dataesr/scientific-paragraphs-categorization huggingface dataset:
C. V. H. ) and the UK Dementia Research Institute, which receives its funding from the UK Medical Research Council, Alzheimer's Society and Alzheimer's Research UK (J. M. W. ), is gratefully acknowledged. Genotyping was supported by a grant from the BBSRC (no. BB/F019394/1). The Nagahama Study (T. K. , S. M. , M. O. , K. S. , Y. T. , K. Y. , A. Tsuchida, P. B. , B. M. , M. J. , M. -G. D. and F. M. ): We are grateful to the Nagahama City Office and nonprofit organization Zeroji Club for their help in conducting the study. This project is supported by operational funds of Kyoto University and the Top Global University Project of the Ministry of Education, Culture, Sports, Science and Technology (MEXT) in Japan. We also received a Grant-in-Aid for Scientific Research from the Japan Society for the Promotion of Science, research grants from the Japan Agency for Medical Research and Development for the Practical Research Project for Rare/Intractable Diseases, and the Comprehensive Research on Aging and Health Science for Dementia R&D.
We thank C. Galmiche for rating PVS in the validation dataset for the artificial intelligence-based method. The Northern Manhattan Study (NOMAS) (N. D. D. , T. J. and R. L. S. ): We gratefully acknowledge and thank the NOMAS participants. Funding was awarded through grants from the National Institute of Neurological Disorders and Stroke (R01 grant no. NS 29993) and the Evelyn F. McKnight Brain Institute. Rotterdam Study (M. J. K. , F. D. , M. W. V. , M. A. I. and H. H. H. A. ): The Rotterdam Study is funded by Erasmus Medical Center and Erasmus University, Rotterdam, the Netherlands Organization for Health Research and Development (ZonMw), the Research Institute for Diseases in the Elderly (RIDE), the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the European Commission (DG XII), and the Municipality of Rotterdam.
The authors are grateful to the study participants, the staff from the Rotterdam Study and the participating general practitioners and pharmacists. The generation and management of GWAS genotype data for the Rotterdam Study (RS I, RS II, RS III) were executed by the Human Genotyping Facility of the Genetic Laboratory of the Department of Internal Medicine, Erasmus MC, Rotterdam, the Netherlands. The GWAS datasets are supported by the Netherlands Organisation for Scientific Research (NWO) Investments (no. 175. 010. 2005. 011, 911-03-012), the Genetic Laboratory of the Department of Internal Medicine, Erasmus MC, the Research Institute for Diseases in the Elderly (grant no. 014-93-015; RIDE2), the Netherlands Genomics Initiative/NWO, the Netherlands Consortium for Healthy Aging, project no. 050-060-810.
We thank P. Arp, M. Jhamai, M. Verkerk, L. Herrera, M. Peters and C. Medina-Gomez for their help in creating the GWAS database; and K. Estrada, Y. Aulchenko and C. Medina-Gomez for the creation and analysis of imputed data. H. H. H. A. is supported by ZonMW grant no. 916. 19. 151. Study of Health in Pomerania (SHIP) (S. F. , R. B. , A. T. , K. W. , H. J. G. and U. V. ): SHIP is part of the Community Medicine Research net (CMR) (http://www. medizin. uni-greifswald. de/icm) of the University Medicine Greifswald, which is funded by the Federal Ministry of Education and Research (grant nos. 01ZZ9603, 01ZZ0103 and 01ZZ0403), the Ministry of Cultural Affairs as well as the Social Ministry of the Federal State of Mecklenburg-West Pomerania, and the network 'Greifswald Approach to Individualized Medicine (GANI_MED)' funded by the Federal Ministry of Education and Research (grant no.
