dataesr/scientific-paragraphs-categorization huggingface dataset:
Comparing measured functional impacts with population genotypes While AIP exhibits incomplete penetrance, HMBS intolerance to variation is supported by a gnomAD ''loss-of-function'' intolerance (pLI) score > 0. 95, a score that includes only nonsense and frameshift variants amongst loss-offunction variants. Similarly, missense variants called as damaging in our combined map were depleted in human cohort databases (see material and methods; OR ¼ 3. 34, p ¼ 5 3 10 À11 ; Fisher's exact test; Figure  S12B ), suggesting that reduced-function HMBS variants have been counterselected in the human population.
Acknowledgments We gratefully acknowledge funding for this project from  Alnylam Pharmaceuticals. We further acknowledge the  National Institutes of Health National Human Genome Research Institute (NIH/ NHGRI) Center of Excellence in Genomic Science Initiative  ( HG010461 ), the  NIH/NHGRI Impact of Genomic Variation   on Function (IGVF) Initiative  ( UM1HG011989 ), the  Canada Excellence Research Chairs Program , and a  Canadian Institutes of Health Research Foundation  Grant to F. P. R. Computational resources for the molecular dynamics simulations were provided by  Compute Canada  and SharcNet. We gratefully acknowledge  Dr. Sharon D. Whatley  for providing clinically annotated variants for use in our positive reference set.
Data and code availability Custom scripts for all downstream analyses are publicly available: https://github. com/wvanlogg/HMBS. Functional impact scores for the erythroid, ubiquitous, and combined variant effect maps have been deposited on MaveDB  54  under accession number urn:mavedb:00000108-a. Genotypes and phenotypes for individuals with AIP drawn from Ipnet (https://porphyrianet. org/en/ content/worldwide-network) and literature-curation, respectively, can be found in Table  S7.
Dataset Due to the absence of real hemodynamic data to validate and test the proposed approach, in this paper, we used an in silico hemodynamic public database 1. A pre-validated one-dimensional model has been used for generating the database of simulated pulse wave signals at different arterial locations. This model generates the signals based on different cardiovascular properties such as age, heart rate, blood density, and arterial diameter among other cardiovascular parameters  (Charlton et al. , 2019). These signals are often used to evaluate various pre-clinical assessment studies and hemodynamic analyzer algorithms, such as assessing pulse wave velocity. The database emulates pulse wave signals of one cardiac cycle of length from 4,374 virtual healthy adults with different ages between 25 and 75 in 10-year increments (six age groups) and heart rate between 66 bpm and 86 bpm, using a sampling frequency of 500 Hz for each signal.
Spectrogram generation The spectrogram is a time-frequency representation used to analyze the change of frequency with respect to time from a given input signal. In this paper, spectrograms are generated using the function spectrogram from MATLAB. It is well known that the selection of parameters involved in the creation of the spectrogram will define the quality of the representation, as shown in  (Jablonski and Dziedziech, 2022). For this reason, windows' parameters, spectrogram's shape, and overlapping percentage were finely tuned. Regarding the window type, it has been shown that Hamming and Kaiser's windows are good options for generating spectrograms from pulse wave signals such as PPG  (Zong and Jafari, 2015; Esgalhado et al. , 2021). Overlapping percentage values of 0, 60, and 95 for Hamming windows and 0, 61, and 70 for Kaiser, were selected based on the values reported in  (Trethewey, 2000)  and  (Heinzel et al.
FCQ score ( f) = F (Y, f) 1 S ∑ s∈S ρ ( f, s) (23) where ρ( f, s) is the Pearson correlation, F(Y, f) is the F-statistic and Y is the target variable to estimate, S the set of selected features, S is the number of feature selected, s is a feature such as s ∈ S and f denotes a feature currently not selected ( f ∉ S)  (Zhao et al. , 2019). Finally, the number of features ( S ) to be selected was chosen by a sensitivity analysis taking a different number of features which range between 1 and the total number of features to predict the cf-PWV using the proposed machine learning models with the default hyperparameters defined by the python library scikit-learn. Then, the set of features that produce the best mean R 2 performance of the models is selected to ensure high performance. Table  2  shows the number of features selected for each feature type, details on the feature selection can be found in the Supplementary Material.
