dataesr/scientific-paragraphs-categorization huggingface dataset:
PLOS PATHOGENS We then assessed the Pseudo sce and 70int sce clones. In both cell lines, all the clones had retained ESAG1 and VSG2 but lost the RFP:PAC (Figs 2D and 2E and S3 and S4), apart from the single 70int sce puromycin resistant clone (clone 10) that had retained the RFP:PAC cassette as expected (Figs  2E  and S4 ). These results agree with the immunofluorescence showing VSG2 on the surface of all the survivors (Fig  2B ). In VSG up cell line, all the clones had lost VSG2, 18 had retained the Pseudo gene and 19 had retained ESAG1 (Figs 2F and S5) which agrees with previously published data  [23, 37].
9; Fig 3A  and 3B ) and PCR assays revealed that the cells had lost the RFP:PAC cassette but retained VSG2 and ESAG1, as has the parental 70int sce cell line, this suggests that repair at this position is MRE11-independent (S7B Fig). Taking all the data into account, we assessed repair pathway choice in these cell lines. Both the Pseudo sce and 70int sce cells lines use MMEJ to repair at these positions, whereas in the VSG up cell line a DSB is repaired by homology-based repair, as has been previously reported  [13, 37].
In both the ESAG1 sce and Pseudo sce cell line, no product was detected at 24 hours suggesting I-SceI cleavage was complete (Fig  4A ). In the VSG up and 70int sce cell lines, the SceR site was cleaved in more than 60% of the cells after 24 hours. Although we did not detect an increase in the amount of PCR product in the VSG up cell line as seen in Devlin, et al  [25] , we do detect a slight increase between 9-12 hours (Fig 4A, black line) and, the data are broadly consistent between the two studies. We next looked at VSG2 expression by RT-qPCR  [48]. RNA was extracted at 6-hour intervals following I-SceI induction, and as anticipated, VSG2 expression decreased over the time course in the VSG up cell line, closely matching that of I-SceI cleavage, with a 50% reduction in VSG2 expression at 18 hours (Fig 4A  and 4B ).
In the three positions tested, we do note that all trigger the DNA damage response as indicated by an increase in γH2A foci, and cells in stalled in G2 position in the cell cycle, but the response is dampened in the 70int sce cell line. Our data also suggests that the majority of repair in the 70-bp repeats or upstream happens via RAD51 and MRE11 independent MMEJ. In contrast a DSB proximal to the VSG is repaired by homology-based mechanisms as previously shown  [37]. In mammalian cells, MRE11 is critical MMEJ initiation  [64] , but both in Trypanosomes  [23]  and the related kinetoplast parasite Leishmania  [54] , MMEJ has been shown to be MRE11 independent. We propose that a rapid cleavage-repair cycle facilitated by MMEJ within the 70-bp repeats or in the Pseudo gene, where there are an abundance of sequence microhomologies, maintains VSG expression, BES integrity and high levels of cell survival.
DNA break site in the active BES gives rise to distinct gene expression changes that determine VSG switching pathway Our data shows, for the first time, that there are distinct gene expression changes associated with the position of a DNA break. I-SceI access is broadly equivalent across all sites tested, suggesting the differences in the response to the break are not due to variations in the efficiency of cutting due to the location of the break. The reduction in VSG2 expression could be interpreted one of two ways. Either DSB repair pathway choice results in downregulation of transcription-i. e. commitment to homology-based repair results in the formation of secondary structure that's inhibit transcription, or the reduction in VSG2 transcript triggers specific repair pathway choice. Our results indicate that there are distinct gene expression changes as early as 6 hours following a DSB in the active BES and propose that the position of the DSB triggers specific VSG switching pathways which may act to preserve the integrity of the BES.
