dataesr/scientific-paragraphs-categorization huggingface dataset:
2; www. ldak. org), the mtCOJO tool from the GCTA pipeline (version 1. 94. 1; https://yanglab. westlake. edu. cn/software/gcta/), TwoSa-mpleMR (version 0. 5. 6; https://mrcieu. github. io/TwoSampleMR/), RIMBANET (version from 26 June 2019; https://labs. icahn. mssm. edu/zhulab/?s=rimbanet) and the Enrichr webserver (version from 29 March 2021; https://maayanlab. cloud/Enrichr/). The code for the druggability analysis is available from GitLab (https://cfinan. gitlab. io/biomisc/scripts/drug_lookups. html). Scripts used for the analyses were deposited in our repository (https://github. com/takiy-berrandou/ GWAS-meta-analysis-of-SCAD-paper-analysis-scripts-). Specific options are indicated in the Methods where relevant.
Code availability and software package The "connectivity path" code is available at: https://github. com/p Thore/skeletonizationData2021. It consists in three C++ files: propagation. cpp, skeleton. cpp and testSkeleton. cpp. The first file, propagation. cpp, performs the propagation (algorithm 1) and calculates the density property (algorithm 2). The second file, skeleton. cpp, performs the connectivity path extraction (algorithm 3). The third file testSkeleton. cpp reads the data, calls the computation of the CPs (algorithms 1 to 3), and writes the CPs.
org/10. 5281/zenodo. 5497876). This phenotypic-based classification outlines 'putative' mimicry rings (e. g. Sanders et al. , 2006; Symula et al. , 2001)  in the sense that it delineates groups of biological entities with patterns assumed to be perceived as similar by predators (e. g. Hoyal  Cuthill et al. , 2019; Symula et al. , 2001; Wilson et al. , 2015). Most Ithomiini species contain several subspecies, which often belong to distinct mimicry rings (Figure  S12 ). In order to study the interplay between Müllerian mimicry and the distribution and climatic niche of ithomiine butterflies, we defined Operational Mimicry Units (OMUs;  Doré et al. , 2022)  as the set of conspecific individuals that share the same mimicry pattern (Figure  S2 ). As such, a mimicry ring typically comprises multiple OMUs representing different species.
Estimating community composition In order to investigate the effect of mimicry on the composition of Ithomiini communities, we retrieved maps of the estimated distribution of all 783 OMUs obtained from species distribution models based on a data set of ca. 29,000 georeferenced occurrences  (Doré et al. , 2022;  https://doi. org/10. 5281/zenodo. 4696055; https://doi. org/10. 5281/zenodo. 4673446). These models predicted the distribution of ithomiine butterflies based on the relationship between occurrences and associated climatic variables, forest cover and elevation. They provided scores interpreted as the likelihood of presence of each OMU within each community represented as a quarter-degree grid cell of ca. 30 km × 30 km. This approach allowed us to build a complete list of likelihood of presences of species and associated putative mimicry rings for each of the 21,415 communities (i.
Describing the climatic niche of species For each OMU, we extracted the set of climatic conditions found at its associated georeferenced records | 5 DORÉ et al. in the occurrence database. We defined the climatic niche of each OMU as the centroid of these occurrence points within the climatic space expressed in four dimensions: mean annual temperature, mean annual specific humidity, temperature seasonality and specific humidity seasonality  (Vega et al. , 2017; MERRAclim v. 2. 0, https://doi. org/10. 5061/dryad. s2v81, accessed on 04/02/2020). As such, each OMU was associated with a set of climatic data describing its mean climate, or its bioclimatic optimum (e. g. Barnagaud et al. , 2012; Hof et al. , 2010) , strictly reflecting the centroid of its realized niche  (Hutchinson, 1957; Soberón & Nakamura, 2009).
This index conveniently allows us to quantify mimicry turnover at both global level and between pairs of communities. As such, we tested whether observed I ST was significantly higher than expectations if mimicry patterns were distributed randomly among OMUs (as in  Chazot et al. , 2014). We also developed a complementary approach to investigate if the dissimilarity between spatial distributions of species was lower for phenotypically similar species. We computed pairwise Bray-Curtis distances  (Bray & Curtis, 1957)  across the predicted distributions, obtained from species distribution models, of all pairs of OMUs with the R package vegan 2. 5-4  (Oksanen et al. , 2019 ; see SI Appendix 6). A high Bray-Curtis value corresponds to a large dissimilarity in species spatial distributions, while a low value relates to an important spatial overlap (Figure  2 ).
