dataesr/scientific-paragraphs-categorization huggingface dataset:
According to  [64] , in practical cases, a modularity above 0. 3 is a significant indicator that the given network exhibits a strong community structure. The networks obtained in this work exhibit high modularities (≥0. 5), as reported in Table  3. In general, the networks obtained using GXN•OMP tend to be splitted into more communities, which were also more homogeneous in size, while those obtained using GXN•EN tend to be splitted in fewer communities, and to be heterogeneous in size, as shown in Figure  5a. Moreover, the R. norvegicus and C. familiaris networks exhibited more communities than the H. sapiens datasets, which could possibly reflect the fact that the former networks were obtained from datasets containing more tissues than the H. sapiens datasets. In terms of inner quality, both methods allow to obtain in general high validation R 2 determination coefficients for most TGs, nevertheless GXN•OMP allowed to obtain better results (median R 2 scores between 0.
8 and 0. 9) than the GXN•EN (median R 2 scores between 0. 7 and 0. 8) as shown in Figure  6a. Moreover, GXN•OMP models required between 2 and 5 times less run-time to be fitted than GXN•EN ones, as depicted in Figure  6b. In summary, for the C. familiaris, R. norvegicus and H. sapiens RNA-seq datasets, GXN•OMP models outperform GXN•EN ones in terms of R 2 coefficients and run-times, both methods exhibit similar results in terms of sparsity, and GXN•OMP exhibits better modularities for C. familiaris and R. norvegicus but worse for H. sapiens. Finally, the communities that were identified in the GXN•OMP and GXN•EN models were tested, through a GSEA, for enrichment in terms of expression on specific tissues, as well as for enrichment of anatomical GO terms. Several GXN•OMP and GXN•EN communities clearly exhibit significant GSEA Normalized Enrichment Scores (i.
e. , the genes from each community exhibit concordant, and statistically significant, over/underexpression on specific tissues), as well as statistically over-represented anatomical GO terms. Interestingly, some GXN•OMP and GXN•EN communities present even both significant GSEA and GO enrichment Scores exhibiting biological coherence, as depicted in Figures  7 8 9 , suggesting that such communities may have functional roles in the different tissues represented in the RNA-seq datasets.
C. familiaris R. norvegicus H. sapiens For instance: (1) In the C. familiaris dataset, communities 0 from both GXN•OMP and GXN•EN, exhibit important enrichment scores for the heart development GO term. (2)  In the same dataset, community 23 from GXN•OMP and community 4 from GXN•EN exhibit important enrichment scores for the neuroretina and for the retina development GO terms. (3) similarly, in the R. norvegicus dataset, communities 39 and 77 from GXN•OMP and community 5 from GXN•EN present an enrichment score for the brain, as well as GO terms related to the development of the nervous system. (  4 ) Communities 5 and 6 from GXN•OMP are enriched in the uterus and also exhibit an uterus development enrichment GO term (5) Community 15 from GXN•EN is enriched in the liver and also exhibits GO terms related to the development of liver and pancreas.
(6)  In the H. sapiens dataset, Communities 8, 17, 20, 23 and 25 from GXN•OMP and 1 and 10 from GXN•EN are enriched for the neuron, and also present different GO terms relative to the development of the nervous system. In order to deepen the analysis regarding intra-communities regulatory interactions, we represent, in Figure  10 , the regulatory links of communities C f |23 and Rn|39 (the full list of TFs from these communities are presented in Table  A1 ). Interestingly, the community C f |23 from the GXN•OMP model of the C. familiaris dataset, contains important regulators involved in the development of the neuroretina. For instance POU6F2 is involved in the development of retinal ganglion and amacrine cells  [71] , PRDM13 is involved in the development of amacrine cells  [72]  and IRX6 is a key regulator of retinal interneuron subtype identity.
Regarding community Rn|39 from the GXN•OMP model of the R. norvegicus dataset, it contains important TFs involved in the development of neurvous system. For example Fezf1, a well conserved TF that controls neurogenesis and cell-fate in the mammalian nervous system development  [73] , Neurog2, a TF involved in determining neuronal type fate  [74] , and POU3F2, a TF plying a key role in brain development  [75].