We note that the FC are small in these analyses, however these samples were taken 6 hours following a DSB. At this early time point only a proportion of the cells had been cut (Fig 4A, 50% cut in VSG up and 40% cut in Pseudo sce ). It has been previously reported that chemically induced nuclear DNA damage leads to upregulation of silent BES promoter and other RNA polymerase 1 transcribed genes  [67]. Our data following a break in the Pseudo sce cell line broadly agrees with this, however we do not see expression of other RNA polymerase 1 genes such as EP1 Procyclin (Tb927. 10. 10260). This may be due to the specificity of the DSB in this study as compared to broad chemical damage in Sheader et al. Reversible derepression of silent BES promoters is also seen when the active BES promoter transcription is blocked  [68] , in a proposed probing of silent BESs before the cells commit to a switch.
Conclusion The mechanisms underlying recombination-based VSG switching are not well understood. To our knowledge, this is the first report that demonstrates that the position of a DSB in the active BES can trigger distinct gene expression changes, directing the cell through a specific repair pathway. We report that a DSB in the 70-bp repeats or upstream is rapidly repaired, predominantly by MMEJ, whereas only a DSB within close proximity to the expressed VSG leads to recombination-based VSG switching. This data has now expanded our understanding on DSBs as a trigger for VSG switching.
This project has received funding from the  Institut Pasteur  to LG. ET was supported by funding from the  French Government Agence Nationale de la Recherche  (https://anr. fr/), ANR-17-CE12-0012  to LG. AKM was funded by the Erasmus + (https://info. erasmusplus. fr/). EM was funded by  H2020 Priority Excellent Science Marie Skłodowska-Curie Actions  ( MSCA ) agreement number  665807  and the  Fondation pour la Recherche Medicale  (https://www. frm. org/)agreement number  FDT202012010602. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Aims. We present the first piece of evidence that adaptive learning techniques can boost the discovery of unusual objects within astronomical light curve data sets. Methods. Our method follows an active learning strategy where the learning algorithm chooses objects that can potentially improve the learner if additional information about them is provided. This new information is subsequently used to update the machine learning model, allowing its accuracy to evolve with each new piece of information. For the case of anomaly detection, the algorithm aims to maximize the number of scientifically interesting anomalies presented to the expert by slightly modifying the weights of a traditional isolation forest (IF) at each iteration. In order to demonstrate the potential of such techniques, we apply the Active Anomaly Discovery algorithm to two data sets: simulated light curves from the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC) and real light curves from the Open Supernova Catalog.
For clarity, we describe the main steps of the data preparation below. From the OSC catalog, we extracted objects with LCs in BRI  (Bessell 1990 ), g r i , or gri filters. We assumed that g r i filters are very similar to gri and that the coefficients of their transformation equations are quite small  (Fukugita et al. 1996; Tucker et al. 2006; Smith et al. 2007). Light curves originally observed in BRI filters were converted to gri using Lupton's transformation equations  5. The simulated data used in this work are a subsample of the LCs prepared for the PLAsTiCC data challenge, which was constructed to mimic the data scenario that we will encounter after 3 years of LSST observations. In order to build a data environment similar to the one we found in the OSC, we restricted our sample to six classes -SN Ia, SN II, SN Ibc, SN Ia-91bg, binary microlensing, and pair-instability SN (PISN)  6.
e. , the likelihood maximization procedure was unable to converge). A total of 7223 objects survived this preprocessing pipeline. The two approaches described above illustrate the flexibility demanded from any feature extraction procedure aimed at preparing astronomical data to be used in standard ML environments (with the exception of a few deep learning techniques). For future large-scale surveys, such as LSST, a numerical criterion such as the one we employ for PLAsTiCC is advised since visual inspection will not be possible. Regardless of the feature extraction choice, our goal is to highlight how AAD can improve upon results from IF, given that a small fraction of scientifically interesting anomalies are present in the final data set.
Methodology In order to compare the AAD results with those obtained with a traditional AD method and with a blind search, we performed a detailed analysis of all instances within ∼2% of the highest anomaly scores (145 objects for the simulated data and 40 objects for the real data). In the simulated data, this process was automatic. Once we selected the classes that represented anomalies (see Sect. 5), the algorithm was able to read the labels directly from the data file. For the OSC data, we recruited a team of two human experts (MP and AV), with extensive experience in observational and theoretical aspects of SN science, to carefully analyze each of the 40 candidates. These specialists performed a thorough investigation of each candidate -including consultation of external literature -and were not involved in the development or implementation of the AAD strategy.
