataset than on the family datasets, they remain high, with NRLMF and NN-MT keeping the best AUPR scores. As discussed in Sections 3. 1 and 3. 2, various prediction methods lead to such high performances because, in the protein family or S 0,2000 datasets, predictions are averaged over test pairs in which the protein and/or the molecule might be orphan, or not.
These averaged results hide less favorable situations, typically double orphan samples. Because these cases are common and important when predicting specificity of a new drug candidate at the proteome scale, we would like to stress that comparing methods in orphan cases is a more stringent and relevant test. In such cases, the performance are expected to be more modest and the methods might not rank in the same order. Therefore, we ran the three methods using a LOO-CV scheme on double orphan (protein, molecule) pairs on the same datasets. We considered the LOO-CV scheme as it is particularly convenient to test double orphan drug-target interactions. In these experiments, for each tested (p, m) pair, interactions involving the considered protein or the molecule are ignored in the train set.
The LOO-CV schemes were balanced in positive and randomly chosen negative pairs. In order to better explore the performance of the considered methods in this doubleorphan setting, we used two versions of the two kernel-based methods, initially introduced for KronRLS. More precisely, in  [19] , the authors proposed an approach called WNN (weighted nearest neighbor) that, for each orphan molecule m (resp. protein), an interaction profile is computed by summing the weighted profiles of non orphan molecules in the dataset. The weighting depends on the similarity between the orphan molecule and all other non orphan molecules. This predicted profile is used in the training to predict labels to all (protein, m) pairs of the dataset. Thus, in the first version of KronRLS  [18] , all the labels of (protein, m) pairs involving the orphan molecule m were set to 0.
Based on this WNN procedure some of these non interactions might be re-qualified as true interaction before training the predictor. In other words, the WNN algorithm can be viewed as a mean to de-orphanize molecules or proteins in order to help the predictions on such cases. In the following, we will call KronRLS-WNN the KronRLS method ran with the WNN algorithm. Using the PyDTI package, we also considered a version of NN-MT in which the WNN algorithm is the implemented, and call it NN-MT-WNN in the following. Table  5  presents the results of the double-orphan benchmark on the family datasets. Surprisingly, in these double-orphan experiments, the NRLMF method has very modest results and does not perform as well as the other methods. The results of NN-MT remain well above the random performance of 0.
5, but not the KronRLS method. The WNN algorithm dramatically improves the performance of KronRLS, and to a lesser extent those of NN-MT, and overall, the NN-MT-WNN algorithm leads to the best performance in most cases. Table  6  presents the results of the double-orphan benchmark S 0,2000 dataset. We did not run the NRLMF method in this experiment, because it was computationally too intensive in this LOO-CV, and because it already gave very poor results on the easier family dataset. Moreover we shortened the train set of KronRLS and KronRLS-WNN methods by considering only the thousand molecules (resp. proteins) closest to the molecule (resp. protein) of the test sample. Thus, the computation time was reduced to some hours instead of months which made those methods computationally reasonable.
Overall, the scores are lower on this dataset than on the family datasets because S 0,2000 is a more diverse dataset on which predictions are more difficult, in general, than on the family datasets. However, the same tendency is observed: NN-MT performs better than KronRLS, and when the WNN algorithm is used, NN-MT-WNN performs better than KronRLS-WNN. Overall, the results of these benchmarks show that the NN-MT method present state-of-theart or better results on the protein family datasets and the more diverse DrugBank-based dataset. In the general case, it appears to be a good default method in terms of performance, number of parameters and computational efficiency, which are important issues for non expert users. In the specific double-orphan case, only the two kernel-based methods NN-MT and KronRLS lead to performance well above those of a random predictor.
The WNN algorithm, proposed in  [19]  improves the performance of KronRLS and of NN-MT, but resulting NN-MT-WNN method lead to the best performance. Finally, it is interesting to compare the computational complexities of the methods as a function of the number of hyper-parameters that they contain. Indeed, these hyper-parameters need to be optimized by cross-validation, leading to heavy computational issues in the case of the large-scale datasets used in proteome-wide chemogenomics. As can be seen in the PyDTI package, NRLMF has 5 regularization parameters, KronRLS has 2 hyper-parameters (decay parameter T and the weight parameter used to combine kernels; the regularization parameter and the bandwidth of the GIP kernel are fixed), and NN-MT has 1 hyper-parameter (regularization parameter C for SVM).
