dataesr/scientific-paragraphs-categorization huggingface dataset:
INTRODUCTION The availability of health data from patient medical records is increasing, and these data constitute, in theory, a rich resource for research purposes. However, despite the unprecedented amount of information now available, health data remain underexploited due to their heterogeneity and complexity. There is, therefore, an urgent need for innovative tools, based on intuitive and interactive graphical interfaces, specifically designed for the exploration of health data by medical practitioners. Data visualization is gradually emerging as a new field of research, and graphical representations are used for two main purposes: (i) explanatory illustration, to highlight novel scientific insights graphically and to ensure efficient communication between scientists  [1] [2] [3] [4]  ; and (ii) exploratory analysis, searching for relationships previously overlooked and leading to new discoveries, thereby maximizing the potential of information-rich databases.
We present here an exploratory analysis of a global clinical network from a large breast cancer cohort, with a novel interactive graphical interface for the exploration of health data. We previously developed an advanced computational method for graphical analyses, including causal relationships, from multivariate data  5. The underlying MIIC (Multivariate Information-based Inductive Causation) algorithm, which was released as an online server  6  , uses a machine learning method combining constraintbased and information theory approaches to reconstruct causal, non-causal or mixed networks from large datasets. The MIIC algorithm was first developed to analyze categorical genomic data  5, 6  and has recently been extended to the analysis of more challenging heterogeneous datasets, such as medical records, combining both categorical and continuous variables, in which interdependence is notoriously difficult to assess  7.
Breast cancer (BC) clinical datasets are particularly suitable for the type of exploratory analysis presented here, as BC is a complex heterogeneous disease highly variable in its aggressiveness and prognosis. BC remains one of the leading causes of cancer-related death among women. The BC patients included in the cohort analyzed here were treated with neoadjuvant (or preoperative) chemotherapy (NAC). NAC was originally restricted to patients with inflammatory or locally advanced BC, but is now the standard care for aggressive early-stage breast cancers, i. e. , triple-negative (TNBC) and HER2-positive BCs  8, 9. From the patient's viewpoint, the benefits of the neoadjuvant strategy include a greater feasibility of breast-conserving surgery and the prognostic stratification of risk obtained after analyses of the residual tumor burden at surgery.
DISCUSSION When applied to a large cohort of BC patients, the MIIC algorithm successfully (i) performed quality controls; (ii) identified intra-and inter-modality correlations; (iii) highlighted differences in clinical practice, including center specificities; (iv) traced the natural course of the disease; (v) highlighted unsuspected and hidden associations, leading to new discoveries. The interactive visualization and causal analyses provided by this algorithm make it a promising tool for fast and effective explorations of the increasing amount of available health data. The amount of exploitable health data is increasing exponentially. The best known health data resource for cancer studies remains the SEER (Surveillance, Epidemiology, and End Results) database, which collects data from population-based cancer registries covering approximately 34.
6% of the US population  51, 52. By 2016, the National Cancer Database (NCDB) had amassed more than 34 million hospital records from cancer patients (almost four times the size of the SEER database), to become the largest clinical cancer registry in the world  53. In France, the French administrative healthcare database, the SNDS (Syste ̀me National des Données de Santé), is one of the largest administrative databases in the domain of medicine, providing many opportunities for medical research  54, 55  , as it covers 99% of the French population (about 66 million people). The French government is planning to ease access to this almost exhaustive population research resource, through release as part of the "Health data hub" project. Finally, beyond these structured databases, the largest mine of untapped data worldwide remains the content of electronic health records (EHRs), encompassing a full range of data (clinical notes, laboratory results, imaging, genetic data, etc.
) relating to patient care. Recent advances in information technology have made it easier for both hospitals and healthcare institutions to collect large amounts of healthcare data. Biomedical scientists are now facing new challenges in the management and analysis of massive, heterogeneous datasets  56. These challenges include the development of tools for exploration and visualization, analytical methods, integration into a comprehensive overview, and translation of the findings into public health impact. The visualization of information makes it possible for users to find profound patterns in clinical data, through visual recognition. Simple charts cannot represent the complexity of big data analyses and fail to support multifaceted tasks effectively  3, 4. There is, therefore, a need for sophisticated visualization tools dealing with many elements simultaneously and enabling users to perceive the patterns and insight generated by the algorithm  57.
