dataesr/scientific-paragraphs-categorization huggingface dataset:
To attribute functions of an AltProt from this list of PPIs, we retrieved the known interactions from STRING, BioGrid, and IntAct database and included the identified crosslinked interactions (Figure  5C ). We observed (green lines) that 10 interactions were already described. These interactions found were: H3F3A-H2AFJ, ITGA5-ITGB1, YWHAZ-YWHAQ, PHB-PHB2, EMC2-EMC8, COX7B-COX4I1, ATP5A1-ATP5F1, PDIA6-PLEKHO1, HLA-B-B2M, and B2M-HLA-A. For the RefProts that did not present referenced STRING interaction, an enrichment has been performed to expand the network (Figure  S1 ). With this expanded network a molecular function GO term enrichment analysis was performed with the ClueGO App from Cytoscape. For the resulting network (Figure  6 ), the interactions between AltProts and RefProts were displayed along the GO terms enriched.
11 A ˚between the two crosslinked lysines (Figure  7A ), which fits with the crosslinking range described for DSSO. HLA-B is part of the MHC class 1 and oversees the presentation of antigenic peptides of 8-13 residues that are recognized by CD8 + T cells driving antigen-specific immune response. Due to the importance of this system for tumor-derived antigens, informatics tools have been developed to predict the binding of peptides to this class of proteins and one of them is NetMHC-4. 0, 60 this tool is based on a machine-learning algorithm that predict the capacity of binding to a protein and peptide sequence based on this size and amino acid constitution, giving the possibility to predict interaction for AltProt not referenced in other tools based on databases identification. The results obtained using the complete sequence of the AltProt divided in 8-14-mers were predicted as weak binding for the alleles HLA-B1502, HLA-B1503, HLA-B1517, HLA-B4001, HLA-B4002 and HLA-B5701.
63 The AltProts that were crosslinked to HIST1H4F were IP_2331010 (AltKDM4C, 3 0 UTR +2 ORF), IP_672441 (AltRPS15AP10, ncRNA), and IP_709097 (AltAC123769. 1, ncRNA). These interactions were found in the cytoplasmic, nuclear, and membrane-bound fraction, respectively. According to the COMPARTMENTS subcellular localization database, 64 HIST1H4F is found experimentally in the nucleus and cytosol, moreover, a GO term linked to the membrane is referenced in UniProt (P62805). This could indicate that these AltProts might be involved in mRNA synthesis or in the interaction between the TAFs and the histones. Another interaction was found involving another histone; H3F3A and IP_627699 (AltSLC41A3, +3 ORF mRNA CDS). A crosslink was found between IP_557247 (AltMRRFP, ncRNA) and ORC1, which is a crucial protein in the initiation of DNA replication by the interaction with MYST histone acetyltransferase 2  65  and has annotated STRING interactions with the TAF family.
Limitations of the study Our study has some limitations and the first one is related to the limited spread of the concept of alternative (ghost) proteins, resulting in a lack of information and established methodologies to unravel the function of such proteins. Secondly, by employing a detergent and microcentrifugation-based subcellular fractionation kit, cross-contamination of cellular fractions can be an issue. Hence, a more efficient technique for subcellular fractionation, like gradient-based ultracentrifugation could be employed to determine the location of AltProts and generate a finer fractionation. Additionally, given the huge database used (OpenProt), a manual check of the MS/MS spectra associated with the interaction of interest must be done. Such large databases call for more stringent analyses on (crosslinked) peptide identifications.
Shotgun data analysis RAW data obtained by nanoLC-MS/MS analysis were analyzed using Sequest HT in Proteome Discoverer V2. 5 (Thermo Scientific) with the following processing and consensus parameters: trypsin and chymotrypsin as enzymes, two missed cleavages, methionine oxidation and N-terminus acetylation as variable modifications, carbamidomethylation of cysteines as static modification, minimum peptide length of 6 amino acids, minimum precursor mass tolerance: 10 ppm and fragment mass tolerance: 0. 02 Da. For RefProts, the protein database used was Homo sapiens UniProtKB v. 2022_02 reviewed and unreviewed. Validation of Sequest results was performed using Percolator with a strict FDR set to 1%. A consensus workflow was then applied for the filtering and results reporting. At the consensus step, the peptide validation for PSM and peptides was established between 0.
