dataesr/scientific-paragraphs-categorization huggingface dataset:
RefSeq Annotation V1. 1: Integration of Manually Curated Genes Once Annotation v1. 0 was released to the community, researchers who are experts of some specific gene families brought corrections to the automated predictions: Sometimes gene copies were missing, sometimes the predicted exon/ intron structure needed to be curated. Feedback was made from the experts to the IWGSC Annotation Group in order to release an updated version 1. 1. This concerns gene families CBFs, NLRs, PPRs, Prolamins, WAKs, and aminoacid transporters. A semiautomated process was developed in order to integrate manually curated gene models. It relies on a Python script using common tools like GenomeTools  (Gremme et al. 2013) , GFFCompare (Pertea and Pertea 2020), pyBEDTools  (Dale et al. 2011). GffCompare was used to check that the curated genes did not overlap each other (different teams may have curated the same gene) and also to identify the RefSeq Annotation v1.
2020). Using resequencing data of chromosome 3B from 20 T. aestivum accessions, it was shown that variable genes represent between 2 and 6% of pairwise comparisons with CHINESE SPRING. This weak percentage implies that approximations due to incomplete genome assembly and differences in gene predictions will strongly impact our capabilities to determine if a gene a really absent. Thus, an underestimated limit that prevents from accurate pangenome construction is the annotation step. Automated gene modeling is strongly dependent on the methods, tools, thresholds, used so that two annotations of the same genome are systematically different. Additionally, these differences are not only background noise. For instance, when the IWGSC RefSeq Annotation v1. 0 was produced by combining independent predictions from two pipelines (TriAnnot and PGSB), 20% of each gene set did not overlap any prediction from the other one.
Functional Annotation: Opportunities Automated functional annotation workflow based on sequence similarity and domain search has been established by IWGSC to assign gene ontology (GO) and function descriptions to the wheat reference gene set (IWGSC 2018). Although approaches based on local alignment search such as BLAST are straightforward and work well for certain species and gene families, the drawbacks are clear. It suffers from low sensitivity or specificity, depending on threshold choice and evolutionary distance of query gene set to species in the annotation source  (Sasson et al. 2006 ). In addition, error or lack of robust annotation evidence in the source databases hinder or bias the large-scale functional annotation analysis, especially in non-model crop species. To overcome these limitations, integrating various omics datasets from high-throughput experiments in combination with novel computational approaches has been considered for complementation to local sequence alignment methods, facilitating annotation of unknown genes or transferring functional knowledge from one gene to another.
2016; Jumper et al. 2021)  or heterogenous data and networks  (Cai et al. 2020; Peng et al. 2021). Several factors limit the application of deep machine learning approach for functional annotation in large-scale and unbiased manner. Firstly, although various omics and structure data are useful, only primary sequence is available for majority of unknown genes. Secondly, imbalance and incompleteness of GO database with respect to species and function categories can bias the learning step, and GO prediction task itself is a complex multi-label problem. Lastly, the quality of transferring gene model information between species that are evolutionarily distant needs to be assessed carefully. Nevertheless, despite these challenges, deep machine learning-based functional annotation and GO assignment have been successfully applied and will continue in many studies, with the support of the continuing expansion of highquality omics and experimental datasets.
NOEMA observations The observations are obtained with NOEMA and are part of the MPG-IRAM Observing Program PRODIGE (Project ID L19MB). In this program, we use the Band 3 receiver and the new PolyFix correlator, tuned with a local-oscillator (LO) frequency of 226. 5 GHz. PolyFix provides a full 16 GHz of bandwidth at coarse spectral resolution (2 MHz channel width) and is divided into 4 units (LSB Outer, LSB Inner, USB Inner and USB Outer). Simultaneously, we place 39 high spectral resolu-  Notes. (*) This is a characteristic radius at which there is a significant drop in the dust flux exponential profile, a proxy for the radius. Observations of Per-emb-50 were conducted in two separate periods for each antenna configuration. The C configuration data were observed during 2019 December 29th and 2021 January 5th.