Acknowledgements. E. E. O. Ishida  and  S. Sreejith  acknowledge support from  CNRS   2017 MOMENTUM grant  under project  Active Learning for Large Scale Sky Surveys. M. Pruzhinskaya  and  M. Kornilov  are supported by  RFBR  grant according to the research project  18-32-00426  for anomaly analysis and LCs approximation. K. Malanchev  and  V. Korolev  are supported by  RBFR  grant  20-02-00779  for preparing the  Open Supernova Catalog and PLAsTiCC data. A. Volnova  acknowledges support from  RSF  grant  18-12-00522  for analysis of interpolated LCs. We used the equipment funded by the  Lomonosov Moscow State University Program of Development. The authors acknowledge the support by the  Interdisciplinary Scientific and Educational School of Moscow University "Fundamental and Applied Space Research".
Cancer is a multifactorial and heterogeneous disease; each tumour typically has its own unique molecular characteristics, often leading to diverse responses to the same therapy for patients despite similar DRP datasets  [15] [16] [17] [18]  make them the most frequently used input for ML-based DRP. Given the omics data acquired from cell lines or tumours, along with their phenotypic responses to drugs, various ML approaches have been employed for DRP, with different inputs, models and learning processes, and different levels of complexity and interpretability of the resulting DRP models. DRP models range from established simple linear regression models to recent and more complex such as deep neural networks (DNNs). In addition, DRP methods can use biomolecular networks (between, e. g. genes and cell lines) that represent known or derived relationships.
While introducing basic biological and ML concepts when needed, (i) we discuss all major databases commonly used to learn DRP ML models, together with a systematic overview of the drug response measurement that was used, numbers of drugs/samples, and URLs, (ii) we survey more than 70 DRP methods across 13 subcategories, together with a systematic overview of used inputs, outputs, evaluation approach, and code and software availability, and (iii) we discuss common approaches to evaluate DRP methods.
Databases A number of ambitious experimental efforts have produced large pharmacogenomic datasets, i. e. collections of drug responses of clinical or preclinical samples, along with their molecular profiles. Box I provides an overview of commonly used preclinical drug response metrics that are used to represent the drug responses in these datasets. Pharmacogenomic data have provided insight into the relationship between clinical and preclinical molecular profiles and responses to anticancer drugs, and drug response-predictive biomarkers. In this section, we survey the commonly used large publicly available pharmogenomic datasets. The Cancer Genome Atlas (TCGA)  [25]  provides genomic, epigenomic, transcriptomic and proteomic features of over 11 000 patients across 33 cancer types. TCGA is a collaborative effort between the National Cancer Institute and the National Human Genome Research Institute started in 2006 aiming to better understand cancer biology and improve cancer treatment and prevention.
In TCGA, patient drug responses are categorized into four classes of complete response, partial response, progressive disease and stable disease, based on tumour growth measured by RECIST, an imaging-based criterion to evaluate the response in solid tumours  [26]. The TCGA dataset can be downloaded from Genomic Data Commons Data Portal. Based on the hypothesis that in vitro drug activity on cancer cell lines can capture essential aspects of in vivo drug activity on corresponding tumours  [12] , molecular and drug sensitivity profiles across a large number of cell lines have been generated; Table  1  provides an overview. In the late 1980s, a project led by the US National Cancer Institute measured the ability of thousands of anticancer drugs to inhibit cell growth across 60 human cancer cell lines  [6, 12].