In practice, the optimization of NRLMF in the LOO-CV scheme was out of reach, requiring several days of calculation while the other methods required hours, which represents a limitation of this method. This could explain in part the very low performances displayed by NRLMF in the double-orphan experiment. In addition, the doubleorphan case was not considered in  [24] , and therefore, we do not have any performance reference for this case. In this setting, latent vectors describing proteins and ligands are both estimated from non-orphan neighbors, and interaction prediction may fail in this situation, and contribute to the poor performance observed. However, we did cross-validate NRLMF parameters in the double-orphan setting in the case of the family NR dataset (the smallest dataset used in this section).
5) and the Tanimoto kernel for molecules (with length of path 8), with a train set including: • all positive intra-task pairs (i. e. all known interactions involving the protein or the ligand of the test pair), and around ten times more randomly chosen intra-task negative pairs. • a small number of the closest positive extra-task pairs (i. e. a number similar to that of intratask positive pairs), and a similar number of randomly chosen negatives extra-tasks pairs. This should provide good default parameters to use the NN-MT method in a straightforward manner for users that are not familiar with machine learning approaches. We used the DrugBank database to build several datasets that illustrate various prediction contexts and that we made available online to the community for future benchmarking studies.
We also updated the PyDTI package  [24]  with an implementation of NN-MT together with several cross-validation schemes and our DrugBank-based dataset. This allowed us to compare the NN-MT method to recent approaches developed on drug target interaction prediction  [18] [19] [20] [21] [22] [23] [24]. In the context of wide-scale prediction of molecule specificity, the DrugBank-based dataset is more relevant that the family datasets that have been widely used. Indeed, it contains a set of proteins that can be viewed as a relevant druggable proteome to train and test computational models for drug specificity prediction. The benchmark study comparing NN-MT to the matrix factorization NRLMF and the kernel-based KronRLS algorithms on family and DrugBank-based datasets showed that, all methods displayed high performances, NRLMF and NN-MT leading to the best results.
However, on the more demanding double-orphan tests performed on the same datasets, NRLMF performed much poorer than the kernel-based NN-MT and KronRLS algorithms. In this orphan case, the WNN algorithm makes it possible not only to significantly improve the performance of KronRLS, but also that of NN-MT, the NN-MT-WNN algorithm leading to the best results. We formalized (protein, ligand) interaction prediction as a classification problem because they can be solved at a reasonable computational cost on large datasets. Whenever the purpose would be to predict the relative affinities of molecules for a set of proteins, the predicted scores can be used to rank all interactions. However, this question could be also solved using a regression algorithm when predicting the affinity between pair of molecule and protein  [26].
Note that in such an approach, the affinity of all (protein, ligand) pairs in the training data is required, which is rarely available on large scale. Although the protein-ligand interaction process takes place in the 3D space, we chose to encode the two partners based on features that do not require 3D information. Indeed, the bound conformation of the ligand, and the 3D structure of the protein binding pocket is unknown in most cases, which prevents predictions on large scale. However, we are aware that a method using 3D information to encode the interaction can be of interest on more restricted datasets (i. e. not covering the druggable human proteome) as those available from the PDB database  [28, 29]. Since the prediction performance strongly depends on the distance between the predicted interactions and the train set, it could be relevant to apply the multi-kernel learning (MKL) framework  [66].
When the absorber is also a good scintillator at low temperature, a fraction of the deposited energy in the absorber is converted into a light signal. This can be read out by a suitable light detector (LD) facing the crystal. By means of the read-out of heat and light signal the identification of the type of interacting particle is feasible, thus allowing for the rejection of α particle interactions. Currently, the best choice for light detection in such ultra low background and low temperature environment is the operation of an auxiliary bolometer. CUPID-0 uses Ge absorbers equipped with a thermal sensor similar to the one used in the main absorber. In Fig. 1  a schematic view of a CUPID-0 single module is shown. The ZnSe crystals and the Ge LDs are thermally coupled to the heat bath by means of a copper structure.
This work was partially supported by the  European Research Council  (FP7/2007-2013) under contract LUCIFER no. 247115. We are particularly grateful to  M. Iannone  for the help in all the stages of the detector construction,  A. Pelosi  for construction of the assembly line,  M. Guetti  for the assistance in the cryogenic operations,  M. Lindozzi  for the development of cryostat monitoring system and the mechanical workshop of LNGS (in particular  E. Tatananni ,  A. Rotilio ,  A. Corsi , and B.