The line observations are not done with self-calibrated data. The resulting continuum image, shown in Appendix A, is done with the Lower Inner (LI) continuum window and has a noise of 0. 2 mJy beam -1. Continuum subtraction and data imaging are done with the GILDAS package mapping using the uv_baseline and clean tasks. All line cubes are imaged using natural weight to minimize the rms, while the continuum maps are imaged with robust = 1, to improve the angular resolution. We image the continuum-subtracted cubes using the standard CLEAN algorithm and a manual mask for each channel. Once we converge to a final mask, we perform a final CLEAN down to the rms level using multiscale CLEAN algorithm implemented in mapping. This has an effect of reducing imaging artifacts (mainly negative emission bowls), thus improving the general image quality around bright sources.
Acknowledgements. The authors would like to thank the anonymous referee for their detailed suggestions, which helped improve the final version of this paper. The authors also thank the  IRAM  staff at the  NOEMA observatory  for their invaluable work making these observations possible. M. T. V. would like to thank  L. Testi  for his valuable discussions and comments. M. T. V. , J. E. P. , P. C. and D. S. -C. acknowledge the support by the  Max Planck Society. D. S. -C. is supported by an  NSF Astronomy  and  Astrophysics Postdoctoral Fellowship  under award  AST-2102405. A. F. thanks the  Spanish MICINN  for funding support from  PID2019-106235GB-I00. D. S. acknowledges financial support by the  Deutsche Forschungsgemeinschaft  through  SPP 1833:  " Building a Habitable Earth " ( SE 1962/6-1 ).
I. J. -S. has received partial support from the  Spanish State Research Agency (AEI)  through project number  PID2019-105552RB-C41. N. C. acknowledges funding from the  European Research Council (ERC)  via the  ERC Synergy Grant ECOGAL  (grant  855130 ). M. T. acknowledges support from project  PID2019-108765GB-I00  funded by  MCIN/ AEI /10. 13039/501100011033. This work is based on observations carried out under project number L19MB with the  IRAM NOEMA  Interferometer. IRAM is supported by  INSU/CNRS (France) ,  MPG (Germany)  and  IGN (Spain). This research made use of Astropy, 1 a community-developed core Python package for Astronomy (Astropy Collaboration et al. 2013Collaboration et al. , 2018)).
Astrophysical surv e ys rely heavily on the classification of sources as stars, galaxies, or quasars from multiband photometry. Surv e ys in narrow-band filters allow for greater discriminatory power, but the variety of different types and redshifts of the objects present a challenge to standard template-based methods. In this work, which is part of a larger effort that aims at building a catalogue of quasars from the miniJPAS surv e y, we present a machine learning-based method that employs convolutional neural networks (CNNs) to classify point-like sources including the information in the measurement errors. We validate our methods using data from the miniJPAS surv e y, a proof-of-concept project of the Javalambre Physics of the Accelerating Universe Astrophysical Surv e y (J-PAS) collaboration co v ering ∼1 de g 2 of the northern sk y using the 56 narrow-band filters of the J-PAS surv e y.
2019 ; Pasquet et al. 2019 ) , on multiband photometric data  (Sharma et al. 2020 ) , and even in the time-spectral domain  (Qu et al. 2021 ). It is straightforward to apply CNNs to sequential data, and to incorporate the information about measurement errors -for a general description of the technique, see also  Rodrigues, Abramo & Hirata ( 2021 ). In order to compare our CNN-based techniques with other well-established ML classification methods, we have also tested the performance of random forests (RFs; Breiman 2001 ) and the light gradient boosting machine (LightGBM;  Ke et al. 2017 ) , two powerful decision tree (DT;  Breiman et al. 1984  )-based algorithms. This work is part of a larger effort to classify miniJPAS pointlike sources. The first paper  (Queiroz et al. 2022 )  describes the construction of simulated data sets (mocks) that we used to train our algorithms, and in this paper we apply CNN and DT-based ML models to those mocks.
In particular, we present a technique that enables us to take into account the measurement errors in the Jspectra. We e v aluate the performances of the classifiers not only with respect to validation data sets, but also for the real miniJPAS point sources, by comparing the numbers of objects with those expected from the luminosity functions (LFs) in different magnitude ranges, redshift ranges, and for the different stellar types. Finally, we test the robustness of the classification against changes in the training sets, and we perform a feature importance analysis to e v aluate which miniJPAS filters are more rele v ant to distinguish between the different classes. In a closely related work, Mart ínez-Solaeche et al. (in preparation) focus on a class of well-established ML models, the artificial neural networks (NNs), to explore different input features as well as to implement data augmentation techniques that introduce hybrid objects (ad-mixtures of single, pure populations) and study how this affects the confidence of the classification.