ME acknowledges funding by the ANR grant CLEARWING (ANR-16-CE02-0012), a Human Frontier Science Program grant (RGP0014/2016), a Leverhulme Trust grant and an ATIP grant. KRW thanks S. Nogales, the INABIO and Ecuadorian Ministerio del Ambiente for arranging permits for research in Ecuador, most recently under the project 'Diversity and Biology of Lepidoptera in Ecuador' (No. 006-19 IC-FLO-FAU-DNB/MA), and the Leverhulme Trust, the Darwin Initiative, the FLMNH Museum Associates, the National Geographic Society (Research and Exploration Grant # 5751-96) and NSF (# 0103746, #0639977, #0639861, #0847582 and #1256742). AVLF thanks FAPESP (2012/50260-6, 2013/50297-0 and 2021/03868-8), the Conselho Nacional de Desenvolvimento Científico e Tecnológico Pq-1A grant (CNPq 304291/2020-0), RedeLep-SISBIOTA-Brasil/CNPq (563332/2010-7) and NSF (DEB 1256742).
Funding information Agence Nationale de la Recherche , Grant/ Award Number:  CLEARWING  ( ANR-16-CE02-0012 ); ATIP;  Conselho Nacional de Desenvolvimento Científico e Tecnológico , Grant/Award Number:  CNPq 304291/2020-0 ;  Darwin Initiative ;  FLMHN Museum Associates ;  French Ministry of Research (MENSR) , Grant/Award Number: PhD Grant in Doctoral School 227;  Fundação de Amparo à Pesquisa do Estado de São Paulo , Grant/Award Number:  2012/50260-6, 2013/50297-0  and  2021/03868-8 ;  Human Frontier Science Program , Grant/Award Number:  RGP0014/2016 ;  INABIO & Ecuadorian Ministerio del Ambiente ;  Leverhulme Trust ;  National Geographic Society , Grant/Award Number:  5751-96 ;  NSF, Grant/Award Number :  0103746 ,  0639977 ,  0639861 ,  0847582  and  1256742 ;  RedeLep-SISBIOTA-Brasil , Grant/Award Number: CNPq 563332/2010-.
DATA AVA I L A BI L I T Y STAT E M E N T All R scripts used to conduct the analyses and generate the figures are available on GitHub (https://github. com/ MaelD ore/ithom iini_conve rgence) and Zenodo (https:// doi. org/10. 5281/zenodo. 6277769). Occurrences data, maps of the distribution of Operational Mimicry Units (OMUs) and species, and mimicry classification used in this study are available from Zenodo (Occurrences data: https://doi. org/10. 5281/zenodo. 4696055; Distribution maps: https://doi. org/10. 5281/zenodo. 4673446; Mimicry classification: https://doi. org/10. 5281/zenodo. 5497876). All results reported in this article can be reproduced with the scripts and data provided.
15 Klau et al. 16  introduced a sequential Lasso-regularized Cox PH model approach based on offsetting, called PriorityLasso, which considers input modality groups one at a time and uses the previous model prediction as an unregularized offset for the model fit with the next input group. Their model outperformed a Lasso-regularized Cox PH model on a cancer dataset in terms of IBS and has offered clinicians the chance of influencing which (groups of) variables should be preferentially included in the model by assigning them a higher priority order. 16 n addition to ample work on statistical models, researchers have also been interested in applying deep learning to multiomics survival analysis tasks. Deep learning models for multiomics integration generally rely on one of late, intermediate, or early fusion.
Their model, deemed OmiEmbed, outperformed DeepSurv and a Cox PH model based on the dimensionality-reduced input data in terms of Harrell's C and the IBS on the full TCGA dataset by integrating gene expression, DNA methylation, and miRNA. Vale-Silva and Rohr 21 presented a pan-cancer multimodal intermediate fusion neural network, Multi-Surv, which uses max-pooling to integrate the representations constructed by a separate neural network per modality, in addition to using a non-proportional hazards loss by assuming discrete follow-up times. 22 In the work of Vale-Silva and Rohr,  21  Multi-Surv outperformed a regularized Cox PH model, RSF, Deep-Surv  5  and DeepHit 23 in unimodal settings and was able to achieve state-of-the-art results when integrating multiple modalities. Per-cancer models instead fit individual models per cancer type to better account for differences between cancer types.