Subpallium Dev. (a)  Interestingly, the confusion matrices between GXN•OMP and GXN•EN communities for the C. familiaris, R. norvegicus and H. sapiens datasets, shown Figure  11 , exhibit a strong correspondence. A Pearson's χ 2 test of independence for each on the the matrices, reveals that the independence hypothesis between the GXN•OMP and GXN•EN communities is rejected with corresponding χ 2 statistics of 14,233. 87 for R. norvegicus, 9,803. 57 for H. sapiens and 11,685. 36 for C. familiaris, which correspond in all cases to near 0 p-values, suggesting that the communities structures obtained using both methods are robust. 12 2 1 3 6  4 2 1 2 4 6 3 1 3  9 3 2 2 1 1 4 1 3 4 3 1 2 4 1 1 1 1 3 1 1 3 1 1 2 1 2 2 1 2 1 1 1 2 5 1 1 1 1 1 1 1 1 2 6 6 1 6 3 2 1 1 1 1 2 2 5 1 3 2 1 2 3 1 1 3 2 5 4 3 2 2 1 2 1 1 2 2 1 1 3 1 1 1 2 1.
Alzheimer/Control Patients Case Study Comparing two groups of gene expression samples (e. g. , control-disease, controltreatment), in order to identify genes that characterize each group is an important task in biomedical and biological applications. For instance, Differential Gene Expression analysis, is a classical methodology that aims at comparing gene expression levels between two groups of samples, to identify significantly up-regulated or down-regulated genes in one condition compared to the other one  [76]. In this work, we illustrate how the GXN methodology can be used to identify significantly up-regulated or down-regulated communities within a GRN. To do so, we analyzed the Alzheimer/Control H. sapiens RNA-seq dataset, described in Section 2. 6, using the GXN•OMP method with parameter d max 0 = 30, since this technique performed better than GXN•EN, on all the other RNA-seq datasets.
Then, we applied the different steps described in Section 2. 9: (i) The Clauset-Newman-Moore technique  [64]  was used to partition the GXN into communities (with a resolution parameter r = 1. 75 determined as explained in Section 2. 9), (ii) the GSEA  [67]  was run to determine communities of genes that are collectively over-expressed or underexpressed in Control or Alzheimer conditions, and (iii) the GOATOOLS  [68]  library was used to extract statistically over-represented GO terms (descendants of the biological process term GO:0008150) for each community. The network inferred from Alzheimer/Control H. sapiens RNA-seq dataset exhibits a high sparsity level (≥ 99. 14%), and a high modularity (≥ 0. 67). The characterization of the GXN•OMP model obtained in terms of topological and inner-evaluation metrics are shown in Figure  12.
The median number of regulators involved in the control of each gene was approximately equal to 12 regulators, while each regulator has 88 TGs in median. In terms of inner quality, the R 2 determination coefficients obtained for the prediction of TGs' unseen expressions is lower than those obtained for the other RNA-seq datasets, but remain descent for a large population of genes, since the median R 2 score was close to 0. 57 (only TGs with a score higher than 0. 5 where kept for further analysis). Finally, the runtimes where comparable to those obtained for other RNA-seq datasets (0. 076 seconds to fit TG's model in median). The Clauset-Newman-Moore technique has detected 40 communities. Among these communities, 26 exhibit significant GSEA Normalized Enrichment Scores and 18 exhibited enriched GO terms.
Discussion In this work we have proposed two generalization-aware self-expressiveness GRN inference tools, called GXN•OMP and GXN•EN, based respectively on OMP and ElasticNet linear regression algorithms. The generalization capabilities of the regression methods were optimized using an inner cross-validation procedure, and then evaluated using an outer cross-validation step. We assessed the performance of both methods using the 4 datasets from well-known DREAM5 benchmark  [24] , and we compared their results to those obtained by state-ofthe-art methods, namely SVR and RF. The quality of the results obtained by the selfexpressiveness methods presented in this paper are comparable to those obtained by state-of-the-art methods, regarding external quality metrics and generalization capabilities.
