dataesr/scientific-paragraphs-categorization huggingface dataset:
Objectives: The aim of our current study was to analyze whether the use of important measures of methodological quality and reporting of randomized clinical trials published in the field of cardiovascular disease research have changed over time. Further aim was to investigate whether there was an improvement over time in the ability of these trials to provide a good estimate of the true intervention effect. Methods: We conducted two searches in the Cochrane Central Register of Controlled Trials (CENTAL) database to identify cardiovascular clinical research trials published in either 2012 or 2017. Randomized clinical trials (RCTs) trials in cardiovascular disease research with adult participants were eligible to be included. We randomly selected 250 RCTs for both publication year 2012 and 2017.
Methods We conducted two searches to identify cardiovascular clinical research trials published in either 2012 or 2017. We searched the Cochrane Central Register of Controlled Trials (CENTAL) database, as this is the most comprehensive resource available containing randomized clinical trials. We used the same search strategy for both years, containing subject headings and key words related to adults (aged >18 years) and cardiovascular diseases, restricted to the years 2012 or 2017. The first author (OB) searched CENTRAL and screening trials for eligibility. We included studies which were published in 2012 in English language journals, where the investigated intervention was related to cardiovascular practice and where participants 18 years or older were included. Our search resulted in 2566 trials.
All identified records were exported to Excel, where they were randomly ordered with the following method: we used the RAND function to assign a number between 0 and 1 to each record. In a subsequent step we were reordering trials from the smallest to the highest number. We included the first 250 (about 10%) eligible randomized clinical trials for both year 2012 and 2017. We used a data extraction tool which was developed for assessing methodological quality of RCTs in child health research  (17). Two authors (OB, OF) independently extracted data for each study included. We discussed all unclear decisions until consensus was established. For each cardiovascular disease study, we identified information about journal type (e. g. , general or specialty medical journal, general or specialty cardiovascular journal), corresponding author's country , study type , study design, intervention type, type of control, number of study centers, study sample, primary diagnostic category in the study using the ICD-10 classification system, presence of data monitoring committee, type of primary outcome, outcome results, and trial registration.
6% compared to 18%), (p<0. 05), and developing (8. 4% compared to 1. 2%) and transitional economy countries (5. 2% compared to 3. 2%) were more often concerned (p<0. 001). In 2017 included trials were more often funded by pharmaceutical company or industry (p<0. 001). (20)  Multicenter trials were defined as trials with two or more administratively distinct study centers. Multinational applied to the countries from which patients were enrolled. Economic status of the country was defined based on Panagiotou et al, 2013  ( 21 )  Table  2  shows changes in important measures of methodological quality and reporting. As compared to 2012 we saw an improvement in 2017 in the reporting of the presence of a data monitoring committee (42. 0% compared to 34. 4%; p<0. 001). As compared to 2012 there was a positive tendency of registering trials in trial registries in 2017; and among clinical trial registries the clinicaltrials.
gov database had increased popularity (registration rate in clinicaltrials. gov was: 78. 4% compared to 68. 9%; p=0. 03). On the other hand, significantly fewer RCTs reported sample size calculation (60. 4% compared to 90. 4%; p<0. 001) in 2017 as compared to 2012. Although, fewer RCTs specified plan to collect adverse effects (48. 4% compared to 74%) (p<0. 001) in 2017, they reported harms more often (68% compared to. 52%; p<0. 001) in 2017. When we investigated reporting of results, we observed that the number of RCTs with statistically significant results of the primary outcome was lower in the 2017 sample (69. 2% compared to 78. 8%; p<0. 01). Further, there were more publications with neutral conclusions in 2017 (18. 4% compared to 7. 2%; p<0. 01). There were no statistically significant differences between 2012 and 2017 in the number of intentions to treat analyses, in the type of outcomes (as most outcomes were objective), or specific types of primary outcomes.