2011 ; Newman et al. 2013 ) , as well as X-ray data from XMM. Ho we ver, in our applications we consider only the cross-match of miniJPAS with the SDSS Data Release (DR) 12 Superset (P ˆ aris et al. 2017 ), which contains visually inspected spectra and redshifts of all BOSS quasar candidates. As a result of that cross-match, we end up with 117 quasars, 40 galaxies, and 115 stars. Fig. 1  shows the histograms of the r magnitudes of the objects in the miniJPAS point-like source subsample, as well as the objects from the SDSS cross-match sample, which is also split into the different classes. The cross-match sample constitutes a 'truth table' that we can use to check the classification derived on the basis of the miniJPAS J-spectra. Although the SDSS cross-match sample constitutes an important test set, one should bear in mind that it is not only extremely small, but it is also biased in terms of brighter sources, stellar types, redshifts, etc.
Mock J-spectra ML algorithms are usually trained and validated using real-world data sets, and are subsequently applied to data that are as similar as possible to the training sets. Ho we ver, when real data are not available or are too scarce, simulations can be employed to either complement existing real-world training sets or to build entire training sets (see e. g. Hoyle et al. 2015 ; Ramachandra et al. 2021 ). Supervised learning algorithms depend on large and complete training sets with verified labels in order for the models to be properly fitted  (Deng et al. 2009  ). In the case of J-P AS/miniJP AS data, the numbers of objects with confirmed labels are barely large enough for us to test the algorithms -never mind training them. Moreo v er, catalogues of astrophysical objects with confirmed labels are typically biased due to the target selection processes prior to the spectroscopic observations.
2003 ). After specifying the types, luminosities, and redshifts of the objects in the mocks, we search for SDSS optical spectra and compute the fluxes and magnitudes in the J-PAS filters by convolving those spectra with the filters. The synthetic fluxes are similar to those measured by miniJPAS, except for the fact that their SNRs are typically much higher due to the nature of the SDSS spectroscopic observations. The next step is therefore to add noise to the synthetic fluxes in such a way that the final simulated data set has an SNR distribution that is consistent with the miniJPAS observations. At this point, care must be taken to reproduce the actual noise properties of the underlying real data set. As shown in  Queiroz et al. ( 2022 ) , for some filters the noise models turned out not to be well fitted by a Gaussian, but some are better fitted by slightly different distributions.
In this paper, unless noted otherwise, we train and test our ML methods with the mocks produced using the best-fitting noise models, labelled as noise 'model 11'. Finally, the mocks also model the pattern of non-detections (NDs) from the miniJPAS point-like source subsample. In order to train the ML models, we leave the fluxes exactly as they are in the catalogues, without any special treatment of those low SNR measurements. We used four data sets to train and validate our models. The training set is a balanced data set  (Johnson & Khoshgoftaar 2019 )  containing equal numbers of stars, galaxies, and quasars (10 5 of each); the validation set, which we used for the ML model selection, contains 10 4 objects of each class; and the 'balanced test set' contains another 10 4 stars, galaxies, and quasars.
M I N I J PA S P O I N T-L I K E S O U R C E C L A S S I F I C AT I O N In this section, we discuss the predictions in the miniJPAS pointlike source subsample, which contains 11 419 objects. Considering only the magnitude range of 17. 5 ≤ r < 23. 6, we are left with 7468 sources. We have spectroscopic confirmation for some of the objects in this data set, obtained by cross-matching the miniJPAS catalogue with the SDSS DR12 Superset (see Section 2. 1 ). The confusion matrix obtained for that sample is shown in Fig. 11. The typical magnitude range co v ered by this sample is 17. 5 ≤ r ≤ 22. 5 (see Fig. 1  ). Therefore, in order to see the degradation of the results on real data relative to the mocks, one should compare Fig. 11  with the first two bins from Fig. A1. The completeness of all classes is higher than 0.