03IS2061A). Genome-wide data have been supported by the Federal Ministry of Education and Research (grant no. 03ZIK012) and a joint grant from Siemens Healthineers, Erlangen, Germany and the Federal State of Mecklenburg-West Pomerania. The University of Greifswald is a member of the Caché Campus program of the InterSystems GmbH. This study was further supported by the EU-JPND Funding for BRIDGET (grant no. FKZ:01ED1615). H. J. G. has received travel grants and speakers' honoraria from Fresenius Medical Care, Servier, Neuraxpharm and Janssen Cilag, as well as research funding from Fresenius Medical Care. Sydney Memory and Ageing Study (MAS) & Older Australian Twins Study (OATS) (R. M. T. , N. J. A. , H. B. , J. J. , M. P. , A. T. , J. N. T. , P. S. S. , W. W. , K. A. M. and M. J. W. ): Sydney MAS: The Sydney MAS has been funded by three National Health & Medical Research Council (NHMRC) Program Grants (grant nos.
ID350833, ID568969 and APP1093083). Collection of WGS data was supported by the NHMRC National Institute for Dementia Research Grants no. APP1115575 and no. APP1115462. MRI scans were processed with the support of NHMRC Project Grants (grant nos. 510175 and 1025243) and an Australian Research Council (ARC) Discovery Project Grant (no. DP0774213) and the John Holden Family Foundation. We also thank the MRI Facility at NeuRA. We thank the participants and their informants for their time and generosity in contributing to this research. We also acknowledge the MAS research team: https://cheba. unsw. edu. au/ research-projects/sydney-memory-and-ageing-study. OATS: The OATS study has been funded by an NHMRC and ARC Strategic Award Grant of the Ageing Well, Ageing Productively Program (grant no. 401162); NHMRC Project (seed) Grants (grant nos.
1024224 and 1025243); NHMRC Project Grants (grant nos. 1045325 and 1085606); and NHMRC Program Grants (grant nos. 568969 and 1093083). Collection of WGS data was supported by the NHMRC National Institute for Dementia Research Grants no. APP1115575 and no. APP1115462. This research was facilitated through access to Twins Research Australia, Agency (ANR) as part of the 'Investment for the Future Program' no. ANR-18-RHUS-0002. It is also supported by a JPND project through the following funding organizations under the aegis of JPND: www. jpnd. eu: Australia, National Health and Medical Research Council; Austria, Federal Ministry of Science, Research and Economy; Canada, Canadian Institutes of Health Research; France, French National Research Agency; Germany, Federal Ministry of Education and Research; the Netherlands, the Netherlands Organisation for Health Research and Development; United Kingdom, Medical Research Council.
This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement nos. 643417, 640643, 667375 and 754517. The project also received funding from the French National Research Agency (ANR) through the VASCOGENE and SHIVA projects, and from the Initiative of Excellence of the University of Bordeaux (C-SMART project). Computations were performed on the Bordeaux Bioinformatics Center (CBiB) computer resources, University of Bordeaux. Funding support for additional computer resources at the CREDIM (Centre de Recherche et Développement en Informatique Médicale, University of Bordeaux ) has been provided to S. D. by the  Fondation Claude Pompidou. The  Three-City  (3C) Study: The  3C Study  is conducted under a partnership agreement among the  Institut National de la Santé et de la Recherche Médicale (INSERM) , the  University of Bordeaux  and  Sanofi-Aventis.
This work was supported by the  National Foundation for Alzheimer's disease  and related disorders, the  Institut Pasteur de Lille , the  labex DISTALZ  and the  Centre National de Génotypage. We thank  A. Boland  ( CNG ) for her technical help in preparing the DNA samples for analyses. UK Biobank (UKB) (M. J. K. , F. D. , M. W. V. , M. A. I. , H. H. H. A. , A. M. and T. E. ): This research has been conducted using the UK Resource under application no. 23509. McGill Genome Center  (M. B. , P. M. , G. B. and  M. Lathrop ): Work done at the  Canadian Center for Computational Genomics  was supported by  Genome Canada. Data analyses were enabled by computing and storage resources provided by  Compute Canada  and  Calcul Québec. G. B. is supported by the  Fonds de Recherche Santé Québec  and the  Canada Research Chair program.
