dataesr/scientific-paragraphs-categorization huggingface dataset:
Correlation networks Functional complex networks created by considering the correlation between the dynamics of pairs of nodes. Cross-industry standard process for data mining CRISP-DM stands for cross-industry standard process for data mining, an industrial group that proposed a methodology for organizing the data analysis process in six standard steps. 169, 170 Since then, the term CRISP-DM has been used to indicate both the group itself and the methodology. The six steps are: Business (or Problem) understanding: initial understanding of the objectives and requirements of the analysis to be performed; these are expressed as a data mining problem, and should include a preliminary roadmap or execution plan. Data understanding: In this second phase, data are collected and a first analysis is executed, to familiarize with them; identify quality problems; discover initial insights, and formulate initial hypotheses; and identify relevant data subsets.
Data analysis software With the widespread adoption of data-based solutions in many real-world scenarios, it is not surprising to find a large number of analytic solutions, spanning from cloud pipelines to commercial and freeware software, and both stemming from research activities and having a commercial nature. The most important are listed here, classified according to their underlying structure in cloud, noncloud, and hybrid tools. Noncloud (or local) solutions. Commercial and freeware software tools for data analysis are designed to work on a local (or at least, noncloud) environment. In this category, one can find: KNIME  175  (www. knime. com); SPSS Modeller 176 (www. ibm. com/products/spssmodeler); RapidMiner  177  (rapidminer. com); Alteryx (www. alteryx. com). These software platforms usually have a broad focus, allowing to process any (or most) kind of data; and they allow to construct models by connecting modules in a graphical interface.
One limitation of a dynamic and fast development of Digital Health lies in local regulations that have the objective of keeping health-related data and information confidential and safe, and allowing their use in ways ensuring data availability and integrity only for relevant individuals (patients and their related one when relevant, professional, and specific organizations). Digital Health is a full component of the Systems Medicine paradigm by allowing a dynamic view of individuals from the nano-level (e. g. , gene expression as a response to an environmental change) to the mega-level (e. g. , population interactions/reactionsdiscussions-on social networks as a response to an epidemic announcement).
The complex processes include Machineto-Machine and Human-to-Machine Interaction. Relevant enabling technologies are related to Heterogenous objects, Heterogenous distributed systems (P2P, Wireless Sensor Networks, Cloud Computing), and Complex Systems of Systems. The IoT as a complex systems is not a simple set of subsystems and involves data and energy transformation, interaction, interoperability, feed-back and feed-forward structures, self-organization, and self-management. 262 n important development of IoT with applications in medicine is referred to as internet of medical things (IoMT). The IoMT can be described as an internetbased environment connecting medical devices and services. 4] [265]  In cancer treatment studies, blood pressure monitoring bracelets and tracking apps have been used to gather relevant information.
Continuous glucose monitor can be connected in an IoT environment to transmit data to mobile devices, thus facilitating the analysis of blood glucose levels. A Bluetooth-enabled coagulation system has been used in connection to the IoT environment to help patients become aware of potential blood clots and transmit results to health care providers. A wearable smart asthma monitor can detect symptoms related to asthma attacks and connected to an IoT environment it can track and detect the inhaler.
271, 272 xamples of the use of the LB method in medicine include the modeling of the motion of endolymph through the semicircular canals of the inner ear  273, 274  ; and the analysis of the numerical and experimental transport of LDLs through arterial walls. 199 Opensource software implementing LB methods are also available; see, for instance, https://www. openlb. net and https://palabos. unige. ch (Fig. 7 ).
Machine learning Machine learning is the science of using computers to discover new information from observations. 275, 276 ere are several families of machine-learning methods: supervised learning, unsupervised learning, and semi-supervised learning. The choice of the strategy depends on the nature of the used data. A large and complex database is commonly required to develop a machine-learning model. In the system medicine field, bio-marker extraction or human genome classification a typical example of a machine-learning model. For further details, see also data mining, CRISP-DM, deep learning.