(Note that a small sum of probabilities is assigned to certain species of choanoflagellates for animal-specific gene families, and vice versa. This is an expected result, as, in our method, every gene family is assigned a finite probability of presence in every species, producing a small background signal when summed over the approximately two thousand animal-or choanoflagellate-specific gene families. Variation in this background signal across species is due to species-specific effects on BLAST scores arising from database size and other factors, as well as intrinsically noisy scores assigned to weak BLAST hits. ) (b-c) Ranked order of gene family retention for (b) animals and (c) choanoflagellates, similar to (a), but with the addition of gene families originating in the last common ancestor of Opisthokonts and of eukaryotes.
, 2006). (We note that the Bioanalyzer software could not calculate RIN for several cultures. ) If we were not able to obtain high-quality total RNA after the first attempt for any culture, we repeated cell growth, centrifugation and total RNA isolation up to a maximum of 5 times, and selected the best available total RNA sample to use for transcriptome sequencing. We produced a rough estimate of the amount of choanoflagellate total RNA present in each sample by calculating the ratio of choanoflagellate to bacterial ribosomal RNA peaks (18S vs. 16S and 28S vs. 23S) and multiplying the resulting fraction by the total amount of RNA present in the sample (Supplementary file 1).
The second, repeat sequencing run did not experience this issue. We incorporated both sequencing runs for affected libraries into subsequent analyses (including Quality trimming and Error correction, see below). We produced between 23 million and 61 million paired-end 100 bp sequencing reads per library (Supplementary file 1). Raw sequence reads are available at the NCBI Short Read Archive with the BioProject identifier PRJNA419411 (accession numbers for each species are listed in Supplementary file 1).
Error correction We performed error correction on trimmed reads using Reptile v1. 1  (Yang et al. , 2010)  following the authors' instructions, with the modifications described below. We began by using the 'fastq-converter. pl' script to convert from FASTQ and to discard reads with more than one ambiguous character 'N' in any window of 13 bases. For reads with one 'N', we chose the character 'a' as the substitute for 'N', as all of the characters in our input reads were in upper case (A, C, G, or T); thus, we could later recognize 'N' bases converted in this step. Next, we tuned parameters using the 'seq-analy' utility following the authors' instructions, in four steps: (1) Running 'seq-analy' with default settings. (2) Adjusting the input settings to 'seq-analy' using the results from step 1.
De novo transcriptome assembly We performed de novo transcriptome assembly on trimmed, corrected sequence reads and quality files with Trinity release 2013-02-25  (Grabherr et al. , 2011)  with '-min_contig_length' set to 150 and all other parameters at their default values. We chose a minimum contig length of 150 (rather than the default of 200) so as not to exclude assembly fragments that might encode predicted proteins with lengths between 50 and 66 amino acids, because some domains in the Pfam database are as short as 50 amino acids. Because none of the species we sequenced had an available genome assembly, we did not know whether transcripts might be encoded in overlapping positions within the genome. To test this possibility, we repeated each Trinity assembly with the addition of the '-jaccard-clip' option and compared the estimated number of fusion transcripts predicted by Transdecoder release 2012-08-15  (Haas et al.
Prediction of amino acid sequences from assembled transcripts and elimination of redundant transcripts We predicted proteins from decontaminated contigs with Transdecoder release 2012-08-15  (Haas et al. , 2013)  with a minimum protein sequence length of 50. We noticed that many of the proteins originating from different contigs within a species were completely identical along their entire length. Furthermore, we also observed many contigs whose predicted proteins were a strict subset of the predicted proteins from another contig. For example, contig one might encode predicted proteins A and B, and contig two might encode two predicted proteins exactly matching A and B, and a third predicted protein C. We removed both types of redundancy (exact matches and subsets) from the set of predicted proteins, and we also removed the contigs from which they were predicted (Supplementary file 1).