We verified that, as a general rule, the main source of confusion is between galaxies and lowz QSOs in the faint end. Stars are more often confused with lowz QSOs as well, specially bluer types (O, B, A, and F), cataclysmic variables, and WDs. The redshift range of QSOs that were most often classified as galaxies is z ∈ [1. 0, 1. 6]. The performances of the classifiers decrease as the objects become fainter and noisier. We verified that the predictions with a mock test set are indeed consistent with our pre vious kno wledge about quasars, stars, and galaxy features, which reinforces the quality of the mock data and also of the ML models developed in this work. After validation, the ML models were finally applied to the miniJPAS data. For the few objects with spectroscopic confirmation of their classes, we obtained results consistent with the mock test sets (QSO completeness ∼0.
2021  ). These uncertainties inform the significance of individual measurements -and this is equally true both for template fitting using a χ 2 as for ML methods. If the data set is very homogeneous, with nearly identical uncertainties for all data points, then of course there is no information in the errors. Ho we ver, for e xtremely div erse data sets such as astronomical catalogues, with both bright and faint objects, and a complex distribution of SNRs as a function of magnitude, this information is critical. We compare the CNNs with two additional ML baseline models: RFs (Breiman 2001  ) and LightGBM (LGBM; Ke et al. 2017 ) , for 2 By noisy labels we mean objects that have been assigned the wrong class, e. g. galaxies labelled as stars. Downloaded from https://academic. oup. com/mnras/article/520/3/3494/7043861 by CSIC -Instituto De Ganaderia De Montana user on 31 August 2023.
Datasets We used an in-house series of 401 liver cancers with matched whole exome (WES, n=275) / genome (WGS, n=126) sequencing and RNA-seq data to benchmark the performance of SpliceAI. Initial bioinformatic analyses (alignment and variant calling) were already performed in previous studies  (19) (20) (21) (22) (23) (24) (25)  and we directly used processed data in this study. We then analyzed a large pan-cancer series comprising 17,714 tumor samples across 25 cancer types from the ICGC-PCAWG (n=2,699 WGS)  (26) , Hartwig Medical Foundation (n=4,818 WGS)  (27)  and TCGA (n=10,197 WES) (28) projects. Somatic mutations were downloaded directly from the online repositories of each project. We used SpliceAI (v1. 3. 1) to predict the splicing impact of almost 300 millions somatic mutations. This computation-intensive task was performed on the Jean Zay supercomputer, the world's 10th most powerful supercomputer, with a computing power reaching 28 petaflops.
RUNJ scores were calculated in two ways, considering as abnormal reads only those supporting the most direct consequence of the splicing alteration (e. g. reads passing trough the essential donor for donor loss events) in the RUNJdirect score, or all reads supporting abnormal junctions in the RUNJdirect score. Finally, a prediction was validated if and only if all of the following hypotheses are verified: -#𝑀 ' ≥ 2 - 3456#7,9:; ≥  or  3456#7,9:; ≥  - '??,9:@ ≥  or  '??,9:@ ≥  or not applicable. To facilitate the validation of large numbers of variants, we created the PyRNA library that allows retrieving relevant reads from RNA-seq bam files and calculating RUNJ scores: https://github. com/FunGeST/pyRNA.
FUNDING This work was performed using HPC resources from  GENCI-IDRIS  (Grant  2021-AD011011412R1 ). This work was funded in part by the  French government  under management of  Agence Nationale de la Recherche  as part of the  "Investissements d'avenir" program , reference  ANR-19-P3IA-0001  ( PRAIRIE 3IA Institute ).
AVAILABILITY LICA-FR data used in this manuscript are hosted in the European Genome-Phenome Archive (https://ega-archive. org). We downloaded public cancer genomic data from the ICGC-PCAWG (https://dcc. icgc. org/pcawg), TCGA (https://gdc. cancer. gov/about-data/publications/pancanatlas) and HMF (https://www. hartwigmedicalfoundation. nl/en/data/database/) websites. The pyRNA library that we developed to mine RNA-seq data in Python is available on GitHub (https://github. com/FunGeST/pyRNA).