Strengths and Weaknesses of the Study For both investigated publication years we selected our sample randomly from Cochrane CENTRAL as the most comprehensive resource of RCTs. The samples covered areas of the prevention, diagnosis and treatment of cardiovascular diseases, including acute myocardial infarction, heart failure, arrhythmia, coronary revascularization, and chronic coronary artery disease. Most of our trials were registered in clinical trial registries, so essential trial details were double checked in both the full text article and the registry. We used the most accurate tool for risk of bias (methodological quality) assessment of includes RCTs. Two independent reviewers performed data extraction and RoB assessment, discrepancies always resolved by discussion. This study has also some limitations.
Several reviews on GRN inference based on different data sources have been published  [27] [28] [29] [30] , many of them reviewing and performing benchmarking analysis on their performance based on simulated or real case datasets  [31] [32] [33] [34]. While almost all of them consider methods based on steady-state bulk or single-cell datasets, here, we will focus particularly on pseudo/time-series transcriptomicsbased inference algorithms, including some methods exploiting the pseudotemporal formalism applied in single-cell datasets, but mostly focusing on inference based on bulk RNAseq time courses. We aim to give researchers a roadmap from the simplification of biological processes in the form of regulatory networks, to an algorithmic review of GRN inference methods based on time-series transcriptomics.
On the other hand, the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, an initiative to benchmark multiple inference methods, indicated that no single method can perform the best in every possible dataset and across different settings. Instead, a high-confidence consensus network, inferred from different methods in a process referred to as Wisdom of Crowds  [14] , is the most accurate, while also providing an estimation of robustness and performance. For this reason, instead of using a single inference method, other researchers have been developing computational tools combining several GRN inference methods  [56] , providing a ranking of the methods according to their performance. The increasing demand for improving the inference accuracy in real datasets has warranted the adaptation of some steady-state inference methods to consider time-series data.
Inference from time-series Inference of gene networks from time-series data provides a more complete picture of the system than using just steady-state data, as the biological system is intrinsically complex and biological function relies on coordinated sets of genes whose expression evolves over time. In general, inference methods based on time-series transcriptomics use a dataset containing a list of genes with their expression measured at several time points. Let us define the dataset D TS , as a matrix with dimensions N×P, where N is the number of genes and P is the number of time points at which their expression is measured: D TS ¼ Xðt 1 Þ; Xðt 2 Þ;. ; Xðt p Þ ð1Þ where X(t p ), p = 1,2,. ,P is a vector of N genes with their expression at time t p. The main goal of inference methods is to assign a weight w j,i �0,i,j = 1,2,.
,N to any putative interaction between gene i (target) and j (source), representing a regulatory interaction in the biological system. To this end, different inference methods use various regression tools to model the expression of a gene as a function of its regulators. It is important to note that, usually, the time-series inference methods combine time-series with steady-state datasets, in order to increase the accuracy and predictive power of the inferred network. Independent of the method chosen, the goal is to reconstruct the GRN that would produce the observed profile of expression across time, in the form of a directed graph, in which each edge is associated with its characteristic weight (Fig  2 ).
Mutual information-based models In these methods, the putative interaction between 2 genes is inferred by estimating the mutual information (MI) between the expression X i of gene i at time point t p and the the expressions X j of gene j at the m th previous time points: The process usually starts with processing the time-series datasets and identifying the time-points that are relevant to describe the biological process (1). As a second step, data formatting, normalization and gene filtering and/or binarization might be necessary (2), depending on the inference method to be used (3). The inferred network consists of a weighted directed GRN (4). Model validation (  5 ) is then necessary to quantify the method performance in inferring the GRN when compared to gold-standard datasets, evidence from literature or prior biological knowledge.