Research questions and matching experiments First, we were interested in which multi-omics integration survival methods performed best in the low-noise setting, in which all modalities to be integrated provided relatively high predictive value. To answer this question, we compared the performance of different methods using only two input modalities: clinical data and gene expression. This combination was shown to be effective in previous research, and initial experiments indicated that these modalities outperformed others in the unimodal setting (Table  S2 ). 13, 21, 26 e explored which integration methods were most effective in settings where certain modalities provided little or no information about patient survival. To benchmark the considered integration methods in this partially noisy setting, we first ran all considered methods using all seven available modalities in the TCGA database and compared their performance to those using only clinical and gene expression data.
We again considered the combination of clinical data and gene expression with m simulated modalities ðm = 1; 3; 5Þ. In the final experiment, we found that adding observed survival times as a feature often resulted in numerical instability when calculating survival functions using the Breslow estimator. 27 This was likely due to the extreme log hazard ratios assigned to the noisy observed survival times. As a result, we excluded the IBS from this experiment and only evaluated Antolini's concordance (Antolini's C), which was not affected by this issue. Further details regarding our datasets, experiments, and training details (e. g. , hyperparameter tuning) may be found in STAR Methods. Throughout the text, we refer to some integration methods using shortened names. We refer to the four considered statistical models, namely BlockForest, PriorityLasso, RSF, and Elastic net using their full names.
In addition, we saw a clear trend of deep learning models underperforming compared to statistical ones in terms of IBS, suggesting that deep learning models tend to be calibrated worse than statistical ones, even when using the same loss function. In fact, the best deep learning models performed worse in terms of IBS than the worst statistical ones (Table  1 ). When looking beyond aggregated performance measures and investigating the rank of each model on each cancer-type-specific dataset in terms of Antolini's C and the IBS we found a similar picture, with BlockForest and some of the intermediate deep learning methods generally securing the higher ranks for Antolini's C, while BlockForest and other statistical models dominated in terms of the IBS (Figures  1A  and 1B ). None of the considered models trained on clinical data and gene expression were able to significantly outperform the best unimodal method in terms of Antolini's C (a neural network trained on only clinical data), and only BlockForest outperformed Resource the best unimodal method in terms of the IBS (an RSF trained only on clinical data) (Table  1 ).
Similarly, all models except Intermediate (Attention), Late (Mean), and the Elastic net performed significantly worse in terms of the IBS when integrating all available modalities, relative to integrating only clinical data and gene expression (Table  1 ). For Antolini's C, most methods decreased by similar amounts (usually between 0. 01 and 0. 02, with Late (MoE) being the outlier with a decrease of 0. 035). Comparing the rank by cancer-type-specific dataset of each integration method in terms of Antolini's C and the IBS told a similar story, with most methods performing better when integrating clinical data and gene expression compared to all modalities on all but a handful of datasets (Figures  1C  and 1D ). Methods that did not use the group structure of the multi-omics data (e. g. , RSF and Early) also decreased their stratification ability  ).
For BlockForest, an initial PCA step increased noise resistance, leading to similar Antolini's C whether integrating all modalities or only gene expression and clinical data. Kaplan-Meier curves stratified by whether a patient was below or above the median predicted 5-year survival according to the model in question (see STAR Methods for further details). RSF, BlockForest, Early, and Late (Mean) shown exemplarity on esophageal (ESCA), head, neck, and shoulder (HNSC), and lung (LUSC) cancers, see supplemental information for all datasets and additional models (Figures  S1  and S2 ). P values are based on log rank tests. Cell Reports Methods 3, 100461, April 24, 2023 5.
Resource We also considered the application of a multimodal dropout unit (see STAR Methods for details). The usage of the multimodal dropout unit significantly improved discriminative performance for two of the models (Table  S4 ). Despite this, even with multimodal dropout, the considered deep learning methods performed worse with clinical data and gene expression compared with integrating all available modalities. Next, we evaluated the performance of different methods on simulated data that included simulated modalities representing Gaussian noise (as described in ''research questions and matching experiments'' and the STAR Methods). Our simulations led to similar conclusions as the experiment of integrating multiple modalities. All methods declined in performance as more noise modalities were included as inputs (Table  3 ).