Moreover, endusers are a crucial component of the overall system in Medical Informatics. For efficiency reasons, researchers in the field of Medical Informatics have to continuously monitor the changes in different spheres such as the social, economic, ethical, and educational, and update their models in accordance to these changes. In recent years, there has been an important and growing trend of applying algorithms and knowhow from the fields of BI and in Medical Informatics, for example, data and text mining, analysis, and information and knowledge management-see the Clinical Decision Support Systems section. From the integrative perspective of systems medicine, Medical Informatics investigates and delivers end-to-end frameworks supporting complex medical decisions, driven by evidence-based medicine for continuously improving health and disease management at the individual and populations levels.
283 One of the most critical parts of research done in Medical Informatics considers ethical and legal regulations and constraints in the technological side of medical field. 284 As new means of measuring, communicating, and managing patients emerge, there is a need to continuously monitor and update the requirements for ensuring security, that is, keeping confidentiality, integrity, and availability of health and medical-sensitive data. metaboAnalyst Part of the same family of websites including networ-kAnalyst and microbiomeAnalyst, this website provides a visual analytics platform for meta-analysis of metabolomics data (www. metaboanalyst. ca). 285.
The major aim of biomolecular simulations is to predict a certain property of the biomolecular system. The third type of granularity is in the depiction of such molecular properties. Biomolecular simulations produce trajectories-thousands of snapshots of thousands of atoms. These pieces of big data can be analyzed to extract relevant low-dimensional properties of the systems. Such properties can be then used to build thermodynamic and kinetical models of the simulated system. The last granularity is the computational granularity. As already mentioned, biomolecular simulations are computationally expensive. Most software used in biomolecular simulations has been developed to run in parallel on multiple cores of a CPU (multithreading) and multiple CPUs and nodes connected by Message Passing Interface.
By integrating both approaches, the idea is to find a compromise between accuracy and efficiency (Fig. 9 ). 308 twork Analysis Software NetworkX. 309  Python library used for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks. Thi  allows the creation of networks with different algorithms, evaluation of a large set of standard metrics, and finally display the results in an easily understood way. Freeware. Available at networkx. github. io Cytoscape. 310, 311  Software specialized on the representation of networks, with some additional tools for the integration of biological data. It also provides some basic network analysis capabilities. Freeware. Available at www. cytoscape. org Gephi. 312 Interactive visualisation and exploration platform.
Freeware. Available at gephi. github. io Pajek. 312 Software for representing complex networks, with some basic analysis capabilities. Freeware. Available at mrvar. fdv. uni-lj. si/pajek/ VisANT. 314 Software for the visual study of metabolic networks and pathways. Freeware. Available at visant. bu. edu IBM Ò i2 Analyst's Notebook. Software for the integration of social data and network analysis. Commercial. Information at www-03. ibm. com/ software/products/en/analysts-notebook SAS Ò Social Network Analysis. Software for the analysis of social networks. Commercial. Information at support. sas. com/software/products/sna/ index. html networkAnalyst Part of the same family of websites, including metab-oAnalyst and microbiomeAnalyst, this website provides a visual analytics platform for meta-analysis of differentially expressed genes or proteins (www.
The network can be downloaded in a Cytoscapecompatible SIF-format, but the standard routine is to visualize it within the web platform in an adjustable manner, including up-or downregulation of expression levels and different layouts, which can be saved in SVG-format. Moreover, and most importantly, the network can then be statistically compared with different databases such as KEGG, Reactome, gene ontologies, or TF motifs to obtain functional enrichment values. A module explorer can be applied to extract subnetworks with statistically elevated links, and these can be further analyzed for functional gene enrichments. In case that the differential expression is computed on the NetworkAnalyst platform, gene clustering can be performed comprising heatmaps, principalcomponent analysis, or t-distributed stochastic neighbor embedding.