Dynamical Bayesian models Dynamical Bayesian network (DBN) model GRN inference methods from time-series datasets  [59, 60]  come as an extension of simple Bayesian network (BN) models applied to steady-state datasets, in order to overcome the limitation of prohibiting the existence of feedback loops in the network. Given a GRN defined as a set of vertices and edges G = (V,E), the method searches for any relationship between the expression of a target gene (defined as a random variable) and its regulators (defined as parent genes) by calculating a joint probability distribution. In a time-series experiment, consisting of N genes and P time points, the joint probability distribution of each gene is defined as: PðX 1 ðt 1 Þ; X 1 ðt 2 Þ;. ; X 1 ðt P Þ;. ; X N ðt 1 Þ; X N ðt 2 Þ;. ; X N ðt P ÞÞ ¼ PðXðt 1 ÞÞPðXðt 2 ÞjXðt 1 ÞÞ.
; N p ¼ 1; 2;. ; P ð4BÞ where α, β are coefficients and η(t p ) is the residual noise at time t p. Generalizing for N genes, gene j (j = 1,2,. ,N,j6 ¼i) is said to be causal for gene i if considering the expression of gene j in the previous time points significantly improves the prediction of the expression of gene i at the current time point. Notably, when referring to the "previous time points," several previous time points (t p -1, t p -2, etc. ) can be considered. Considering N genes in the dataset, the vector autoregressive (VAR) model is used to estimate the Granger causality over the entire list of genes, where a linear dependency between genes is being assumed  [63]. Several methods using Granger causality for GRN network inference have been developed, such as BETS  [64] , SWING  [65] , CGC-2SPR  [66] , among others.
Given a time-series dataset, the expression X i of gene i at time t p is given by a Boolean function of its regulators: X i ðt p Þ ¼ F B i ðX i;1 ðt p À 1Þ; X i;2 ðt p À 1Þ;. ; X i;k ðt p À 1ÞÞ ð5Þ where X i,k , k = 1,2,. ,N-1, k6 ¼i are the regulators of gene i, and F B i is a Boolean function describing the regulation of gene i by using the Boolean operators AND, OR, and NOT. In this way, the inference of any causal relationship is performed through a qualitative dynamical modeling of gene expression  [67, 68]. Compared to the other inference methods, BN inference methods offer the advantage of being parameter free and relatively easy to apply. On the other hand, they suffer from several drawbacks and limitations, arising from the discretization/ binarization process and the finite space explored for inferring the regulatory processes (limited number of possible BNs), and the qualitative description of expression  [69].
Consequently, ODE methods are limited to network inference on a small number of genes and are usually combined with other inference methods in order to reduce the computational complexity  [44, 45]. In principle, a causal relationship between 2 genes is considered if the interaction is described by a relatively high interacting coefficient. Despite the difficulty of applying them to real datasets, several methods using ODE models have been developed, including Inferelator  [76]  and TSNI  [77] , but they often display a relatively lower performance than alternative approaches, as reported in  [64].
Causal interactions are quantified by estimating the confidence levels of each j!i interaction, representing the weights w j,i. Importantly, in order to increase the accuracy of the inferred network, some tree-based methods have been adapted to use both timeseries and steady-state datasets. Some inference methods based on decision-tree algorithms can be highlighted for having particularly good performance: dynGENIE3  [24]  and Jump3  [79] , both being extensions of GENIE3  [46]  on time-series datasets.
Nonetheless, this representation helps implement the GRN inference algorithms in modeling the expression levels at a given (pseudo)time-point as a function of gene expression at the previous (pseudo)time-point (s). Accordingly, a subset of the inference methods require specific information about the pseudotemporal ordering of the cells (CellOracle  [91] , SCODE  [20] , SINCERITIES  [92] , SINGE  [93] , LEAP  [94] , SCRIBE  [95] , etc. ), having a significant difference in performance  when such information is not available. Other methods, like GENIE3  [46] , GRNBoost2  [96] , or PPCOR  [97] , do not require a temporal ordering of the cells as input and have relatively good performance when tested on some published curated models  [98]. However, the incomplete equivalence between bulk gene expression time courses and pseudo-time time-series implies that these 2 types of inference cannot always be performed by the same tools.