Limitations of the study It is worth noting that our work has certain limitations. Since we focus on datasets from TCGA, it cannot be guaranteed to generalize to other input datasets-for example, due to a difference in the informativeness of the clinical data modality. In fact, the generalization of the present work to additional datasets and projects, such as METABRIC  28  and TARGET would certainly represent an interesting extension. Except for RSF and Block-Forest, we restricted ourselves to methods based on the Cox PH model. Although we consider it unlikely, it may be that other survival loss functions, such as the Deep Extended Hazard model  29  and others might have exhibited improved noise resistance. In addition, due to the extremely large space of possible preprocessing options, we were not able to consider all possibilities with respect to preprocessing.
METHOD DETAILS Datasets In our study, we considered 17 datasets from The Cancer Genome Atlas (TCGA). 30 Specifically, we chose only datasets that had available all modalities of interest, namely, clinical data, gene expression, protein expression, Copy Number Variation (CNV), methylation, mutation, and micro-RNA (Table  S1 ). We preprocessed data as in Wissel et al. 31  Briefly, we preprocessed all data directly from the PANCANATLAS,  30  except for CNV, which we took from Xenabrowser. 32 We used overall survival (OS) as the endpoint as per Liu et al. 33  Missing numerical variables that were missing for more than 10% of samples were removed, while those missing for less than 10% were imputed using the median across non-missing samples. Missing categorical variables were assigned a separate category.
Reproducibility All of our results can be reproduced using the code in the GitHub repository https://github. com/BoevaLab/noise_resistance_multi_omics. The repository also contains links to the splits, datasets as well as all of our results. All deep learning models were trained on a workstation running 16 cores of Intel i9-9900K CPUs @ 3. 60GHz. The inner splits (that is, the hyperparameter optimization) for all deep learning methods were parallelized across 16 threads using scikit-learn  49  and joblib. Statistical models were trained on an internal HPC system instead to speed up computation times. All statistical models except those with noise simulations and with an initial PCA step were run on a node with 34 cores of Intel Xeon (Cascade Lake) CPUs @ 2. 60GHz. Statistical models that were part of the noise simulations or that had an initial PCA step were run on a node with 56 cores of AMD EPYC-Rome CPUs @ 2.
We replaced all IBS values larger than one by the maximum for that of all other splits for the same model and dataset. We note that these replacements had no impact on Antolini's C and noted the number of replacements in Table  S5. We chose Antolini's C as opposed to Harrell's C or Uno's C 53 due to two reasons. d The IBS may only be evaluated on the survival function scale and we felt it would not have been fair to then evaluate the concordance measure on the patient-level scale (i. e. , risk-measure scale) since the survival function and risk-measure use different estimators for RSF and BlockForest (KM and expected mortality respectively). Cell Reports Methods 3, 100461, April 24, 2023 e4 Resource.
Including survival times in the clinical modality greatly improved performance for all methods, but most methods were not able to maintain this gain when integrating additional Gaussian noise modalities Mean Antolini's C and IBS across test splits and datasets when integrating clinical data and gene expression along with up to five simulated noise modalities and survival times as part of the clinical modality (see STAR Methods). Standard errors are in parentheses. k noise modalities indicates that the model was trained with k noise modalities of dimensionality 10,000, in addition to clinical data and gene expression. ''With target'' indicates that the true survival time was added to the clinical features. * indicates performance significantly better than the same model trained on only clinical data and gene expression (without noise modalities and target).
[ denotes a metric for which higher values are better, Y the opposite. P values are based on paired one-sided Wilcoxon signed-rank tests (see STAR Methods for details). All the data reported in this paper have been deposited at Zenodo and are publicly available via the DOI in the key resources table. All raw data used within this paper are accessible via PANCANTALAS and Xenabrowser. We also provide scripts to automatically download and preprocess the raw data via a bash script that is accessible on our Github repository. Links and DOIs are listed in the key resources table. d All original code has been deposited on Github and is publicly available. The Github repository link and archival DOI are listed in the key resources table. d Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.