The most simple solution involves the calculation of a Z-score, which indicates how many standard deviations the observed metric is from the (null model's) expected value. 202 idia Clara Nvidia Clara is a computational platform that gathers Compute Unified Device Architecture (CUDA) accelerated tools for medical imaging and genomics. The Software Development Kit (SDK) provides libraries for computing, visualization, and AI. The SDK allows the users to deploy their applications in any GPU platform they have access to. Within this platform, Nvidia Clara Medical Imaging provides tools for data annotation, training of AI models, and deployment in the case of medical imaging applications (e. g. , computerized to-mography, MRI, ultrasound, X-ray, and mammography). Adapting one of the included in the SDK pretrained AI models with transfer learning accelerates the AI modeling, as less time and training data are used.
In systems medicine, we can imagine that large macromolecules (genes, mRNAs, proteins including enzymes and TFs, etc. ) are objects. The signaling pathways represent links or information that is transferred through connections between these objects. Nowadays, Modelica is used as the most popular programming language for object-orienting modeling. The benefit of Modelica is that the users can create their own libraries. BioChem has been designed as a library for metabolic pathways  322  that describes enzymatic reactions in different biochemical pathways. SysBio library  323  was initially used to construct the SteatoNet model with multilayered regulation, including the transformation of genes to proteins and the transcriptional regulation. 324 In addition, SteatoNet describes multiple tissues, that is, the liver and adipose tissue and their connections through the blood.
390 In general, the major challenge with statistical networks is to assess and minimize statistical artefacts that may hamper network-derived biological conclusion-drawing. 391 pport vector machine Binary linear classifiers are based on the identification of hyperplanes in the feature space, dividing the training instances in two groups according to the training label. The model is trained by first constructing a feature space, that is, a hyper-space defined by the features available in the data set, which must always be numerical. Records are mapped into this space, and the best linear separation between them is then calculated. The best separation is achieved by the hyperplane that has the largest distance to the nearest training-data point of any class, as this minimizes the error. Modified versions of SVMs have been developed to tackle different problems, including regression problems,  392  or the use of different kernels (i.
Structural covariance networks A technique is used to reconstruct complex networks representations of brain cortical regions. The network is defined such that nodes represent brain regions, and link the Pearson's correlation of CT or volume between pairs of regions, as yielded by magnetic resonance data (MRI). 418, 419 Structural covariance between regions can be used to construct the so-called structural covariance networks. Several studies have been conducted in which structural covariance networks have been analyzed in healthy subjects,  420, 421  and in groups of patients with disorders such as autism, attention deficit hyperactivity disorder, schizophrenia, or Alzheimer's disease,  [422] [423] [424] [425]  or to assess the differences between gifted children and controls. 426 Since the SCN is at the group level, (structural) connectivity parameters are also at the group level and a permutation test will be needed to infer differences between measures.
To determine the system parameters, we performed a global analysis of our six CHEOPS light curves together with other available photometric and RV data. This includes first a custom TESS light curve, which we extracted from the target pixel files following ref. 2  , to conduct a careful treatment of instrumental systematics (see Methods and Supplementary Figure  4 ). We also included 246 previously published  1, 2  RV measurements obtained with HARPS between May 2004 and August 2017 (see Methods and Supplementary Figure  6 ). Since transit and RV data only yield the parameters of the planets relative to those of their star, we performed a detailed characterization of the host star (see Methods), whose main derived parameters are given in Table  1. We carried out our global data analysis using the juliet package  14  , which allows the joint modeling of transits and radial velocities simultaneously with signals of non-planetary origin (instrumental systematics, stellar variability) to ensure a full propagation of the uncertainties on the derived system parameters.