Supplementary Table  1  shows the main data visualization tools used to present medical data. Many of the visual methods have been adopted directly from the field of data mining, but others, specific to the healthcare domain, have also been designed (Supplementary Table  2 ). For example, Happe and Drezen built the ePEPs toolbox, which displays relevant patterns extracted by eye from patient reimbursement data in the SNDS database, and supporting interactive exploration by researchers  58. CARRE provides web-based components for interactive health data (fitness and biomarkers) visualization and risk analysis for the management of cardiorenal diseases  59. The MITRE Corporation has also developed a web-based solution that provides an overview of an individual's health through graphical representations of EHR data, highlighting abnormal values  60.
None of these visualization programs has yet managed to bridge the gap between of the large amounts of clinical data available and the discovery of clinical knowledge or paths for scientific research. By processing large heterogeneous sets of variables inherent to clinical records, MIIC provides physicians with a full picture of BC disease. It will be interesting to see how extending the present cohort of BC patients to larger BC cohorts treated with similar NAC therapy will allow us to refine the visual clinical network presented here, Fig. 1. In addition to this use for visualization, the MIIC algorithm presents several other advantages for analyses, including its unsupervised nature, overcoming the need for training or human involvement. This feature makes it possible to obtain new knowledge through the automatic identification of patterns and dependences in the data, highlighting new interactions, and it may be of use for feature selection in machine learning models.
Each edge corresponds to a "direct" association between two variables, that is, a statistical association that cannot be entirely explained by indirect effects involving other variables. Red and blue edges correspond to positive and negative (i. e. , anti-correlated) associations, respectively. Four types of edge orientations are distinguished by the MIIC online server: (i) directed edges with a gray arrowhead represent inferred causal relationships; (ii) bidirected edges (drawn with dashed lines) reflect the presence of a latent common cause (L) unobserved in the available dataset, i. e. , X←(L)→Y; (iii) directed edges with a colored (red or blue) arrowhead are consistentent with either a causal or a latent common cause relationship; and (iv) undirected edges, whose orientation if it exists, cannot be inferred from non-perturbative data.
The original algorithm was restricted to categorical variables  5  , but MIIC has recently been extended to include continuous variables, the values of which are partitioned into optimal bins, maximizing mutual information with another (continuous or categorical) variable of interest, while preventing the overfitting of datasets of finite size due to the use of too many bins  7. In particular, each continuous variable may have different information-maximizing partitions depending on the associated variable of interest. For instance, MIIC finds three maximally informative bins for the residual cancer burden (RCB) score in association with patient survival status (Supplementary Fig. 1A ), whereas eight RCB bins are required to estimate its mutual information with post-NAC cellularity correctly (Supplementary Fig.
MIIC online server The MIIC online server is freely accessible at https://miic. curie. fr and can be used with the Google Chrome, Mozilla Firefox, Edge, and Safari browsers. The user guide summarizing the main steps for running the MIIC algorithm is accessible at https://miic. curie. fr/user_guide. php, and an online video tutorial is available at: https://miic. curie. fr/tutorial. php. The workbench is available from https://miic. curie. fr/workbench. php. As input data, the user can upload a dataset formatted as a table with commas, semicolons, tabs, pipes or colons, as separators, without row names. Each variable can be either categorical or quantitative (discrete or continuous). Variables can be grouped into families, identified with different colors on the network. Missing values are allowed in the dataset and their possible statistical biases are taken into account by MIIC  7.
Alternative proposed MIE were 'deposition of ionizing energy' (MIE 1550) or 'increase, DNA damages' itself (KE 1194). However, for the sake of coherence and transposability with the MELODI/Alliance WGs it was decided to keep 'deposition of energy' as the MIE for microcephaly and highlighted that further discussion and harmonization of potential MIEs could be relevant within the radiation community. The integration of the effects observed at different levels of biological organization, central for the AOP framework, was identified as closely linked to the concept of systems biology. The existence of a high-throughput dataset describing the effects of IR on gene expression leading to microcephaly in the mouse embryonic brain  (Mfossa et al. 2020 ) was discussed as an interesting approach to identify KEs and characterize the KERs, but such analysis was beyond the reach of the group.