01 and 0. 05 FDR with a minimum peptide length of six. The minimum number of peptide sequences for a protein was selected as two. Finally, at the Protein FDR Validator validator, the target FDR was set as 0. 01. For AltProts, the protein database used was Homo sapiens OpenProt v1. 6 database which contains RefProts and predicted AltProts detected in mass spectrometry experiments with at least one unique peptide leading to a total of 184,706 sequences. Validation of Sequest results was performed using Percolator with a strict FDR set to 1%. At the consensus step, the peptide validation for PSM and peptides was established between 0. 01 and 0. 05 FDR. Peptide confidence set at high with a minimum peptide length of six. The minimum number of peptide sequences for a protein was selected as one.
Crosslink data analysis The obtained data were analyzed using the XlinkX algorithm of the Heck Lab (Utrecht, Netherland) at Proteome Discoverer V2. 5 (Thermo Scientific). DSSO (158. 0037 Da) was defined as the crosslinker. The protein database used was the Homo sapiens OpenProt v1. 6 database which contains RefProts and predicted AltProts detected in mass spectrometry experiments with at least one unique peptide leading to a total of 184,706 sequences. First, protein identification was made by Sequest HT considering the following parameters: Trypsin/LysC and Chymotrypsin as enzymes, maximum two missed cleavages, peptide length from 6 to 150, precursor mass tolerance of 10 ppm and fragment mass tolerance as 0. 02 Da. The dynamic modifications included were methionine oxidation, cysteine carbamidomethylation, N-terminus acetylation, DSSO amidated, hydrolyzed and Tris form.
Modeling and prediction of interactions between AltProts and RefProts Structural models of AltProts were generated with I-TASSER (Iterative Threading ASSEmbly Refinement). Reference protein models were downloaded from the AlphaFold Protein Structure Database. AltProts models with C-score between -5 and +2 (most stable) generated by I-TASSER were considered for protein-protein interaction (PPI) prediction, which were generated by ClusPro. The RefProts were assigned as receptors and the AltProts as ligands. The docking interactions were generated without the crosslink influence. The resulting models were ranked by stability order and displayed by YASARA view. Using the data obtained from XlinkX, the distance between the lysine residues involved in the AltProt-RefProt crosslink was measured and displayed in the model.
The Euclid Space Telescope will provide deep imaging at optical and near-infrared wavelengths, along with slitless near-infrared spectroscopy, across ∼15 000 deg 2 of the sky. Euclid is expected to detect ∼12 billion astronomical sources, facilitating new insights into cosmology, galaxy evolution, and various other topics. In order to optimally exploit the expected very large dataset, appropriate methods and software tools need to be developed. Here we present a novel machine-learning-based methodology for the selection of quiescent galaxies using broadband Euclid I E , Y E , J E , and H E photometry, in combination with multi-wavelength photometry from other large surveys (e. g. the Rubin LSST). The ARIADNE pipeline uses meta-learning to fuse decision-tree ensembles, nearest-neighbours, and deep-learning methods into a single classifier that yields significantly higher accuracy than any of the individual learning methods A99, page 1 of 36.
Introduction The study of galaxies plays a pivotal role in the effort to understand how the baryonic component of the Universe evolved across cosmic time; it is on the size scale of galaxies that key processes such as star formation, chemical evolution, black hole growth, and feedback predominantly take place. Large, systematic imaging and spectroscopic surveys are among the most valuable resources for investigating galaxy evolution, allowing a wide range of studies, from the identification and study of rare or elusive objects (e. g. Alexandroff et al. 2013)  to statistical studies of large samples of galaxies (e. g. Kauffmann et al. 2003). A variety of ground-or space-based surveys have already provided rich databases for investigating questions surrounding the evolution of galaxies, for example the Sloan Digital Sky Survey (SDSS;  York et al.
g. LSST). 1 AB magnitudes are used herein. Automated classification and derivation of physical properties are crucial steps towards the scientific exploitation of data from any large astronomical survey, and traditionally this would be done using colour-colour methods (e. g. Haro 1956 ;  Daddi et al. 2004; Leja et al. 2019)  or by fitting spectral models or templates (e. g. Bolzonella et al. 2000; Fotopoulou et al. 2012; Gomes & Papaderos 2017). Machine-learning techniques have proven to be particularly powerful in this context since they have the ability to detect structure, correlations, and outliers in large, multi-dimensional datasets, producing models that are typically stronger and more efficient than the traditional methods (e. g. Baqui et al. 2021; Ulmer-Moll et al. 2019; Logan & Fotopoulou 2020; Clarke et al.