A key feature of juliet is that it uses nested sampling algorithms  15  to explore the parameter space, which also allows one to perform model comparison via Bayesian evidences. We first analysed each dataset individually, to explore for each of them a large range of correlated noise models and select the best one, based on Bayesian evidence (see Methods). During this process, we detected in the CHEOPS light curves, besides typical instrumental noise phased with the spacecraft roll angle, some extra higherfrequency correlated noise which we attribute to stellar granulation and oscillations. We modeled this stellar noise using a Gaussian Process (see Methods) and studied it in detail in an independent analysis (see below). We then performed several global analyses assuming circular or eccentric orbits for some or all the planets (see Methods).
Following a similar approach to the one used by ref. 2  , we extracted our own custom light curve from the TESS pixel files, in order to conduct a careful treatment of spacecraft systematics. Using the lightkurve python package  32  , we retrieved the calibrated 2-minute target pixel image files from the Mikulski Archive for Space Telescopes (https://archive. stsci. edu) using the default quality bitmask. We extracted light curves for twenty different apertures centered on the target, which were then background-corrected by subtracting the sky contribution determined using a custom background mask (Supplementary Figure  3 ). We chose the photometric aperture minimizing the 1-hour Combined Differential Photometric Precision (CDPP) metric  33  , in this case a circular aperture with a radius of 4.
stsci. edu/missions/ tess/engineering/). For each of the three vector components, we computed the means and standard deviations of the measurements within each 2-minute science image. We also retrieved the various cotrending basis vectors (CBVs, https://archive. stsci. edu/tess/bulk_ downloads/bulk_downloads_cbv. html) computed by the Pre-Search Data Conditioning module  35, 36  of the TESS Science Processing Operations Center pipeline  37. We then decorrelated our light curve against the quaternion and CBV time series using Bayesian linear regression, while masking the in-transit points in the fit. We tested many different combinations of regressors and kept the one minimizing the Bayesian Information Criterion  38  , which notably comprised the first and second-order quaternion time series as well as the high-frequency (band 3) CBVs.
Archival radial velocities In our global analysis, we also included 246 previously published  1, 2  RV measurements that were obtained between 27 May 2004 and 4 August 2017 with the HARPS spectrograph on ESO 3. 6m telescope (La Silla, Chile). Among these, the last six measurements were acquired after an instrument upgrade and we thus treated them as an independent dataset in our analysis. These RV data are shown in the upper panel of Supplementary Figure  6. For details about the instrument, observations, and data reduction, see ref. 1, 2  and references therein. Ref. 2  also published 169 RV measurements obtained with the UCLES spectrograph on the 3. 9-m Anglo-Australian Telescope (Siding Spring, Australia), as well as 43 RV measurements obtained with the HIRES spectrograph on the 10-m Keck I telescope (Mauna Kea, Hawaii).
Two sets of stellar parameters were computed with different stellar evolution codes, respectively using the CLES code  53  and the PARSEC stellar tracks and isochrones  54. In the first case, with CLES, the optimal stellar parameters were determined following a Levenberg-Marquardt local minimisation scheme. The CLES stellar models adopt the solar chemical mixture  55  , OPAL opacities  56  , FreeEOS equation of state  57  , and nuclear reactions from ref. 58. In the second case, with PARSEC, output parameters were inferred using the Isochrone placement interpolation scheme  59, 60. The PARSEC models adopt the solar-scaled composition given by ref. 61  , nuclear reaction rates considering the JINA REACLIB database  62  , OP  63  and AESOPUS  64  opacities, and FreeEOS equation of state  57.
Global data analysis and derivation of the system parameters To determine the system parameters, we performed a combined analysis of the CHEOPS and TESS transit photometry, together with the HARPS radial velocity data. For this purpose, we used the publicly available juliet library 14 (https://github. com/nespinoza/juliet), which is built over batman 66 for the modeling of transits and radvel 67 for radial velocities. A key feature of juliet is that it uses nested sampling algorithms (in this work, dynesty  15  ) to explore the parameter space, which also allows one to perform model comparison via Bayesian evidences. For this analysis, we assumed that the CHEOPS single transit event was caused by ν 2 Lupi d (see Section "On the origin of the CHEOPS single transit event" below). juliet is very versatile and allows for a variety of parametrizations.