Measurement of expression levels and elimination of noise transcripts To estimate expression levels, we mapped sequence reads to decontaminated, non-redundant, Tandem Repeats-masked contigs using the Burroughs-Wheeler Aligner, BWA, version 0. 7. 5a  (Li and Durbin, 2009). We ran BWA 'mem' with the '-a' option to report all equally best hits, with all other parameter values left at their defaults. We converted BWA output to BAM format using SAMtools version 0. 1. 18 (Li et al. , 2009)  and ran eXpress version 1. 4. 0  (Roberts and Pachter, 2013)  with default parameter values in order to produce estimated expression levels, in fragments per kilobase per million reads (FPKM). The distribution of FPKM values (Figure  1 -figure supplement 3d) had a peak near 1, with steep decreases in the number of contigs at lower values.
Therefore, we chose an extremely conservative noise threshold two orders of magnitude below the peak, at FPKM 0. 01, and discarded contigs (and their associated predicted proteins) below this value (Supplementary file 1). The final sets of contigs are available as Dataset 1, and the proteins as Dataset 2  (Richter et al. , 2018). FPKM values for contigs are given in Dataset 3  (Richter et al. , 2018). We also submitted the final sets of contigs and proteins to the NCBI Transcriptome Shotgun Assembly (TSA) sequence database. The contigs (and their associated proteins) available in the TSA differ from our final sets in three ways: (1) The TSA database does not accept contigs with lengths less than 200, whereas our minimum was 150; (2) The submission system identified and rejected a small number of contigs as bacterial contaminants; (3) The submission system identified and required us to trim a small number of contigs to remove Illumina adapter sequences that were missed by our screen with Trimmomatic.
Tests of choanoflagellate species identity and contamination with animal sequences To confirm the identity of the choanoflagellate cultures we sequenced, we compared our transcriptome data to seven protein coding genes with choanoflagellate sequences previously available in GenBank: actin, alpha tubulin, beta tubulin, EF-1A, EFL, HSP70 and HSP90. To search the transcriptomes for each gene, we downloaded all previously available choanoflagellate sequences from Gen-Bank, aligned them using FSA version 1. 15. 7  (Bradley et al. , 2009)  with default parameter values, and trimmed the alignments using Gblocks  (Talavera and Castresana, 2007)  with allowed gap positions set to 'half' and all other parameter values set to their most permissive. We next built HMMs for each trimmed alignment using hmmbuild from the HMMer package version 3.
0  (Eddy, 2011)  and searched the contigs from each transcriptome (and their reverse complements) with hmmsearch, both with default parameter values. In each case, the top hit we retrieved for each transcriptome (the contig with the lowest E value) matched the corresponding sequence for the species in GenBank. We tested for the possibility of animal contamination within choanoflagellate transcriptomes using the same set of seven HMMs to search the nucleotide coding sequences from all 59 species in our data set. For each target species, we retained the top three hits by E value. For each gene, we aligned the resulting sequences using using MAFFT version 7. 130b  (Katoh and Standley, 2013)  with the parameters '-maxiterate 1000 -localpair' and trimmed alignments using trimAl version 1. 2rev59  (Capella-Gutierrez et al.
Within animals, we defined three sub-groups: sponges (O. pearsei, Ephydatia muelleri, A. queenslandica), ctenophores (M. leidyi), and later-branching animals (T. adhaerens, cnidarians, bilaterians). An animal-specific gene family present in at least two of these three sub-groups would be considered to have been present in the Urmetazoan (and thus to have evolved on the animal stem lineage); this inference would not change if either sponges or ctenophores were the earliest-branching animals. Similarly, a choanoflagellate-specific gene family was required to be present within both craspedids and loricates [two well-defined groups of species  Leadbeater, 2015; Carr et al. , 2017) ] to be considered present in the Urchoanozoan. Inferred group presences for each gene family are available in Dataset 5  (Richter et al.
0  (de Hoon et al. , 2004) , with all other parameter values set to their defaults. We visualized heat maps using Java TreeView version 1. 1. 6r4 (Saldanha, 2004) and color palettes from ColorBrewer  (Harrower and Brewer, 2003). For display purposes, we restricted Figure  2  to gene families inferred to have been present in at least one of six nodes representing last common ancestors of interest: Ureukaryote, Uropisthokont, Urholozoan, Urchoanozoan, Urchoanoflagellate, and Urmetazoan. The full heat map for all gene families with representatives from at least two species is shown in Figure  2 -figure supplement 4. Gene families present, gained and loss at each ancestral node are listed in Dataset 6  (Richter et al. , 2018).