As such, they are likely to discard a significant quantity of otherwise useful information that is present in a broadband SED. In this context, machine-learning methods offer a promising alternative since they are able to perform selection from within highly multi-dimensional datasets, and can be tuned to make a trade-off between purity and completeness that is appropriate for a desired science application. Indeed,  Steinhardt et al. (2020)  recently explored the selection of quiescent galaxies using the unsupervised t-distributed stochastic neighbour embedding method (van der Maaten & Hinton 2008; van der Maaten 2014), reporting a significant improvement over the UV J and template fitting methods. In this paper we present a new supervised machine-learning method for the separation of quiescent and star-forming galaxies using Euclid and ancillary photometry, which is designed to handle sparse data and can provide photometric redshift estimates where necessary.
Metrics of model quality To evaluate our classification models, we used several metrics that are useful to quantify model quality. Precision, P, is the fraction of assignments to a particular class that are correct, calculated as P = TP TP + FP , ( 1 ) where TP is the number of true positives and FP is the number of false positives. Precision is also sometimes known as 'purity' in astronomy, or the 'positive predictive value'. Recall, R, is the fraction of galaxies of a particular class within the dataset that are correctly classified as such. It is calculated as R = TP TP + FN , ( 2 ) where FN is the number of false negatives. Recall is also sometimes known as 'completeness' or 'sensitivity'. The F1-score is the harmonic mean of the precision and the recall and, as such, provides a more general metric for model quality  (Dice 1945; Sørensen 1948).
SED Deep Fig. 5. Effect of various photometric pre-selection criteria on the number and redshift distribution of galaxies in the four mock catalogues. We show four cases: (i) only a detection in I E is required; (ii) detection is required in all four Euclid bands (I E , Y E , J E , H E ); (iii) detection is required in u and all four of the Euclid bands; and (iv) detection is required in ugriz and all of the Euclid bands. As described in the main text, for all bands we adopt a signal-to-noise detection threshold ≥3. where we have opted to give equal weights to P and R. The metrics P, R, and F1-score have values between 0 and 1. Since there is a large imbalance between classes in the datasets used here, we compute the metrics separately for each class. Unless otherwise stated, the values of P, R, and the F1-score we quote are computed for the quiescent galaxy class only.
The learning algorithm After completion of the feature engineering and preprocessing steps described in Sect. 4. 2, the data are split into a training set and test set with a 2:1 ratio between the two. This ratio was chosen to give a good balance between having a large number of examples on which to train the learners, while still having a test set that is representative of the whole dataset 5. The split is done randomly, without attempting to balance target labels, redshift, or any other observational properties, and each execution of the pipeline produces a different train-test split. The pipeline then trains binary classification models on the training dataset, using five different 'base-learners' (see Fig. 6 ). Three of the base-learners are tree-ensemble methods with somewhat different implementations (CatBoostClassifier 6  version 0.
23. 2; LGBMClassifier 7  version 0. 90; RandomForestClassifier), one is nearest-neighbours-based (KNeighborsClassifier), and one is deep-learning-based  (MLPClassifier). All are open source and are briefly summarised below. The Python Scikit-Learn 8  version 0. 22. 2 package  (Pedregosa et al. 2011 ) offers various machine-learning methods with excellent functionality and inter-operability with a multitude of in-package preprocessing tools. From Scikit-Learn we make use of the nearest-neighbours-based, non-parametric KNeighborsClassifier method, the Multi-layer Perceptronbased MLPClassifier, and the randomised decision-tree classifier RandomForestClassifier  (Breiman 2001). Several other, advanced machine-learning methods are used in our pipeline. CatBoost  (Prokhorenkova et al. 2018 ) is a gradient-boosting, tree-ensemble method that offers high performance classification or regression, using 'ordered-boosting' in place of the classic boosting algorithm to significantly reduce the 'prediction shift' commonly associated with the latter.