The NCI-60 panel of measurements includes gene expression (GE), copy number variation (CNV), single nucleotide polymorphism (SNP), DNA methylation (DM) and proteomics profiles of each cell line. Analysis of this data resulted in the discovery of a number of gene mutations that are highly predictive of clinical responses  [27]  and provided new insights into drug activity modulators and drug-target interactions. Following the NCI-60 effort, two large collaborative datasets were established: the Cancer Cell Line Encyclopedia (CCLE)  [16]  and the Genomics of Drug Sensitivity in Cancer (GDSC)  [15]. CCLE is a collaboration of the Broad Institute and Novartis, and GDSC is a collaboration between The Cancer Genome Project at the Wellcome Trust Sanger Institute (UK) and the Center for Molecular Therapeutics at the Massachusetts General Hospital Cancer Center (USA).
Both datasets contain GE, CNV, DM, and mutational status (MS) of around 1000 human cancer cell lines from various tumour types, together with pharmacological profiles across a large fraction of these cell lines, for 24 and 518 drugs, respectively, in CCLE and GDSC. Although several studies pointed at inconsistencies in the drug response measurements between CCLE and GDSC  [28, 29] , others argued that the inconsistencies disappear under a specific, reasonable, interpretation of the data  [18, [30] [31] [32] [33]. To shed more light on this issue, the Genentech Cell Line Screening Initiative  [18]  generated profiled 410 cell lines common to GDSC and CCLE using various approaches, including RNA-seq GE, MS and CNV, along with their responses to 16 drugs tested in both studies. The outcomes of this study supported the previously proposed mode of consistent interpretability across CCLE and GDSC data.
PharmacoDB  [34]  provides large publicly available preclinical datasets that have been prepared for integrated use, with special emphasis on consistency. The Cancer Therapeutics Response Portal (CTRP)  [17]  provided another pharmacogenomic dataset with a variety of different molecular features of cell lines, including MS, GE, CNV and tissue type. The last published version of this dataset includes 860 characterized cancer cell lines and their responses to 481 compounds. DepMap is a data repository that aggregates a number of different preclinical datasets (including GDSC, CCLE and CTRP).
Logistic regression Instead of predicting drug response as a continuous variable, samples can also be classified as sensitive or resistant. Logistic regression is a common classification approach that can be derived, in a Bayesian framework, from a simple probabilistic model that expresses drug response as a function of molecular profiles  [30, 44, 55, 56]. Classifiers derived from these models have linear decision boundaries, i. e. lines or hyperplanes that separate different classes in the input space  [42, 57]. The assumption of linearity can be a strength in the case of small datasets, but a limitation when larger datasets would be able to resolve more complex decision boundaries. Huang et al. [58]  proposed a logistic regression model that is additionally constrained based on biomolecular networks.
Ma et al. [65]  used a transfer learning approach to address the problem of relatively little available DRP data for advanced preclinical models. In a first pre-training phase Downloaded from https://academic. oup. com/bib/advance-article/doi/10. 1093/bib/bbab408/6383559 by BIUS Jussieu user on 13 October 2021 of their TCRP method, a multilayer ANN is learned using a larger number of basic preclinical training samples (cell lines). In the following learning phase, a small dataset from advanced preclinical models is used to recalibrate the model to the new context. ANNs with many layers are called DNNs. Deep learning has recently attracted considerable attention across many fields and has become popular for DRP as well  [66] [67] [68]. Encoders are particular types of ANNs, typically used in DNNs for feature reduction, i.
Multitask linear regression Linear regression is also employed for MDL. In a DRP challenge competition performed by NCI-DREAM  [22]  the best-performing method was Bayesian multi-task multiple kernel learning (MKL), in which multiple data types (including original omics datasets and computationally derived data) are first kernelized (i. e. converted into nonlinear transfer functions), and then a global similarity matrix is constructed as their weighted sum. Next, a set of drug-specific weights, determined through multitask linear regression, are used to provide the final drug response values. Ammad-ud-din et al. [91]  pointed out that, although kernels may enhance the model predictions, the resulting models are typically hard to interpret, in terms of the relationship of their inputs to their outputs.