Instead, the screening of additional databases useful for AOP development, such as the Comparative Toxicogenomics Database (CTD), GeneCards, AOP-Wiki and PubMed databases were highlighted as useful approaches to capture and assess evidence systematically. The integration of these data to the results of the scoping review, also performed by the WG, was thus proposed as an innovative way to incorporate the existing knowledge to the microcephaly AOP and ensure its comprehensive assembly.
Radiation-induced microcephaly, test-case application of a machine learning method Given the enormous amount of existing knowledge in the scientific literature and other data sources, the identification of relevant biological information to build AOP is a complex and time-consuming task, especially in deciphering sparse data. Thus, to assist in the AOP development process, the WG discussed that the use of artificial intelligence (AI) and automated data mining methods can represent a complementary approach to the expert manual review of the available data. From this perspective, an innovative AI tool, called AOP-helpFinder, that combines graph theory and text mining, was developed to automatically explore the scientific abstracts from the PubMed database  (Carvaillo et al. 2019; Jornod et al.
Doing so the co-occurrence of 'ionizing radiation' with 'Microcephaly' or 'Small head size' in the abstract of PubMed database was scanned. Other phenotypical terminology such as brain atrophy, gliosis, or reduced neurogenesis where considered less precise. After automatic computational exploration of 125,226 abstracts related to IR from PubMed database, the AOP-helpFinder tool allowed to gather 47 articles associating IR and microcephaly (or small head size). Among these 47 links, a manual curation of the abstracts allowed to keep only the 9 epidemiological articles dealing with exposure to IR in humans. All these 9 publications were fully evaluated and discussed in the WG. Two types of epidemiological studies were discriminated, either related to A-bomb survivors in Hiroshima and Nagasaki  (Plummer 1952; Miller and Blot 1972; Otake and Schull 1998)  or exposures after the Chernobyl NPP accident  (Wertelecki et al.
Preliminary AOP based on expert consultation Based on the discussion in the WG, it was possible to propose a preliminary AOP related to radiation-induced microcephaly (Figure1). acutely to 1 Gy and 2 Gy of X-rays  (Kashiwagi et al. 2018). The authors also quantified TP53BP1 by immunofluorescence and describe that the peak of DNA damages (10 min after irradiation) precedes, as expected, the peak of TP53 activation (3h to 5h after irradiation). Similar data were obtained by  Limoli et al. (Limoli et al. 2004 ), but with higher dose (5 Gy). In the context of microcephaly, the link between increased DNAdamages and TP53 activation was demonstrated by immunofluorescence using H2Ax and TP53BP1 on brain cryosections and immunoblotting against phosphorylated TP53 in Citk-/-E14. 5 mouse embryos  (Bianchi et al.
Acknowledgements: This project was supported by funding from the  Euratom research  and training program  2019-2020  under grant agreement No. 900009  (RadoNorm) ; the  Research Council of Norway (RCN)  through its  Centre for Excellence  funding scheme (Project No. 223268  and project No. 268294  ' MixRisk ') and  NIVAS Computational Toxicology Program , NCTP (www. niva. no/nctp, RCN project No. 160016 ). Disclosure statement All authors declare that they have no competing interests. The opinions expressed and arguments employed herein are those of the authors and do not necessarily reflect the official views of the OECD or of the governments of the OECD Member Countries. 1194 Increase,  DNA damage Immunostaining Western Blot Barazzuol  and Jeggo 2016 Saha et al. 2014 Kashiwagi et al. 2018 Nowak et al.