The XGBoost algorithm uses gradient boosting and has several key innovations, including sparsity-aware split-finding, which is of particular relevance for datasets containing a significant fraction of missing values (e. g. photometric non-detections, masked or unobserved areas). Our application of these learning algorithms to the data at hand reveals that no single algorithm consistently outperforms the others over the full range of classification problems posed herein. Thus, our pipeline employs model ensembling and interactive algorithm selection. The base-learners are trained within a stratified k-fold procedure: the training data are shuffled and split, with replacement, into five similarly sized folds, ensuring that each fold contains the same proportion of each target class. Each base-learner is trained five times, each time leaving out a different fold, for which a set of out-of-fold (OOF) class predictions is produced.
To further improve model quality, the pipeline contains our implementation of the 'generalised stacking' method proposed by  Wolpert (1992). A linear discriminant analysis or MLPClassifier meta-learner is trained to classify galaxies using the OOF predictions from the five base-learners as features. This is performed within a stratified k-fold procedure as described above, and produces a new set of OOF predictions for the training dataset, in addition to a new set of predictions for the test dataset. A second iteration of meta-learning is then performed, this time training on just two features: (i) the results of the hard-voting ensemble and (ii) the OOF predictions produced in the previous iteration of meta-learning. Finally, the resulting model is used to predict classes for the test data.
However, the user can instead use a subset of the base learners, or a single base-learner, when appropriate. For example, we opt to use XGBoostClassifier for the selection of quiescent galaxies at 2. 5 < z < 3. 0, where its sparsity awareness confers a significant advantage over the other classifiers. In addition, the pipeline has a 'fast mode' that uses LightGBM for all classification or regression tasks, at the cost of a small but significant reduction in P, R, and F1-score (∼0. 01-0. 03). Timed on a mid-range laptop with a quad-core Intel i5-8350U CPU and 16 Gigabytes of RAM, the pipeline used in fast mode takes at total of ∼2 min to train its classifier on a dataset with ∼120 000 examples and 70 features, compared to ∼74 min when using the default (5 base-learners) pipeline configuration.
Missing data imputation strategy One of the advantages of our machine-learning approach to galaxy classification is the possibility to efficiently deal with missing data. A subset of galaxies in the mock catalogues is undetected, or unobserved, in one or more filters; we consider it highly desirable to include them in our analysis, where possible, for several reasons: (i) such objects enlarge our training dataset; (ii) non-detection in some bands is likely to carry useful information for our learning algorithm (e. g. u-band drop-outs); and (iii) the upcoming large surveys (e. g. Euclid, LSST, etc. ) that motivate this work will produce large datasets where many galaxies have missing data in one or more bands, and such objects need to be utilised to make the most effective use of the survey data.
For comparison, we show the F1-scores when only features derived from the u and Euclid bands are used. We also show curves representing the u -I E , I E -J E colour-colour selection method proposed by B20. A99, page 13 of 36 A&A 671, A99 (2023) which values are missing, and its subsequent use of missing values when performing splits, allows the XGBoost algorithm to build stronger classifiers than other learners when there is a high fraction of (informative) missing values in the dataset, as is the case here (see Fig. 4 ). It is also interesting to note that the Int Wide catalogue contains only 16 quiescent galaxies in the range 2. 5 ≤ z ≤ 3 with detections in all of the Euclid bands. As such, the training set contains on average 10. 7 quiescent galaxies, and the test set 5. 3, making this a 'few-shot learning' problem.