In addition, unlike state-of-the-art methods, the self-expressive models exhibit high levels of sparsity, making them directly analyzable, without requiring the application of postprocessing steps (e. g. , select top-k links, apply a threshold) to focus only on the most interesting links. We have also applied both methods to three complex eukaryotic multi-tissue RNA-seq datasets. The GRNs inferred by these methods revealed to exhibit a sparse and modular structure, and their inner communities of TFs showed statistically significant over/underexpression on specific tissues and cell types, as well as significant enrichment for some anatomical GO terms, which suggests that such communities may also drive important functional roles. Indeed, both methods have revealed, for instance, communities of TFs particularly related to the development of the heart in C.
Moreover, given the importance of the control/disease (or control/treatment) experimental configuration, and its associated differential expression analysis, in this work we analyzed an Alzheimer/Control RNA-seq study case, in order to illustrate how the GXN methodology can be used to perform differential GRN analysis. This analysis revealed 12 communities presenting significant GO and GSEA enrichment scores, and thus are likely to have important differential functional roles in Alzheimer disease and control groups respectively. In addition, a more in-depth analysis of the regulatory links present in two of these communities has revealed the links between important TFs that have shown to be involved in the Alzheimer disease in previous works. In order to infer tissue or conditionspecific gene networks, a promising research direction seem the adaptation of this new formalism to the analysis of single-cell RNA-seq datasets  [96].
The experiments and the methods implementations are available online in a dedicated gitlab repository https://gitlab. com/bf2i/gxn (last updated on 20 February 2023) and the software can be installed as a Python library, from the Python official third-party software repository for Python https://pypi. org/project/GXN/ (last updated on 20 February 2023). In addition to the implementation of the GXN•OMP and GXN•EN methods and dedicated tutorials, the GXN package includes different visualization and data exportation functions that would allow the users to represent and extract the GXN models regulatory links. These sources of information could be valuable inputs for biologist users in order to better understand the regulatory processes involving specific genes of interest. The results obtained in this work suggest that generalization-aware self-expressive gene networks inference is a novel and promising methodology to produce, assess and tune, simple, interpretable, and predictive GRN models.
datasets studied here do not exhibit important batch effects, since samples tend to cluster according to tissue origin. ANKAR, ARID3C, DACH2, ENSCAFG00000023024, ENSCAFG00000032541, ESR2, FOXN4, GBX1, GBX2, GSX1, GSX2, HELT, HES5, IRX6, NHLH1, NR2E1, ONECUT2, ONECUT3, POU4F2, POU4F3, POU6F2, PRDM13, PRDM8, PRMT8, PTF1A, SALL3, SEBOX, SOX8, TCF23, TENM2, TFAP2D, VSX1, ZGLP1, ZIC4, ZNF556.
Funding: This work was supported by the  BQR INSA Lyon 2023 Neurinfo , the  French National Institute for Agriculture, Food, and Environment (INRAE) , and the  National Institute for Applied Sciences (INSA Lyon ). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. Institutional Review Board Statement: Not applicable, since the data on animal or humans used in this study, have not been produced by us, and are publicly available in the dedicated databases (see.
Data Availability Statement: The scripts and the methods implementations as well as the C. familiaris, H. sapiens, R. norvegicus and the Alzheimer vs. Control datasets are available online at https:// gitlab. com/bf2i/gxn (last updated on 20 February 2023). And the DREAM5 dataset is available at https://www. synapse. org/#!Synapse:syn2787209/wiki/70350 (last accessed 20th of February 2023). The software can be installed as a Python library, from the Python official third-party software repository for Python https://pypi. org/project/GXN/ (last updated on 20 February 2023).