Data and code generated for this study This paper Zenodo: https://doi. org/10. 5281/ zenodo. 7529459; GitHub: https://github. com/BoevaLab/ Multi-omics-noise-resistance Python 3. 10 The Python Software Foundation https://www. python. org/; RRID:SCR_008394 R 4. 1. 3 The R Project for Statistical Computing https://www. r-project. org/; RRID:SCR_001905 Numpy 1. 23. 0 The NumPy community https://numpy. org/; RRID:SCR_008633 scikit-learn 1. 1. 1 The scikit-learn community https://scikit-learn. org/; RRID:SCR_002577 Pandas 1. 4. 3 The Pandas community RRID:SCR_018214; https://pandas. pydata. org/ Skorch 0. 12. 0 The skorch community https://skorch. readthedocs. io/en/stable/ Pytorch 1. 12. 0 The Linux Foundation RRID:SCR_018536; https://pytorch. org/ scikit-survival 0. 19. 0 The scikit-survival community https://scikit-survival.
0035) Intermediate 0. 622 0. 575 0. 172 0. 182 (Embrace) (0. 0056)* ,c (0. 0057) (0. 0024) c (0. 0026) Intermediate 0. 635 0. 602 0. 174 0. 186 (Max) (0. 0055)* ,c (0. 0056) (0. 0025) c (0. 0027) Intermediate 0. 636 0. 615 0. 171 0. 176 (Mean) (0. 0054)* ,c (0. 0056) (0. 0023) c (0. 0025) Late (Mean) 0. 631 0. 627 0. 172 0. 171 (0. 0056)* (0. 0054)* (0. 0025) (0. 0023) Late (MoE) 0. 599 0. 564 0. 179 0. 188 (0. 0056) c (0. 0054) (0. 0024) c (0. 0034) Mean Antolini's C and IBS across test splits and datasets when integrating clinical data and gene expression compared to integrating all of the available modalities (described in TableS1). Standard errors are in parentheses. * indicates values that were not significantly worse than the best model per column. a denotes a metric for which higher values are better.
176 (0. 0024)* 0. 177 (0. 0025)* (Max) Intermediate 0. 636 (0. 0059) 0. 635 (0. 0056) 0. 614 (0. 0056) 0. 614 (0. 0057) 0. 167 (0. 0023)* 0. 169 (0. 0023) 0. 173 (0. 0023) 0. 175 (0. 0024)* (Mean) Late (Mean) 0. 633 (0. 0058) 0. 628 (0. 0059) 0. 622 (0. 0057) 0. 626 (0. 0055) 0. 168 (0. 0022)* 0. 170 (0. 0023)* 0. 169 (0. 0023)* 0. 171 (0. 0023) Late (MoE) 0. 597 (0. 0057) 0. 597 (0. 0058) 0. 561 (0. 0054) 0. 566 (0. 0053) 0. 175 (0. 0022)* 0. 174 (0. 0023)* 0. 178 (0. 0023)* 0. 177 (0. 0023) * Mean Antolini's C and IBS across test splits of the considered datasets on TCGA when integrating clinical data and gene expression or all available modalities combined with using PCA for dimensionality reduction per modality, with a specific number of components. Standard errors are in parentheses. Ã indicates significantly better performance compared to the same model trained without dimensionality reduction (Table1).
629 (0. 0055) 0. 625 (0. 0057) 0. 615 (0. 0056) y 0. 176 (0. 0023) y 0. 185 (0. 0023) y 0. 195 (0. 0027) y Late (MoE) 0. 573 (0. 0049) y 0. 546 (0. 0048) y 0. 541 (0. 0049) y 0. 186 (0. 0024) y 0. 196 (0. 0032) y 0. 204 (0. 0034) y Mean Antolini's C and IBS across test splits and datasets when integrating clinical data and gene expression along with up to five simulated noise modalities, and optionally, the true survival time (see STAR Methods). Standard errors are in parentheses. k noise modalities indicates that the model was trained with k noise modalities of dimensionality 10,000, in addition to clinical data and gene expression. y indicates performance significantly worse than the same model trained on only clinical data and gene expression (without noise modalities and target). [ denotes a metric for which higher values are better, Y the opposite.
Data collection As most children could not directly answer self-report questionnaires, various data sources were collected to mitigate the bias associated with each source and get closer to the child's view. They include practitioners' observations (questionnaires and interviews), three video recordings (two cameras and a video recording of the child's AR view), and children's drawings. As no validated questionnaire to be filled by practitioners existed, we used the semi-structured questionnaire we previously devised for our pre-tests  (Bauer et al. , 2022). First, six questions address the child's state before and after the sessions. Nineteen questions then address the child's experience. Practitioners can finally add comments. The questionnaire lasts between five and ten minutes. Each item consists of 1-5 or 1-7 Likert-type scales (depending on the references that it draws from) and optional comments.