GRN validation A crucial and essential step in performing GRN inference is validation of the inferred network. Having a validation protocol that allows evaluation of the score of each proposed model is very important in order to choose the optimal inference procedure among the variety of existing algorithms. Despite advances in this direction, evaluating the effectiveness of the inference method remains an open challenge, mostly due to limitations in ground truth/gold standard datasets. A common approach to evaluate the accuracy of an inference method is to compare the inferred edges to TF-target interactions annotated in public databases, which can be of different kinds, depending on the type of information they contain  [30, 112]. Some databases will provide information on functional interactions, i.
e. , evidence that 2 genes/proteins, in this case a TF-target pair, can be related based on any type of link between them, evidenced by gene expression correlation, coevolution of the genes, comention in scientific abstracts, etc. These functional interactions can be found in databases such as String  [113] , Reactome (the functional interaction network [114], TRRUST  [115] , or RegNetwork  [116] , the latest selecting information from around 25 databases. Moreover, there are efforts to map more specific TFtarget interactions based on experimental evidence, either collecting results of ChIP-seq experiments that show in which target gene promoters the binding peak of the TF can be found or also combining these experimental results with a crosscheck of the presence of the TF binding motif in the peak regions.
Among the databases including these interactions, we mention ChEA  [117]  or ReMap  [118]. Additionally, other databases, generally used when GRN inference is performed on single-cell RNAseq datasets, the validation of inferred interactions is performed on data on the same or similar cell types. Databases providing such information include ENCODE [119], ESCAPE [120], or ChIP-Atlas  [121]. However, it is important to note that repositories that report experimental data contain reference data for a reduced number of interactions, limiting the validation to a subset of the inferred network. This implies that the interactions for which the reference data are absent are considered as not existing, raising important questions on the implication of literature biases or the potential to infer novel interactions and regulators.
Focusing on prior knowledge for validation might reduce our chances of identifying new regulatory pathways involved in the system under consideration, for which gold standard references with high scores are absent or sparse. Another possibility for network validation comes from using simulated data, which can be engineered to include several conditions and measurements  [14] -yet the extent of coverage of the inferred interactions remains limited to small network size. Other than validating the inferred network with a gold-standard one, another important point in GNR inference is network comparison between different inference methods, on the same dataset, thus providing an estimate of the "robustness of the inferred network. " Additionally, one may perform this benchmark analysis in order to choose the algorithm/method that best suits a given dataset.
For example, using single-cell ATAC-seq, patterns of gene expression can be combined with chromatin accessibility profiles, thus identifying cell subpopulations and groups of cells at different developmental stages  [124]. Consequently, several differential network analysis algorithms have been developed, such as DiffRank [125], dcanr R package [126], DiffK [127], DINA  [128] , to name a few. We refer the reader to [129] for a statistical perspective of DiNA and [130] for a DiNA algorithm comparison. In principle, these algorithms consist of combining the information from 2 main computational tasks: (i) differential expression analysis, which estimates the gene expression abundance for each gene in the GRN between the 2 conditions, and (ii) network expression analysis, which estimates the importance of each gene in the GRN, based on the topological properties of the network.
Applications Although inferring regulatory networks is a trending topic and despite the multitude of different algorithms available, GRN inference methods struggle to reach a high performance in realworld studies, on both bulk and single-cell RNA-seq data, as reported in  [42, 131]. Therefore, their application to biologically relevant datasets remains limited. Recent effort has been focused on combining information from multiple data sources to improve predictions  [132, 133] , where gene expression data are combined with DNA methylation, copy number variation, genome-wide binding data, or CHIP-seq data. As a consequence, many inference methods introduced above combine multiple data sources to complement GRN inference and provide more insightful results on the regulatory processes. For example, in SCENIC  [25] , results of a GRN analysis are combined with TF binding motif information from RcisTarget to identify a subset of high-confidence interactions.