FitFigure 12. 12 Figure 12. Distribution of TGs in-degree (a), TFs out-degree (b), R 2 determination coefficients (c), run-times (d) needed to train each TG model, and number of TGs within each community sorted in decreasing order (e), for the H. sapiens Alzheimer/Control dataset; (a) TGs in-degree; (b) TFs out-degrees; (c) R 2 score; (d) Run-time; (e) Communities sizes.
The International Agency for Research on Cancer (IARC) reported the status and the performance of cancer screening programs from EU Member States in the years 2008 and 2017 (refs. 8,9). Such consecutive evaluations permit comparisons in the performance of screening programs using a harmonized set of indicators. Outside the EU, cancer screening evaluation reports have only been published regularly in a few countries 10, 11. Most LMICs have only been reporting screening coverage based on population surveys because of logistic, fiscal and organizational challenges of data collection across the screening continuum  12, 13. In 2019, IARC launched the Cancer Screening in Five Continents (CanScreen5) project, which aims to collect, analyze and disseminate information on cancer screening programs globally, and encourage and support countries to routinely collect screening performance data.
This global project gathers information and performance data on breast, cervical and CRC screening programs in a standardized manner using an online portal (https://canscreen5. iarc. fr/). Validated data made publicly available through the portal will support program managers in cancer screening evaluation, benchmarking, quality improvement and informed policy formulation. This study describes how the CanScreen5 project works and reports the status, organization and performance of screening programs that have participated in the project up to September 2022. The key outcomes of the current study and their implications to inform policies in cancer screening are displayed in Table  1. Article https://.
The CanScreen5 project is a global cancer screening data repository that aims to report the status and performance of breast, cervical and colorectal cancer screening programs using a harmonized set of criteria and indicators. Data collected mainly from the Ministry of Health in each country underwent quality validation and ultimately became publicly available through a Web-based portal. Until September 2022, 84 participating countries reported data for breast (n = 57), cervical (n = 75) or colorectal (n = 51) cancer screening programs in the repository. Substantial heterogeneity was observed regarding program organization and performance. Reported screening coverage ranged from 1. 7% (Bangladesh) to 85. 5% (England, United Kingdom) for breast cancer, from 2. 1% (Côte d'Ivoire) to 86. 3% (Sweden) for cervical cancer, and from 0.
Main findings and limitations Eighty-four countries joined the CanScreen5 project and shared qualitative or quantitative (or both) data on breast cancer, cervical cancer and CRC screening programs. Substantial heterogeneity exists between cancer sites and between countries regarding program organization, covering screening policies, protocols, governance, financing mechanisms, systems of invitation and recall, processing of data collected for program monitoring and quality assurance. Policy implications Substantial heterogeneity in screening program performance revealed by the first batch of data from the CanScreen5 project underscores the need for many such programs to do further in-depth analysis of their performance, identify the scope for improvement and take appropriate measures. To implement corrective actions, program managers need to be aware of the implications of the outcome indicators; for example, a low detection rate may indicate poor performance of the screening or diagnostic tests (or both) or low compliance of screen-positives, but may also be due to low prevalence of disease especially in a frequently screened population.
The gap in data collection across the screening continuum in both high-and low-resource settings is a concern. Programs need to build robust information systems to be able to capture screening performance data and use the same for quality improvement. Almost all countries worldwide have invested greatly to strengthen disease surveillance mechanisms (including improvement of health information systems) to mitigate the COVID disease pandemic. Cancer screening programs need to leverage these new developments to improve their own performance and quality. The reporting format is country or region, reporting year and screening protocol. Mx, mammography. a Mexico: the target population is not the total of individuals eligible for screening (age-based). b Japan: women with a negative screening test receiving further assessment due to clinical recommendation were also included.
Through fulfillment of these criteria, screening programs can ensure that any inherent harms are minimized and outweighed by the potential benefits at the population level. With an ultimate objective to improve the quality and impact of cancer screening programs, the CanScreen5 project engaged directly with screening program managers and coordinators and trained them to submit information from their own programs related to 13 of the 16 essential criteria identified by the IARC expert group (Methods). With the help of global experts, CanScreen5 also listed and defined key indicators to measure performance across the screening continuum (Supplementary Table  4 ). Trained participants were requested to submit data from their respective programs to enable the project to estimate these indicators.