Machine learning models To create the training and testing dataset for supervised machine learning models, the dataset was split into two different groups where the 70% of the total dataset was used for the training set, and the 30% left was used for the testing set. Finally, each of the three different features types were fed into the following machine learning methods: Random forest regression (RF), Gradient Boost Regressor (GBR), multilayer perceptron (MLP), Multiple Linear regression (MLR), and Suppor Vector Regression (SVR).
Death rates of host (μ j ) and vector (μ V ). Death rates of vector and hosts were estimated using the typical assumption that their lifetime follows an exponential distribution with a mean equal to the average life duration expectancy. The average species lifespan was found in  [48]  for C. quinquefasciatus, in  [62]  for 44 other hosts species, and in 4 open-access scientific databases visited in June 2021 for the last 16 species (see S1 Appendix). For each of the five groups of hosts, the death rate was then calculated as the average of those species' death rates weighted by their relative abundance. As strong differences were reported in C. quinquefasciatus survival between the rainy and dry seasons  [48] , we considered two separate estimates of the vector death rate for the corresponding period of time, i.
This study is set within the framework of the ' Laboratoires d'Excellences (LABEX) '  TULIP  ( ANR-10-LABX-41 ) and of the ' E ´cole Universitaire de Recherche (EUR) '  TULIP-GS  ( ANR-18-EURE-0019 ). This work has benefited from a  Post-Doc fellowship  to  AFF  (CONACYT, Person Number.
The cleaned DRKG. We first clean the DRKG data  11  in order to reduce redundant information (see "Materials and methods" section). The cleaned DRKG contains about 98,000 entities of 13 different types: gene, taxonomy (Tax), pathway, biological process, molecular function, cellular component, anatomy, disease, symptom, compound, pharmacologic class, ATC code, side effect (Fig. 2 ). For our drug repurposing goal, 8103 compound entities, corresponding to approved or in-trial drugs are present in the cleaned DRKG graph. The COVID-19 disease is represented by 27 associated virus proteins (see Supplementary-I, Table  S4 ). The KG contains 102 relation names and 5,813,617 triples. The Treat relation exists between compounds and diseases except for COVID-19 disease entities. We provide few statistical information on the cleaned DRKG in Supplementary-I, Fig.
The highest number of compounds (40) in top-100 corresponds to the SARS-CoV-2-nsp13 target. Among these 40 compounds 2 are in-trial and 38 are new. We choose to focus our molecular evaluations on these new compounds. Molecular evaluation of compounds for SARS-CoV-2-nsp13. For molecular evaluations, we compare our 38 predicted compounds with 86 compounds known to bind the SARS-CoV-2-nsp13 target (38 from literature and 48 from the PDB database  34  ). We provide molecular evaluations based on either ligand or target structures and these are named hereafter ligand-based and target-based evaluations. Ligand-based evaluation. This evaluation consists of clustering the predicted and known ligands based on their structural similarity. We find a total of thirteen clusters. Among these clusters, ten contain both predicted and known ligands and their content is listed in Table  2.
We also provide molecular weight (MW) of the maximal common sub-structure (MCS) of ligands in each cluster in the table. The first cluster contains 10 ligands among which 6 are known from the literature and 4 are new predicted compounds. Moreover, this cluster displays the highest MCS, thus revealing a good molecular similarity between all these compounds. Based on the MCS values that display an important decrease between cluster 6 and 7, one could retain all the new compounds from clusters 1 to 6 (i. e. 18 compounds) as potential interesting ligands for the nsp13 target. Interestingly, this set of 18 compounds includes Fosinopril that will also appear in the target-based evaluation. Target-based evaluation. This evaluation consists of performing molecular docking of the 38 predicted and 86 known ligands in the active site of the nsp13 structure using the GOLD software.
When the best disease target is not nsp13, the information for the second best target is provided only if it is nsp13. Surprisingly the best docked ligand Diosmine does not display nsp13 as best or second-best target. We further analyzed our docking results by exploring the interaction maps of our four best-ranked compounds and comparing them with Diosmine (known ligand with the best Gold score), Ergotamine and Risperdal (known ligands with the two highest probability scores with nsp13 in DRKG) to check their binding similarities. Figure  3  shows the superposition of the ligands (4 predicted versus 3 known) extracted from the corresponding docking best poses and Table  4  compares the list of nsp13 residues concerned by interactions with all these ligands. These data show that the predicted compounds share with the known ligands several structural elements and two of them (Fosinopril and Dinoprostone) interact with 6/7 amino-acid residues from the nsp13 active site.