In an effort to leverage the observed activity of signalling pathways, Tang & Gottlieb  [97]  proposed a pathway-based method named PathDSP. In their approach, drug structure data along with the pathways enriched for drug targets, GE, somatic mutation and CNV are fed as inputs to a DNN. Zhu et al. [98]  used transfer learning to overcome the challenge of learning with limited amounts of data by pre-training their model on a dataset from a related context, and then finalizing the training process in the context in which the model is designed to predict. Convolutional neural networks (CNNs) are specific types of non-fully connected feedforward ANNs that can exploit natural adjacency structures in the data (such as physical or temporal adjacency) to more efficiently detect global patterns in the input data.
1093/bib/bbab408/6383559 by BIUS Jussieu user on 13 October 2021 relevant patterns of the molecular features in specific regions. The resulting images are then fed to a CNN for DRP. GNNs can be used to encode drug features as points in a low-dimensional space, in which similar drugs are close to each other, by representing molecular structure of compounds as networks formed by sets of chemical bonds between atoms  [105, 106]. In DeepCDR  [107]  and GraphDRP  [108] , deep drug features learnt by GNNs are concatenated with deep multi-omics features, to be fed to feedforward ANNs for DRP. Manica et al. [109]  proposed the MCA approach, in which genes are selected based on a network diffusion algorithm  [86]. Attention-based convolutional encoders are a recent new development from the field of natural language processing that has been useful to reduce the dimensionality of the GE and drug chemical data, to be used as input to a fully connected neural network for DRP  [110].
Evaluation of DRP models Model evaluation, i. e. the assessment of the expected performance of a trained model on future data, needs to occur on test Downloaded from https://academic. oup. com/bib/advance-article/doi/10. 1093/bib/bbab408/6383559 by BIUS Jussieu user on 13 October 2021 data that are independent from the training data. Otherwise, during training, the learned model parameters may be better adapted to the test data than could be expected for future data, thus leaving the test data evaluation optimistically biased. Data leakage, e. g. the-sometimes subtle-presence of information about the test data in the training data, may, for instance, occur when very similar data, such as drugs with similar structures, are present between training and test sets, or when the entire dataset is used for data preprocessing, e.
g. for normalization across cell lines. There are different common ways of splitting a single dataset into training and test data  [57]. In repeated random subsampling, the dataset is randomly split into training and test datasets repeatedly, typically, of given sizes. In each iteration, the model is trained on the training set and an evaluation metric (e. g. mean square error) is calculated on the test set. Finally, the model performance is estimated as the average evaluation metric over all iterations. In k-fold cross validation, the dataset is split into k subsets (folds, typically of identical size). In k iterations, each fold is used as a test set, and the remaining data are used for training. K-fold cross validation ensures that each data sample will appear in both train and test sets with the same frequency, and avoids stochastic fluctuations, particularly for small datasets.
Moreover, in cases when k-fold cross validation would lead to training sets that are too small, typically, leaveone-out cross validation (LOOCV) is used, in which test sets of only a single sample are used in each iteration. It is worth noting that LOOCV is not recommended for large datasets since it is computationally expensive and leads to estimates of the model performance with high statistical variance  [146]. Leavepair-out cross validation (LPOCV) is a special case where only two samples are held out. Finally, there is a validation method specifically for RF called out-of-bag validation (refer to section Ensemble Learning Methods). In models with hyperparameters, an additional subset of the training data (validation set) is typically split-off from the training set before training to evaluate the model under different hyperparameter values before its version with the best hyperparameter is evaluated on the test set.
Training and test sets can also be selected from different datasets. Cross-dataset evaluation thus also incorporates potential batch effects, i. e. systematically different characteristics between datasets, such as scale. In many real-world cases, heterogeneous output data present an additional challenge. For instance, regression models that predict IC50 values observed on cell lines may be difficult to evaluate against the drug effects as they are commonly characterized in clinical datasets. In biomedical classification scenarios, the amount of available data may strongly vary across the different classes, which presents special challenges for learning and evaluation. To balance classes before learning, one can undersample the majority class, over-sample the minority class with replacement, or use synthetic data (e.