Code availability The PVS quantification method used in the Nagahama Study is available using this link: https://github. com/pboutinaud/SHIVA_PVS. We used publicly available data from METAL (https://github. com/stat gen/METAL), GCTA-cojo (https://yanglab. westlake. edu. cn/software/ gcta/#COJO), FUMA (https://fuma. ctglab. nl/), MAGMA (https://ctg. cncr. nl/software/magma), LD-Score Regression (https://github. com/ bulik/ldsc/), MTAG (https://github. com/JonJala/mtag), coloc (https:// chr1swallace. github. io/coloc/) R package, RadialMR (https://github. com/WSpiller/RadialMR), TwoSampleMR (https://mrcieu. github. io/ TwoSampleMR/), STEAP (https://github. com/erwinerdem/STEAP/), CELLECT (https://github. com/perslab/CELLECT), S-LSC (https:// github. com/bulik/ldsc), H-MAGMA (https://github. com/thewonlab/H-MAGMA), HBT (https://hbatlas.
for brain expression pattern across the lifespan of other genes). The spatio-temporal gene expression level is plotted as log2-transformed exon array signal intensity (y-axis) against the post conception days (x-axis) as provided by the Human Brain Transcriptome project database. Periods of human development and adulthood are indicated by vertical dashed lines: 4-8 post conception weeks [PCW] (period 1), 8-10 PCW (period 2), 10-13 PCW (period 3), 13-16 PCW (period 4), 16-19 PCW (period 5), 19-24 PCW (period 6), 24-38 PCW (period 7), birth-6 postnatal months (period 8), 6-12 postnatal months (period 9), 1-6 years (period 10), 6-12 years (period 11), 12-20 years (period 12), 20-40 years (period 13), 40-60 years (period 14), and 60 years + (period 15). The boundary between pre-and postnatal periods is indicated by the solid vertical line.
We found a significant enrichment of BG-PVS genes in targets for antiinfectives for systemic use (ATC J01, OR = 252. 4, P = 8. 85×10 -3 ), driven by CRHR1 (chr17q21. 31), a target for telavancin (A, B); of note, CRHR1 antagonists were shown to attenuate blood brain barrier permeability, cortical vascular hyperpermeability and tight junction disruption. ATC indicates Anatomical Therapeutic Chemical classification. https://doi. org/10. 1038/s41591-023-02268-w Extended Data Fig. 8 | Enrichment of PVS genes in targets of drugs validated in other indications (Trans-Phar). We used the Integration of Transcriptomewide Association Study and Pharmacological Database (Trans-Phar) software,.
Preprocessing 10X reads I preprocessed 10X reads using the following pipeline for use in several algorithms (section 2. 4). I first removed duplicate reads using Nubeam-dedup  (Dai and Guan, 2020; commit 25dd385). I then used proc10xG (https://github. com/ucdavis-bioinformatics/proc10xG; commit 7afbfcf) to split the reads from their 10X barcodes for further processing. I used a custom filtering step designed to remove under-and over-represented barcodes and their associated reads from the data (figs. S1 to S3, filter barcodes. R script, personal communication of P-A Gagnaire). Reads were additionally filtered with fastp (v0. 20. 1; Chen et al. , 2018)  with the main objective to remove poly-G tails created by the Illumina Novaseq sequencing technology. I obtained at this point reads equivalent to a short-read sequencing run, usable in some parts of the assembly and quality control pipeline.