Although the current study shows impressive drug repurposing performance for COVID-19, it presents several improvement possibilities. Firstly, the embedding generation approach may generate low-quality embeddings due to data scarcity problem, a common issue for embedding methods. This can affect the subsequent drug repurposing performance by producing false (positive and negative) results as we have seen false negative result for the 'Diosmin' compound. Improving the knowledge graph by including more information about compounds, diseases, and other concepts may reduce false results. Secondly, the COVID-19 disease is still evolving and naturally the 2020 built DRKG may lack important information. For example, WHO recommended Sotrovimab (Drug-bank id: DB16355) for COVID-19  40  which is missing in the current DRKG version.
The molecular evaluation and explanation(s) of the predictions in this study lead to a trustable conclusion. The rules, learned in this study, could be useful to build query patterns on other similar KG datasets. Though this study focuses on the COVID-19 disease, the drug repurposing framework is generic and could be applied to other diseases for which a KG exists. In this paper, we provide molecular evaluation of predicted compounds for only nsp13. Evaluation of compounds for other COVID-19 disease targets such as nsp6 or nsp5-C145A which appear as preferred targets in our top-20 predicted compounds, is another possible perspective of this study.
The trained DNN-based prediction model is used to compute probability of an unobserved (Compound, Treat, Disease) triple or simply (Compound, Disease) pair as the model is trained for the Treat relation only. As explained earlier, we are interested to find top-ranked compounds which are expected to treat COVID-19 disease. There are 27 disease entities in the DRKG that represent COVID-19 disease (see Supplementary-I, Table  S4 ) and none of them is linked to any Compound in DRKG. The authors of the DRKG dataset  11  have integrated in DRKG a set of 8103 FDA-approved or investigational drugs referenced in Drugbank, which have a MW greater than or equal to 250 Da, as candidates for drug repurposing. We use this set of candidate compounds in our Treat link prediction experiment. As a result, we have 8103 × 27 = 218, 781 (Compound, Disease) pairs to test.
For collecting known ligands for this disease target, we search both literature and PDB database. We collect 38 known ligands for nsp13 through literature screening  [35] [36] [37] [38] 53, 54. We al o extract the ligands for nsp13 found in the PDB entries for nsp13 found in the relevant PDB entries. We obtain 71 entries for nsp13 structures complexed with a ligand in the PDB database and we reduce this number to 48 thanks to a redundancy threshold of Root-Mean-Square Deviation (RMSD) set to 2Å. The list of PDB identifiers (IDs) and corresponding ligands is provided in Supplementary-I, Table  S6. This table also contains the PDB ID for the structure of the nsp13 protein without any ligand (PDB:7NIO), also known as apo-nsp13. The 48 PDB ligands are designated hereafter with their capitalized PDB abbreviation.
In total, our dataset of known nsp13-ligands contains 86 ligands (48 from PDB and 38 from literature). The 2D structures of all ligands are collected from the PubChem database  55  in SDF format. We use our 86 known and 38 predicted ligands for two types of molecular evaluation: ligand-based and target-based. Ligand-based evaluation. The ligand-based evaluation is based on the concept of chemical structure similarity that says that is similar ligands or compounds would bind to similar disease targets with almost the same binding affinity and express similar biological responses 5. This type of evaluation is quick and takes into account the polypharmacological properties of ligands  5. In ligand-based evaluation, we perform cluster analysis of known and predicted ligands to see how the ligands are grouped based on their similarity.
As one small ligand could be just part of a large ligand, consideration of molecular weight similarity among ligands in a cluster is also an important aspect. It is expected that better predictions will have higher structure sharing and lower difference in MW when compared to known ligands. Target-based evaluation. In target-based evaluation, the binding of ligands to target proteins is assessed from a 3D point of view using a computational approach. Molecular docking is a common computational approach to optimize the process of finding the most favorable 3D binding conformations of the ligand to the target protein  5. Molecular docking is performed using the GOLD software from Cambridge Crystallographic Data Centre. GOLD stands for Genetic Optimisation for Ligand Docking  56. The target structure is the apo-nsp13 structure (PDB: 7NIO) lacking any ligand.