As for the RVs, we simply modeled them using a sum of three Keplerians (to account for the three planets), with a different zero point for each dataset (pre-upgrade and post-upgrade). No correlated noise model was necessary in this case. Finally, we also fitted for each of our photometric and RV datasets an extra jitter term which was added quadratically to the error bars of the data points, to account for any underestimation of the uncertainties or any excess noise not captured by our modeling. Altogether, this yields a total of 70 free model parameters, of which 26 describe the planetary signals and the other 44 are nuisance parameters. These parameters are listed together with their priors in Supplementary Table  3. To aid convergence, we placed normal priors on the nuisance parameters, based on the posterior distributions returned by our individual analyses.
On the refinement of the system parameters To assess the improvement brought by our CHEOPS data for planets b and c compared to the previous TESS measurements 2 , we also analysed both datasets individually, in a homogeneous way, using the same methods and priors as above. For this exercise, we assumed eccentric orbits for the planets to allow a direct comparison with the results of ref. 2. We first did a combined analysis of the TESS light curve and the radial velocities. The results of this analysis are compared to those of ref. 2  in Supplementary Table  5  (second and third columns) for some key parameters. Our results are consistent, but our uncertainties are significantly larger, e. g. by factors of ∼1. 7 and ∼2. 1 for the planet-to-star radii ratios of planet b and c, respectively. These larger uncertainties are likely related to our modeling of the residual systematics with a GP which is fitted simultaneously with the transits, while ref.
2  performed a full detrending of the TESS light curve previously to the transit fitting. We consider our approach to be more robust, as it accounts for the possible covariances between the nuisance and transit parameters, thus ensuring a proper propagation of the uncertainties on the derived transit parameters. We then performed a combined analysis of our six CHEOPS visits together with the radial velocities. The results are reported in the fourth column of Supplementary Table  5. They are in good agreement with those of our TESS data analysis and also provide tighter constraints on the transit parameters. The planet-to-star radii ratios are measured with relative precisions of 2. 8% and 2. 1% for planets b and c, respectively, a factor ∼2 more precise than the measurements returned by our analysis of the TESS data.
This significant refinement stems from both the higher photometric precision of CHEOPS and the larger number of transits observed (four transits of planet b observed with CHEOPS versus two with TESS, three transits of planet c observed with CHEOPS versus only one with TESS). Of course, the best constraints are obtained when combining all the data (TESS+CHEOPS+RVs) together (last column of Supplementary Table  5 ). Thanks to the longer temporal baseline, the transit ephemerides are significantly refined, thus enabling efficient follow-up observations. The mid-transit times of planets b and c in May 2021 (middle of the next observing window) have now uncertainties of only 10. 1 and 2. 6 minutes, respectively. This is a major improvement when compared with the previous respective uncertainties of 88 and 106 minutes obtained when using the ephemerides of ref.
To compute the expected TTV signals, we simulated the system with a N-body integrator. We used the parameters in Table  1  to integrate the orbits of the three planets for 3. 5 years (i. e. , the nominal duration of the CHEOPS mission) with the TRADES  [82] [83] [84]  program, that allows us to compute the simulated transit times for each planet. We then extracted the semi-amplitude of the O -C diagram of each planet, assuming the simulated transit times as O and subtracting the transit times C calculated from the linear ephemeris based on the orbital period P and mid-transit time T 0 in Table  1. We found that the expected TTV semi-amplitudes are rather small: about 20 s for planets b and d, and about 40 s for planet c. To explore our current dataset for possible TTVs, we ran another global analysis with juliet, additionally including a free TTV offset parameter for every transit of planet b (6 transits) and planet c (4 transits), while keeping their orbital period P and mid-transit time T 0 fixed to the values given in Table  1.