17; H. Li, 2017)  was used in the contig self-mapping step. I obtained the purged assemblies using the get seqs steps without restricting the purging to the end of contigs (without the -e option). I used AGOUTI (https://github. com/svm-zhang/AGOUTI; commit a7e65d6;  Zhang et al. , 2016)  to improve scaffolding by using paired end RNA-seq reads. For each species, a different set of published transcriptomes were used (see Supplementary File 1 for accession numbers). AGOUTI require a gene prediction as input in addition to RNA-seq reads. I used  Augustus (v3. 3. 3; Stanke et al. , 2008)  to produce an intermediate annotation for each assembly using the Caenorhabditis model. RNA-seq reads were first cleaned using rcorrector (v1. 0. 4; Song and Florea, 2015)  and trimgalore (v0. 6. 6; https: //www.
bioinformatics. babraham. ac. uk/projects/trim galore; --quality 20 --stringency 1 -e 0. 1 --length 70). RNA-seq reads were mapped independently using  bwa-mem2 (v2. 2. 1; Vasimuddin Md et al. , 2019) , and then merged as a common bam file for each assembly with samtools (v1. 12; H. Li et al. , 2009). Finally, the AGOUTI scaffolding pipeline was run using the gene prediction and mapped RNA-seq reads (-minMQ 20 -maxFracMM 0. 05; using python v2. 7. 15 and samtools v1. 10 for compatibility). As a last step, I ran  Blobtoolkit (v2. 4. 0; Challis et al. , 2020)  on the three assemblies to evaluate quality and potential contamination levels. I used a custom script (btk conta extraction. py) to filter the assembly contigs based on the taxids found by the Blobtoolkit pipeline to remove the most obvious contaminations.
Scaffolding on the Mytilus coruscus genome At the time of assembly, the closest relative of the Mytilus species of interest with a chromosome scale assembly was Mytilus coruscus (GCA 017311375. 1; Yang et al. , 2021). Given the conserved number of 14 chromosomes in the Mytilus genus, I decided to scaffold our contigs on this high quality reference. Additionally, I had Oxford Nanopore reads for the MeduEUN individual. I used minimap2 (v2. 17; H. Li, 2017)  and LRScaf (v1. 1. 10; https://github. com/shingocat/lrscaf) to first improve the MeduEUN assembly with this small amount of long reads. Then for each assembly, I ran the scaffolder RagTag (v1. 1. 1; Alonge et al. , 2021)  to position contigs on the M. coruscus chromosomal assembly. A final polishing step was performed using Pilon (v1. 24; Walker et al.
Repeats I masked repeats for the purpose of annotation using  RepeatModeler (v2. 0. 1; Flynn et al. , 2020)  and RepeatMasker (v4. 1. 2-p1; Smit et al. , 2013 Smit et al. , -2015) )  through the TETools DFAM container (v1. 3. 1; https://hub. docker. com/r/dfam/tetools). I first built repeat databases for each of five assemblies MgalMED, MeduEUS, MeduEUN, M. coruscus (GCA 017311375. 1; Yang et al. , 2021)  and M. galloprovincialis from the Atlantic  (GCA 900618805. 1; Gerdol et al. , 2020). Then, I built a common Mytilus database of repeats by merging those five databases using cd-hit (v4. 8. 1;  Fu et al. , 2012). I used the same options as used by default in RepeatModeler: -aS 0. 8 -c 0. 8 -g 1 -G 0 -A 80 -M 10000. Finally, soft repeat masking was performed on the assemblies with RepeatMasker using the merged database.
Annotation I used  Braker2 (v2. 1. 6; Brna et al. , 2021)  as to obtain structural annotations, using both a protein database and RNA-seq reads (preprocessed in section 2. 4). To build the protein database, I used all Mollusca proteins (taxid 6447) from OrthoDB (v10. 1; Kriventseva et al. , 2019). To provide gene presence hints, I mapped all RNA-seq reads for each species using HISAT2 (v2. 2. 1;  Kim et al. , 2019). I used the Mantis pipeline (v1. 5. 5; Queirós et al. , 2021)  to obtain consensus functional annotations of genes based on multiple databases. Protein sequences for each assembly were built from the structural annotations using the python module gff3tool (v2). Mantis was run with default parameters and databases: kofam  (Aramaki et al. , 2020) , NPFM  (Lu et al. , 2020 ), eggNOG (Huerta-Cepas et al.