Based on the literature  35, 37, 42  , we select the ATP binding site of nsp13 as the binding pocket for molecular docking. The ATP binding site composition in terms of amino-acids (or residues) is as follows: GLU261, ASN265, GLY287, LYS288, SER289, HIS290, LYS320, LYS323, TYR324, ASP374, GLU375, GLN404, ARG442, ARG567. A single structure (the apo-nsp13 one) is used for a target because binding site variations across available PDB entries are weak (checked by RSMD calculation, see Supplementary-I, Table  S6 ). For the ligands, we use the set of known and predicted (from the Top-100 predicted pairs) nsp13 ligands. We extract the 2D structures of these ligands in SDF format from PubChem  55  and use the Corina tool  57  (purchased from Molecular Network, GmbH, Nürnberg, Germany; https:// mn-am.
smMIPs-based ABCA4 sequence analysis and data processing Sequencing of the samples was performed using an in-house smMIPs probe pool, as described previously. 19  Briefly, 3,866 smMIPs were designed to capture and sequence the entire ABCA4 gene, in addition to 23. 9 kb upstream and 14. 9 kb downstream regions. Each probe has a capture region of 110 nt and the overall smMIPs pool was designed to cover every nucleotide position in ABCA4 with two different smMIPs, thereby increasing sensitivity and minimizing dropout because of variants in the smMIPs arms. For SNPs known to be in the sequence of the annealing arms, different smMIPs were designed to avoid allelic dropout. To sequence the 858 cases, five sequencing runs (runs 1-5) of $215 samples each, including 6 control samples, were performed on an Illumina NextSeq 500 platform using high-output kits ($400 million reads).
Run 3 was performed twice because of the low coverage obtained in the first sequencing run. A threshold of >100,000,000 reads per run and >1503 average smMIPs coverage was applied for all runs. Variant mapping and annotation were performed using an in-house bioinformatics pipeline, as previously described,  22  on raw sequencing data files (FASTQ). Sequencing reads were aligned to the reference genome using the human genome build GRCh37/hg19. Variant annotation included gene component information and the predicted effect of the variants, as well as frequency information derived from several population databases.
Data analysis and variant selection Data analysis was performed in two steps. Firstly, all known ABCA4 variants were identified, i. e. , variants that were previously reported in literature and splice-affecting variants that had been tested using in vitro splice assays or patient-derived photoreceptor progenitor cells. 16, 17, 19, [23] [24] [25] [26] [27] [28] [29]  During this step data in the ABCA4 Leiden Open Variation Database (LOVD: www. lovd. nl/ ABCA4)  15, 18  was used for the identification of variants known in literature. Novel causative variants were selected by applying filters on the annotated data. Variants were filtered based on their position (e. g. , exon, canonical splice site sequences, NCSS). Variants were considered to be NCSS variants when found in nucleotide positions À14 to À3 upstream of exons, þ3 to þ6 nucleotides positions downstream of exons, or positioned within the first and last two nucleotides of an exon.
Allele frequency (AF) of <0. 005 filtering was performed both on the internal sequencing run frequency and population databases, such as the Genome Aggregation Database (gnomAD v2. 1. 1; http://gnomad. broadinstitute. org/, average AF) and Genome of the Netherlands (GoNL; https:// www. nlgenome. nl/). Variant effect was assessed using in silico pathogenicity tools, such as Sorting Intolerant From Tolerant (SIFT; https://sift. bii. a-star. edu. sg),  30  Polymorphism Phenotyping v2 (PolyPhen-2; http://genetics. bwh. harvard. edu/pph2/),  31  and Mutation Taster (https://www. mutationtaster. org/). 32  Additionally, variants were checked through the Franklin Genoox platform (https://franklin. genoox. com/: Accessed July 2022) for suggested American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG-AMP) classification.
The second phase of analysis focused on identifying novel putative splice altering variants. In addition to the filter on AF (<0. 005), the effect of these variants on splicing was assessed considering SpliceAI predictions. 33  SpliceAI was set to return the highest delta score in a window of 5,000 nt upstream and downstream of the variant position. Candidate variants were selected with a delta score R0. 03 in either acceptor gain (AG), donor gain (DG), acceptor loss, or donor loss. To ensure data were of high quality, the region read coverage and the presence of the variant were checked in each sample BAM file using Alamut Visual 2. 13 software. Additionally, the programs available on Alamut Visual 2. 13 software, that is, SpliceSiteFinder-like,  34  MaxEntScan,  35  NNSPLICE  36  and GeneSplicer  37  were used as aid to visualize the position and predicted effect of the variants.