Word Sense Disambiguation (WSD) is a significant and challenging task for text understanding and processing. This paper presents an unsupervised approach based on Weighted Co-occurrence bio-Term Graph (WCOTG) for performing WSD in the biomedical domain. The graph is automatically created from biomedical terms that are extracted from a corpus of downloaded scientific abstracts. Two kinds of weights are introduced on the links of the built bio-term graph and are taken as important factors in the process of disambiguation. The modified Personalised PageRank (PPR) algorithm is used for performing WSD. When evaluated on the NLM-WSD and MSH-WSD test datasets, and an acronym test set, the method outperforms the widely used unsupervised ones addressing the same problem, and the average result is almost equal to that of the BlueBERT_LE-based method.
Currently, the Unified Medical Language System (UMLS) Knowledge Sources are widely used in tasks such as disambiguation and automatic question-answering in the biomedical field. However, existing work such as  [5]  and  [6] , etc. , typically involves extracting concepts (CUIs) from UMLS to build systems. In disambiguation tasks, this can lead to a mapping bottleneck when converting polysemy in biomedical documents into concepts. Therefore, we build term graphs directly, not concept graphs, the latter requires the use of term-toconcept mapping tools. This method can be considered an improvement of the existing work by Duque et al. [5] , whose knowledge base is an unweighted concept graph. We not only directly construct the term graph, but also add weights to the edges of the graph to improve the system's disambiguation performance.
The disadvantage of these supervised approaches is that they require lots of labeled training data, which are extremely time-consuming and expensive to create  [13]. Semi-supervised approaches offer solutions to that problem by using techniques to generate sense-tagged examples automatically  [9] ,  [14]. Unsupervised methods do not require labeled training examples and typically use graph-based clustering techniques  [15]. Recently, word embedding models  [16]  and pre-trained language model BERT (Bidirectional Encoder Representation from Transformers)  [17] ,  [18]  and its variant BERT models  [19] ,  [20] ,  [21]  all pre-trained on large corpora were introduced to conduct unsupervised learning for WSD. For example, Mao and Wah  [6]  generate semantic relatedness measurements between UMLS concepts to achieve disambiguation by applying the word embedding models and various flavors of BERT.
The work by Duque et al. [5]  is an unsupervised method. It introduces a graph-based approach for WSD in the biomedical domain, wherein, the UMLS database is explored to convert text from the original document set to biomedical concepts, and a corpus of biomedical documents is used to build a co-occurrence graph that is then analyzed to identify the meanings of ambiguous words. The evaluation results show that it outperforms most of the knowledge-based methods for WSD in the biomedical domain. However, the disambiguation algorithm does not consider the role of the links' weights in the graph. Inspired by this work, we go further to make some improvements to it. This paper proposes a novel approach based on Weighted Co-occurrence bio-Term Graphs (WCOTG) for performing WSD in the biomedical domain.
On the one hand, the weights of links in the created graph are taken into account in the disambiguation algorithms. Comparative evaluation of weighted bio-term graphs and non-weighted ones is also performed. On the other hand, the work by Duque et al. [5]  makes use of the UMLS MetaMap 1  program to convert biomedical texts to concepts and build concept graphs, while in our case, a co-occurrence graph is built based on terms directly instead of concepts. This is mainly based on the following considerations. Mapping programs  [22]  have automatic disambiguation limitations. ''One of the most difficult problems MetaMap deals with is ambiguity'' 4. Using MetaMap, monosemous words will be attached to just one concept, whereas polysemous words may be attached to several concepts  [5]. Although the MetaMap program offers a disambiguation server, it cannot eliminate most of the ambiguity.
And manually assistant mapping is difficult and time-consuming. Furthermore, we use a transformer-based neural network model to calculate the weights between terms in the built graph. In recent years, pre-trained neural language models, such as BERT (Bidirectional Encoder Representation from Transformers)  [17] ,  [18] ,  [23] , XLNET  [24] , Gloss-BERT  [25] , BEM  [26] , and ALBERT  [27] , etc. have achieved state-of-the-art results in various natural language processing (NLP) tasks. In Word Sense Disambiguation (WSD) tasks, Loureiro and Jorge  [28]  used the BERT model to create sense embeddings for all senses in WordNet. Vial et al. [29]  and Kumar et al. [30]  used pre-trained BERT word vectors as input embeddings. In the biomedical domain, transformerbased neural network models are also successfully used to predict the DNA promoters  [31]  and identify DNA N6  [32].