Quality assessments and comparisons Preprocessed 10X reads (section 2. 3) were used to first estimate estimate genome size and heterozygosities of the three individuals. I used the reference free k-mer based method GenomeScope (https://github. com/tbenavi1/genomescope2. 0; commit 5034ed4;  Ranallo-Benavidez et al. , 2020)  and the fork of the KMC k-mer counting program (https://github. com/tbenavi1/ KMC; commit 1df71f6). Assembly statistics were computed with the python module assembly stats (v0. 1. 4; Trizna, 2020). To assess the remaining level of duplication in the assemblies, I used the program KAT (v2. 4. 2; Mapleson et al. , 2017)  to compare k-mer spectra from reads (preprocessed 10X) and from the assembly. Finally, gene completion analyses were carried out using BUSCO (v5. 1. 1; Manni et al.
Phylogenetic species tree I compiled protein sequences from published Mytilus genomes and transcriptomes, in addition to the current three genomes and annotations, to build a species tree. I used transcriptomes produced in  Popovic and Riginos (2020)   Gerdol et al. , 2020) , and American M. edulis (GCA 019925275. 1; annotation as personal communication of Tiago Hori, PEIMSO). For GCA 019925275. 1, protein sequences where retrieve from the fasta and gff files using the python module gff3tool (v2. 1. 0). I used OrthoFinder (v2. 5. 4; Emms and Kelly, 2015, 2019 ) to find orthogroups and orthologue genes. Species tree was inferred using the MSA method of OrthoFinder (Emms 6 & Kelly, 2018) using the MAFFT aligner (v7. 505; Katoh and Standley, 2013) , the STRIDE species tree rooting algorithm  (Emms & Kelly, 2017)  and the FastTree software for tree inference (v2.
Histogram of read pairs identified for each 10X barcode for MgalMED. As part of preprocessing step, barcodes for which too few or too many read pairs are associated with each unique barcode are removed from the dataset (red regions). Histogram of read pairs identified for each 10X barcode for MeduEUS. As part of preprocessing step, barcodes for which too few or too many read pairs are associated with each unique barcode are removed from the dataset (red regions).
Acknowledgements I thank  Nicolas Bierne  and  Pierre-Alexandre Gagnaire  for their help on the project and discussions;  Christine Arbiol  for DNA extractions;  Maurine Hammel  and  Erika Burioli  for sharing their transcriptome data; and  Petr Strelkov  for sampling of the M. trossulus. Some bioinformatic analyses were performed on the Core Cluster of the  Institut Français de Bioinformatique  ( ANR-11-INBS-0013 ). I thank  Tiago Horri  for providing his annotation of the American Mytilus edulis genome ( GCA 019925275. 1 ). AS was supported by the  French ANR  grant  TRANSCAN  ( ANR-18-CE35-0009 ) and by the  National Institute of General.
Data availability Raw data are available under BioProject PRJNA785550. Assemblies are available under accessions JAKGDF000000000 (MgalMED), JAKGDG000000000 (MeduEUS), and JAKGDH000000000 (MeduEUN). The assembly pipeline is available at https://github. com/alxsimon/assembly 10x. Annotations, and the OrthoFinder pipeline and results are available in the Zenodo archive 10. 5281/zenodo. 7034399. Data and code for this manuscript are available at https://github. com/alxsimon/Mytilus ref genomes paper.