For amplification, ABCA4 exonic primers were used whenever possible. Alternatively, primers for the flanking exon 3 and 5 of Rhodopsin (RHO) present in the midigene system were utilized. Actin beta (ACTB) was selected as housekeeping gene control and exon 5 of RHO as a transfection control (Table  S3C ). Electrophoresis gel analysis was performed, followed by Sanger sequencing of the bands excised from gel (Figures  S2-S13 ). Ratios between different RNA products were quantified by densitometry analysis using Fiji software after gel electrophoresis,  38  in RNA and protein notations only products that accounted for more than 15% of total product were reported. The cDNA, RNA and protein notation of all variants were obtained using the MANE transcript NM_000350. 3.
Copy number variant analysis Detection of copy number variants (CNVs) was performed using an Excel script developed in-house based on the smMIP read depth, as previously described. 19  Since this method allows for the identification of large deletions and duplications but not of inversions and insertions, the term CNV is used hereafter instead of SV. CNV analysis was performed independently for each of the sequencing runs (Tables  S4-S8 ), and CNVs were confirmed by PCR amplification using AmpliTaq Gold DNA polymerase (Thermofisher Scientific, Waltham, MA) followed by Sanger sequencing to identify the breakpoints of the rearrangement (Table  S3D  and Figure  S14 ). Six samples containing known CNVs were included in all runs as positive controls.
Interestingly, this was also the shortest PE inclusion identified in our cohort. Two DIVs (c. 3522þ524A>G and c. 6283-321C>G) showed the expected splice defect, but aberrant transcripts were present in less than 20% of the cDNA products, leading to the tentative classification of these variants as benign (Table  S14 ). Variant c. 6283-321C>G showed a complex splicing defect with two PE inclusions and partial or complete intron inclusion (Figure  2 ). PE1 (258 nt) was the most abundant aberrant product, followed by the exon elongation, complete intron retention and PE2 (113 nt). PE1 corresponded with the highest predicted effect by SpliceAI, while PE2 was predicted as the fourth highest (Table  S13 ). Several heteroduplex artifacts were observed after RT-PCR, which made result interpretation challenging.
Acknowledgments We thank  Saskia van der Velde-Visser  for technical assistance. This work was supported by the  Retina UK , grant no. GR591  (to F. P. M. C. ), a  Horizon 2020 ,  Marie Sklodowska-Curie Innovative Training Network entitled European Training Network  to Diagnose, Understand and Treat Stargardt Disease, a  Frequent Inherited Blinding Disorder -StarT ( 813490 ) (to F. P. M. C. ), the  Foundation Fighting Blindness USA , grant no. PPA-0517-0717-RAD  (to F. P. M. C. ), the  Rotterdamse Stichting Blindenbelangen , the  Stichting Blindenhulp , and the  Stichting tot Verbetering van het Lot der Blinden  (to F. P. M. C. ), and by the  Landelijke Stichting voor Blinden en Slechtzienden , the  Macula Degeneratie fonds  and the  Stichting Blinden-Penning  that contributed through  Uitzicht 2016-12  (to F.
P. M. C. ). This work was also supported by the  Pro Retina Foundation Germany , the  Stichting Blindenhulp , the  Stichting ter Verbetering van het Lot der Blinden , the  Gelderse Blinden Stichting , the  Stichting voor Ooglijders , and the  Oogfonds  that contributed through Uitzicht 2020-17 (to F. P. M. C. ). This work was also supported by  EJPRD19-234  (Solve-RET) (to F. P. M. C. and P. L. ) and by the  GA UK  n. 321522  (to P. L. and M. V. ). This work was also supported by the  Israel Science Foundation  (grant number  1778/20  to D. S. and E. B. ) within the  Israel Precision Medicine Partnership program. This research was funded by  Fighting Blindness Ireland  ( FB18CRE , FB20DOC). This work was also supported, in part, by the  NIH/NEI  grants  R01EY028203 ,  R01EY028954 ,  R01EY029315 ,  P30EY019007 ,  Foundation Fighting Blindness  award  PPA-1218-0751-COLU , and the Unrestricted funds from the  Research to Prevent Blindness (RPB)  to the  Department of Ophthalmology, Columbia University,   New York, NY, USA.