Currently, European programs have standards for a limited number of indicators, for example, for CRC screening, participation rate to screening out of those invited, further assessment participation rate and completion rate of follow-up colonoscopy  9. Collecting high-quality data on a continued basis will allow programs to set their own standards. CanScreen5 also identified irrational policies and cancer screening practices in some countries. A decision to introduce a new screening program depends on disease burden, availability of resources, health system preparedness and prioritization of healthcare needs in the country. LMICs struggling to maintain a cervical cancer screening coverage over 10% have little justification to introduce a breast cancer screening program, as reported from Bangladesh.
Instead of collecting data from the program, LMICs mostly use data from the WHO's STEPwise approach to surveillance surveys to report screening coverage  13, 23. Such data are dependent on self-reports, which are subject to recall bias  24. Moreover, screening coverage correlates poorly with the impact of screening (such as reduction in mortality) as demonstrated in several Latin American countries, underscoring the need for programs to measure the performance of diagnostic and treatment services  25. Building on the experience from the Canscreen5 project, cancer screening programs should consider the following measures to improve data quality and completeness: (1) conduct a thorough assessment of services associated with screening based on the information and data available; (2) identify the essential criteria for organized programs that are either missing or poorly implemented; (3) develop a feasible, measurable and time-bound plan in consultation with all stakeholders to improve the quality of services at different levels; (4) dedicate an adequate budget for quality assurance and put together a team responsible for implementing quality assurance, if not already in place; (5) build or strengthen information systems to capture performance data so that the quantitative data collection tools can be completed and KPIs can be estimated; (6) create links with population databases (for example, electoral rolls or birth registers) to be able to identify screen-eligible individuals and with PBCR to monitor impact; (7) leverage the vertical investments made to improve surveillance systems and mobile health applications to mitigate the coronavirus disease (COVID) pandemic; and (8) invest in capacity building of policymakers, managers and health professionals engaged in screening-related activities to be able to understand the value and application of quality assurance.
The CanScreen5 project has limitations. Although our ambition was to reach out to all countries and build a data repository as an IARC flagship program, that is, the Global Cancer Observatory (https://gco. iarc. fr), at this stage of the project we could only manage to collect data from a limited number of countries. As the project matures and published data become more visible, we hope to involve more countries as part of the network. The reasons for nonparticipation of some countries we approached to participate include: voluntary nature of participation (no national or global mandate); nonavailability of approval from higher authorities; and reluctance of programs to share data because of the fear of receiving criticism for poor performance. Another limitation is that the data collected from EU countries in 2016 are out of date.
A new round of data collection from Europe will be initiated by IARC in 2023 to update these data. Furthermore, the quantitative data from most LMICs is very incomplete. Sometimes programs are reporting the number of examinations and tests performed and not the number of participants undergoing screening, which makes it difficult to exclude participants undergoing repeat testing within a short interval. At this stage, CanScreen5 is collecting screening data on three cancer sites for which screening is most prevalent. However, as screening for other cancer sites becomes evidence-based and is implemented, for example, lung and prostate cancers, we have plans to include these too. The strength of the CanScreen5 project is that we have not relied on secondary data sources; instead, we collected information provided and validated by program coordinators.
This global initiative collects cancer screening performance data beyond screening coverage. In conclusion, the CanScreen5 project is a dynamic, ongoing activity and not just a one-time data collection project. We will continue with our engagement with countries, especially LMICs, to enhance data collection and quality. Investments in information technology infrastructures, high population coverage with broadband and Internet facilities, and digital capability building of the health workforce to mitigate the COVID pandemic-induced health crisis have created an enabling environment for countries to strengthen multisectoral digital healthcare  26. We are optimistic that screening programs will take advantage of this accelerated digital transformation to reform the process of data collection.