Our pipeline achieves a normalised mean absolute deviation of < ∼ 0. 03 and a fraction of catastrophic outliers of < ∼ 0. 02 when measured against the COSMOS2015 photometric redshifts of  Laigle et al. (2016). These values appear to improve on the results of the Euclid Photometric Redshift Challenge (Euclid Collaboration 2020), but we emphasise that the mock photometry catalogue used therein is of a significantly different construction to those we have used herein, making any inter-comparison of photometric redshift metrics potentially unreliable. -The inclusion of galaxy redshifts among the train and test datasets offers significant improvement in the quality of our classification models, even when the redshifts are relatively noisy or incomplete. -We have investigated the potential impact of various systematics.
For comparison we also show the results from the LePhare template fitting method applied to the SED Wide mock catalogue (see Sect. 6. 4), using Euclid, ugriz, W1, W2, and 20 cm photometry, and with redshifts fixed at the  Laigle et al. (2016)  values. error driven by photometric bands that are present in the multiwavelength dataset used to generate the labels, but which do not appear in the data that are seen by our pipeline. Under these conditions, the presence of noisy labels varies depending on the particular classification problem that is being addressed. For instance, when selecting quiescent galaxies at A99, page 29 of 36 A&A 671, A99 (2023) 0 ≤ z ≤ 3 from the Int Wide catalogue, without foreknowledge of redshifts, and with 33 per cent of the labels having been flipped, the metrics obtained when the test set labels contain errors are relatively poor, at P = 0.
For this experiment, the data are pre-binned by redshift as described in Sect. 5. 1, but we did not include the redshift values as a feature in the input data for the classification pipeline. The results are shown in Fig. B. 8, where it can be seen that classifiers trained on the SED Wide catalogue are indeed able to select quiescent galaxies from the Int Wide catalogue, albeit A99, page 30 of 36 Euclid Collaboration: Selection of quiescent galaxies with marginally lower F1-scores compared to models trained on the Int Wide catalogue itself. This opens up the interesting possibility of using machine-learning models trained on synthetic galaxy SEDs as a potential alternative to traditional colour-colour or template fitting methods (e. g. Girelli et al. 2019; Cecchi et al. 2019 ) of selecting quiescent galaxies that have redshifts (or other properties) for which there are no (or few) known examples.
(2016, see also Sect. 6. 4). This template fitting method clearly provides results of similar quality to our pipeline in the 1. 0 < ∼ z < ∼ 2. 0 redshift range, but at z < ∼ 1. 0 or at z > ∼ 2 the method significantly under-performs our transfer learning method. The under-performance of our LePhare template fitting method is likely caused, at least partly, by the absence of priors concerning (i) the known (or suspected) distribution of the galaxy classes within the redshift and colourspaces, and (ii) the relative importance (or weighting) that should be given to each data point in the broadband SED, aside from their signal-to-noise ratio.
B. 7. 2. Training on Deep and predicting on Wide survey data The Euclid Deep Survey will provide photometry (and spectra) in several fields for which there are pre-existing multiwavelength observations, allowing the construction of source catalogues with high-quality labels. In turn, this is expected to facilitate the training of classifiers, which can then be used to predict labels for the enormous number of sources that will be detected in the Euclid Wide Survey. An important question, however, is whether models trained using the deep field photometry are suitable for use in selecting quiescent galaxies in the wide field survey. While the Deep and Wide Surveys are expected to be somewhat similar (but clearly not identical) within the magnitude-space covered by the Wide Survey, at least 60 per cent of the galaxies in the Deep Survey are below (or close to) the I E 3σ detection threshold of the Wide Survey.
76, compared to 0. 82 when using only SED Wide catalogue. Thus, although it is possible to separate quiescent and starforming galaxies in the Wide Survey mocks, the F1-score suffers a significant penalty and is reduced by at least ∼0. 06. Clearly, the presence of a large number of additional, faint galaxies in the training set exacerbates at least some of the degeneracies described in Sect. 1, and induces the learning algorithms to place undue weight on galaxies near the faint end of the magnitude distribution. In this study we are, of course, only able to make use of mock Euclid photometry catalogues, but we must also consider how the training set for this task will be constructed from real Euclid data. Based on the analyses presented heretofore, we propose constructing the training set(s) from Deep Survey photometry from fields that have high-quality multi-wavelength observations (and spectroscopy), using the deepest available data in order to generate high-quality ground truth labels, but then training classification models on Wide Survey-depth observations.