Mohamed Abderrafi Benotmane  (MAB), Ph. D, is a researcher project leader at the  Radiobiology Unit at the Belgian Nuclear Research He  coordinated the  FP7 EU project CEREBRAD  ( GA 295552 ) ( 2011-2015 ) and participated to many other EU projects (DoReMi,  OPERRA ,  CONCERT , and  MEDIRAD). He is a member of the  High Council for Health in Belgium. He is delegate at the High-level group on low-dose research (HLG-LDR)He is member of the of the  High-Level Group   on Low-Dose Research  ( HLG-LDRMagdalini Sachana  (MSShe is member of the  ICRP Task Groups  110 (Veterinary  Knut Erik Tollefsen  (KET)is a Chief Scientist at the  Norwegian Institute for Water Research (NIVA)  and an Adjunct Professor at the  Norwegian University of Life Sciences (NMBU). He is a Norwegian delegate at the High-level group on low-dose research (HLG-LDR),  OECD Extended Advisor Group on Molecular Screening and Toxicogenomics (EAGMST) , co-chair of the  HLG-LDR Rad/Chem AOP Joint Topical Group  and coordinates  NIVAS's Computational Toxicology Program , NCTP (www.
functional analysis (i. e. mutants, transgenesis and other mechanistic data) are thus required to delineate causes and consequences in the context of DNA damage induced by IR. It is also noteworthy that most AOP related to DNA damage in the AOP-Wiki database relate to cancer effects. If cancer is indeed a major outcome following exposure to a relative low to moderate dose of IR, non-cancer effects (including developmental effects but also cardiovascular or cognitive effects in adults) remain less studied and should not be neglected. The final proposed AOP is relatively simple and more dedicated work is required to assess WoE in a systematic and transparent manner as well as identifying additional MIEs and KEs relevant for the same AO. This effort would entail developing additional AOPs that collectively can form an AOP network.
Sample In this section, we give a brief description of the high-redshift samples used in this work and their available photometry. In this work, we used X-ray catalogues with sources in the early Universe (z 3. 5). We constructed the sample using data from different fields of various areas and depths to compile a dataset with the highest possible completeness with respect to the high end of the luminosity and the redshift ranges. We used the X-ray high-z sources selected in the CCLS, XMM-XXL, and eFEDS fields, described in the following. The Chandra COSMOS Legacy Survey (CCLS,  Civano et al. 2016 ) covers an area of 2. 2 deg 2 and includes Chandra observations of about 4. 6 Ms, reaching a depth of 2. 2 × 10 -16 erg cm -2 s -1 in the soft X-ray band (0. 5-2 keV). Marchesi et al. (2016a)  provided the optical and infrared identifications for the whole sample of 4016 X-ray sources in the CCLS field and obtained the photometric redshifts using the LePhare code  (Arnouts et al.
46 ± 0. 12, suggesting a very weak correlation. The linear fit to the data (using BCES) also supports this low correlation (y = 0. 0018 ± 0. 0005 × x -0. 0002 ± 0. 0013). We also noticed a difference in the SFR NORM values we calculated using the X-ray sources and the photometry available in the  Masoura et al. (2021)  study using our SED fitting grid compared to the SFR NORM values presented in the left panel of their Fig. 10. The trends and overall conclusions are the same, namely, SFR NORM increases with L X , low L X sources lie below the MS, while high L X sources are above the MS. However, the SFR NORM values estimated in  Masoura et al. (2021)  are higher by a factor of ∼1. 5 on average. This highlights the importance of taking into consideration the different methods or grids that are applied in different works to estimate the host galaxy properties when we compare results from different studies.
1994; Aird et al. 2018) , we calculated the k Bol values that are dependent on the X-ray luminosities  (Lusso et al. 2012; Duras et al. 2020 ) using the functional form defined in Eq. (1) of  Duras et al. (2020)  and adopting the coefficients a, b, and c from their Table  1. Regarding the black-hole to stellar mass ratio A, we used Eq. (  11 ) of  Kormendy & Ho (2013) , which takes into account the stellar-mass dependence. Figure  11  presents the λ sBHAR distribution of our high-z sample. The median value for the whole sample is logλ sBHAR = -0. 51 ± 0. 07. This corresponds roughly to Eddington ratio values of λ Edd ∼ 0. 3. Table  4  presents the median values for the individual datasets and their uncertainties derived using a bootstrap resampling method. In Fig. 12 , we present the distribution of the high-z sources in the SFR-M * plane colour-coded with the corresponding λ sBHAR.