Histogram of read pairs identified for each 10X barcode for MgalMED. As part of preprocessing step, barcodes for which too few or too many read pairs are associated with each unique barcode are removed from the dataset (red regions). Histogram of read pairs identified for each 10X barcode for MeduEUS. As part of preprocessing step, barcodes for which too few or too many read pairs are associated with each unique barcode are removed from the dataset (red regions). 10 6 10 5 Mean 326. 9 Maximum 231 Mean 318. 9 Retained 405739512 0. 986 Read pairs in bacode 10 2 212 Retained 410695244 0. 985 10 2 Read pairs in bacode Figure S2: Maximum Removed low 5456266 0. 013 10 0 10 1 10 2 10 3 10 4 10 5 10 6 10 0 10 1 Count Removed low 5792687 0. 014 10 0 10 1 10 2 10 3 10 4 10 1 Count 217 Mean 334. 4 Removed low Retained 5524958 428003612 0.
Related work Many algorithms, based on different principles and formalisms for grouping instances of a dataset into clusters according to their similarity, have been proposed in the literature. The most recent approaches developed in this domain presented in this section intent to optimizing this process. Semi-supervised clustering methods aim at integrating available initial prior knowledge about the relevance of grouping or separating certain pairs of instances. Consensus clustering methods aim to combine the results of different clustering algorithms, based on different approaches, to generate a final clustering based on the most frequent aggregations between the initial clusterings. The semi-supervised consensus clustering approach combines these two methods in order to optimize the clustering result in terms of robustness and relevance of the result, depending on the initial information available in the application domain.
At the first stage, different clustering solutions Φ are generated from the same dataset. Then, in the second stage, a consensus function is applied on Φ to find the final consensus clustering  [22]. Fig. 1  shows an overview of the consensus clustering process. There are various ensemble generation strategies and consensus function approaches. A clustering ensemble can be achieved by using different clustering algorithms, by random sub-spacing/sampling initial data  [23] , by using different number of clusters k parameter values, by repeated executions of a single clustering algorithm with several parameter sets  [24] , or by using any combination of the aforementioned methods. Different consensus functions have been developed to combine the base clustering results, such as mixture models  [25] , voting-based approach  [26] , mutual information  [27] , co-association based approach  [28] , graph and hyper-graph based approaches  [29]  and genetic algorithms  [30]  among others.
MultiCons approach The MultiCons approach for multiple consensus clustering is presented in  [50]. This approach is based on the frequent closed itemset framework  [51]  to efficiently discover closed patterns among the different base clustering solutions. Each closed pattern defines an agreement between the base clusterings in grouping a set of dataset instances. By dividing/merging these patterns into groups, MultiCons generates multiple consensuses in a tree-like structure that helps understanding the clustering process and the data space subjacent natural structures. In the following subsections, the MultiCons approach is described step by step and illustrated using a dataset X = {x 1 , x 2 ,. , x 9 } of nine instances as a support example.
Ensemble members The clustering ensemble, consisting of several base clusterings, defines the search space in input of the approach. Different clustering algorithms with different parameter settings, defining each an algorithmic configuration, are used in this step to ensure diversity in the initial clustering solutions that will be combined by the consensus function. This diversity allows to discover intrinsic clusters in the data space, defined by the analyzed dataset, corresponding to different subspace structures. These data subspace structures can be discovered by different algorithmic approaches, such as centroid based, density based or hierarchical based clustering approaches for instance, with different parameter settings. As a support example of the MultiCons process, we consider the five base clusterings in the clustering ensemble shown in Table  2  generated by applying five unsupervised clustering algorithms to dataset X.
A set of columns C is closed if h(C ) = C and a set of rows R is closed if h ′ (R) = R. The h(C ) and h ′ (R) operators generate extensions and intensions, respectively, of closed patterns when applied to the binary matrix. A closed pattern thus represents a maximal, according to inclusion relation, set of columns related to a maximal set of rows in the matrix, that is all the columns in the closed pattern intension and all the rows in its extension are related in the binary matrix. For efficiently extracting all closed patterns from large data matrices, since the number of columns is usually much lower than the number of rows in the dataset, closed pattern extensions are identified in the matrix using the h(C ) closure function. The corresponding intensions are implicitly generated during the calculation of the h(C ) closure from the matrix  [52].