Other applications have been focusing on cell differentiation and development  [25, 103] , by integrating different layers of information in GRN inference. For example, in [138], time-series of RNAseq data is combined with ATAC-seq data to derive dynamic gene regulatory networks for human myeloid differentiation, specifically promyelocytes differentiating into macrophages, neutrophils, monocytes, and monocyte-derived macrophages. In Bocci and colleagues [139], single-cell transcriptomics is reinforced with mRNA splicing analysis to identify the key molecular drivers leading to different final states when starting from a common initial state during pancreas endocrinogenesis and epithelial/mesenchymal state transition. In another application, Thorne [140] used public time-series datasets from recount2 database  [141]  to infer the GRN of neural progenitor cell differentiation.
Performing structural analysis on the inferred GRN, they identified key genes like CDON and MCUR1, which were experimentally observed to influence neuronal differentiation. In another approach, Kamimoto and colleagues  [91]  used single-cell RNAseq and ATACseq datasets to infer cell state-specific GRNs that emerge during the differentiation process of fibroblasts. In this way, analyzing the changes in the GRNs during cell reprogramming or development can help understanding how the TF interactions regulate and define cell identity. In cell fate decision studies, Fleck and colleagues [101] used multiomics datasets, including RNAseq and ATACseq combined with transcription factor binding sites analysis, to infer a GRN describing brain organoid development, leading to the identification of the GLI3 transcription factor as a key regulator of cell fate.
Similar studies are presented in  [142] [143] [144]. In other efforts, data-driven GRNs have been used to build molecular disease maps  [145, 146] , phenotypic characterization of a cell in a given microenvironment  [147, 148] , identifying predictive or prognostic biomarkers  [64] , performing extensive studies on performance of the available methods on different datasets/conditions  [22, 124, 149] , and many more.
Purely data-driven GRN inference remains an open challenge, especially in single-cell RNAseq. Recent promising research addresses the task of network inference in the presence of incomplete multiomics datasets, resulting in the development of advanced computational methods  [158]. We speculate that perhaps the information provided by transcriptomics data, even in single cell, is not sufficient to cover the complex cellular processes giving rise to phenotypes and that the inherited concept that coexpression patterns might reveal putative gene interactions might not be universally applicable. For example, due to posttranscriptional and posttranslation processes, a highly expressed mRNA may not lead to a highly abundant functional protein, and vice versa -a synthesized protein does not necessarily possess the necessary activation state or conformation or localization to affect its downstream targets.
Therefore, there is a necessity to produce multiple types of 'omics data, consider different biological entities and levels of regulation, and the challenge in performing GNR inference will consist of integrating the data effectively. Additional limitations in the performance of GRN inference methods can come from other -often ignored-sources. For example, gathering experimental samples from various patients can suppress the variability, since patients have individual histories, immune system, or genetic profiles. Experimental protocols can present restrictions as well, such as limited measurements of expression (transcription, degradation, sequencing capture, etc. ), heterogeneity in bulk datasets, or incompleteness in single-cell RNAseq transcriptomics. Another challenge in data-driven GRN inference is result interpretability and the difficulty in dealing with the high complexity of the resulting networks increasing with their size.
One of the most difficult challenges in performing data-driven GRN inference is validating the results and estimating the method performance in real case studies. Benchmarking the inference methods with simulated datasets from a prior knowledge network (PKN) and a small list of genes can be an efficient way to estimate the methods' performance. However, this is far from real biological systems, in which the actual regulatory mechanisms are mostly unknown and-in some cases-experimentally unexplored. Unfortunately, using public databases (usually built on bulk-RNAseq) for validating the inferred GRNs can be limited to smallsize GRNs and narrowed around the highly studied regulatory pathways. Consequently, many approaches can lead to biased conclusions and a real difficulty in identifying novel regulatory pathways that might play an important role in the system under study.