Summary and conclusions In this work, we built a sample of high-redshift AGNs selected in fields with different observed areas and depths (CCLS, XMM-XXL, and eFEDS) to examine the relation between the AGN power and the host galaxy properties. We constructed their SEDs using data from X-rays up to FIR and derived the physical properties of their host galaxies using the X-CIGALE SED fitting algorithm. After applying several quality and reliability selection criteria, we obtained an X-ray luminous (L 2-10 keV >∼ 10 44 erg s -1 ) sample of 89 high-z sources (z 3. 5) with reliable estimated values of the SFR and the M * of the host galaxies. 55% have secure spectroscopic redshifts. Our main results can be summarised as follows: 1. Using the derived host galaxy properties, we found that our luminous high-z X-ray AGNs live in galaxies with median M * = 5.
6 × 10 10 M and median SFR * ≈ 240 M yr -1. Our results are in agreement with previous studies at high redshifts. 2. We compared the location of our X-ray sources in the SFR-M* plane with that of star-forming MS galaxies adopting the  Schreiber et al. (2015)  relation. We found that ∼89% of our high-z AGNs lie inside (47. 2%) or above the MS (41. 6%), indicating higher star forming activity compared to normal galaxies. In particular, by estimating the SFR NORM parameter, we found that our high-z AGNs have enhanced SFR compared to MS star-forming galaxies by a factor of ∼1. 8 (mean log S FR NORM = 0. 22), in the luminosity regime probed by our dataset (L 2-10 keV ≈ 10 44-46 erg s -1 ). 3. Our estimated SFR NORM values are in agreement with previous studies at lower redshifts. This is evidence that SFR NORM does not evolve with redshift, confirming the findings of previous studies and extending it up to the higher redshifts probed by our sample.
4. To examine the role of stellar mass in the SFR of our X-ray sample, we used the specific black hole accretion rate, which is a tracer of the Eddington ratio. The bulk of AGNs that lie inside or above the MS of star-forming galaxies have higher specific accretion rates compared to sources below the MS. 5. In agreement with previous studies at lower redshifts  (Mountrichas et al. 2021a (Mountrichas et al. , 2022b)) , our results indicate that in the most massive systems (log (M * /M ) > 10 11. 5-12 ), SFR NORM remains roughly constant. We combined data from three different fields and compiled a luminous X-ray sample at very high redshift (z ≥ 3. 5). Although the size of the sample is not large, we reached important conclusions regarding the SFRs of AGNs in the early Universe and how they compare with those of MS galaxies.
Increasing the number of AGNs with available multi-wavelength photometry at high redshifts will allow us to examine, in a statistically significant manner, whether SFR NORM increases with L X and whether our conclusions hold for the most massive systems. Compilation of a large non-AGN galaxy sample at similar redshifts and with similar available photometry will enable us to directly compare the SFRs of AGNs with those of non-AGN systems, minimizing the effect of any systematic effects. the examination of the manuscript. GM acknowledges support by the Agencia Estatal de Investigación, Unidad de Excelencia María de Maeztu, ref. MDM-2017-0765. IG acknowledges financial support by the European Union's Horizon 2020 programme "XMM2ATHENA" under grant agreement No 101004168. The research leading to these results has received funding (EP and IG) from the European Union's Horizon 2020 Programme under the AHEAD2020 project (grant agreement n.
871158). XXL is an international project based around an XMM Very Large Programme surveying two 25 deg 2 extragalactic fields at a depth of ∼6 × 10 -15 erg s -1 cm -2 in the [0. 5-2] keV band for point like sources. The XXL website is http://irfu. cea. fr/xxl. Multi-band information and spectroscopic follow-up of the X-ray sources are obtained through a number of survey programmes, summarised at http://xxlmultiwave. pbworks. com/. This research made use of Astropy, a community-developed core Python package for Astronomy (http://www. astropy. org, Astropy Collaboration 2018). This publication made use of TOPCAT  (Taylor et al. 2005)  for table manipulations. The plots in this publication were produced using Matplotlib, a Python library for publication quality graphics (Hunter 2007). Based on observations obtained with XMM-Newton, an ESA science mission with instruments and contributions directly funded by ESA member states and NASA.