dataesr/scientific-paragraphs-categorization huggingface dataset:
Using NetProphet3 when little to no binding data are available We were able to train NP3 on many TFs that have binding data because S. cerevisiae is a well-studied organism. Less well-studied organisms have binding data on fewer TFs, so we investigated whether NP3 can be trained effectively on fewer TFs. We used TFKO and ZEV datasets and built separate networks for each. For each dataset, we trained NP3 on binding data from 10, 20, 30,. , up to 120 TFs. Each experiment was repeated 20 times with randomly selected TFs. Although we used different numbers of TFs for training, we predicted networks with the same number of edges by nesting random sub-sampling of TFs within the training folds of 10fold CV. The performance of NP3 continued to improve as the number of training TFs increased, up to 90, after which it did not change (Fig.
2D ). Training with fewer than 40 TFs yielded lower average performance than NP2, which has fixed parameters. Next, we investigated whether a model trained on the ZEV dataset could make accurate predictions on the TFKO data, and vice versa ('cross-trained' in Fig. 2E-H ; Supplementary Fig. S8A  and B  show results using the AUROC and AUPRC, respectively). By all metrics, the performance of NP3 cross-trained was at least as good as NP3 performance when trained on 40 TFs using features from the same expression dataset. Despite the differences between TFKO and ZEV datasets, which include growth format (batch versus continuous flow), nutrients and perturbation mechanism, parameters from one dataset transferred well to the other. Based on this transferability, we recommend using the model trained on yeast when applying NP3 to organisms that have binding data on < 40 TFs, to make predictions for the TFs that do not have binding data.
Interpretable probability scores NP3 predicts the probability that a given edge will have a positive label, i. e. that it would be supported in a binding experiment. To explore whether these probability scores are accurate estimates, we ran NP3 and calculated the expected fraction of support by binding data (mean of predicted probabilities) for edges above various thresholds (Fig. 3A  and B ). The expected fractions were close to the actual fraction of edges supported by binding data; where they differed, the actual fraction usually exceeded the expected value. We can thus interpret NP3 output for an edge as the probability that the edge will be supported by evidence of direct binding. (The accuracy of the predicted probabilities when training on one dataset and testing on another is lower, Supplementary Fig.
NetProphet3 can combine any number of evidence scores One advantage of NP3 over NP2 is that it can use any number of evidence scores as features during XGBoost training. To test this out, we generated DE, LASSO, BART and PWM scores separately from the TFKO and ZEV datasets and provided all eight features to XGBoost during training and testing by 10-CV. By all metrics, the resulting network was better than using each dataset separately (Fig. 3C-F ; Supplementary Fig. S9A  and B  show results using AUROC and AUPRC). The resulting network is provided as Supplementary File S8. Next, we tried adding the scores output by another network mapping method, Genie3, as a fifth feature in separate analyses of the TFKO or ZEV datasets. This had essentially no effect on performance (Supplementary Fig. S4A-D ), which shows that combining more network inference algorithms does not necessarily improve performance.
Discovery of potential new functions for TFs So far, we have used the yeast data as a benchmark for developing and evaluating network mapping algorithms. Here, we shift from evaluation to exploration of the yeast network map. Since our purpose is to construct a highly accurate map, not to benchmark, we trained NP3 on all the binding data rather than using CV. We trained NP3 using the eight features from TFKO and ZEV datasets together and thresholded the network at an average of 25 targets per TF (Supplementary File S9). We then investigated two aspects of the resulting network. First, we considered pairs of TFs with highly overlapping target sets as predicting PPIs between the TFs. Second, we considered the GO biological process term that was most enriched among the targets of a TF as predicting the function of that TF.
3. 6. 1 TFs with the most similar NP3-predicted target sets form wellstudied complexes that are known to regulate the TFs' shared targets Figure  4A  shows the PPI evaluation metric for this network at a resolution of one TF pair, for all 67 (TF, TF) pairs with Jaccard similarity above 0. 1. The nine (TF, TF) pairs with the highest Jaccard similarity all interact physically and 62% of pairs with similarity above 0. 1 do. To investigate further, we formed an undirected network in which TFs are vertices and two TFs are connected if they share more than one target in common and the Jaccard similarity of their target sets is >0. 1. Figure  4B  and C  show the connected components of this graph. Most edges are supported by strong evidence for physical interaction according to the STRING database (0.
Integration mode: using NetProphet3 to forge consensus networks from gene expression and binding data So far, we have shown that NP3 can be trained on TFs that have binding data to predict target probabilities for TFs without binding data. In this section, we show that NP3 can forge an even better network for TFs that have binding data by integrating the gene expression and binding data. For the previous section, we predicted edges using eight features (from TFKO and ZEV) by training one model with all available TFs that have binding data, rather than by CV. We wondered whether performance would degrade when a limited number of TFs is available for training, so we trained with fewer TFs. We did not evaluate these networks with the binding metric because predictions were not blind to binding data.
The binary training labels were derived by curation from a variety of datasets generated with different technologies, so they did not have any associated confidence scores. For a binary network, the only threshold that can be applied includes all edges with score 1, so we evaluated the positively labeled edges. NP3 scores were much better than the binding network by the GO and PPI metrics (Fig. 5D-F , solid green versus dashed brown). Among NP3-predicted targets with the most significant GO term for each TF, most were supported by binding data. But for the binding label network, by definition, all targets are presumed direct. This suggested another way to use NP3 in integration mode: use NP3 to rank the edges labeled 1, so that multiple thresholds can be applied to them, while leaving the edges labeled 0 with score 0.
This can be viewed as constraining the NP3 scores for 0-edges, but not for 1edges. Ranking the binary network in this way improved it greatly, according to both the GO and PPI metrics (Fig. 5D-F , dashed green vs. dashed brown). In fact, the performance of the constrained NP3 scores was comparable to that of the original NP3 scores in integration mode at every threshold. However, all edges with non-zero score are now supported by binding data. Throughout this article, the binding labels we used were binary-since they were derived from multiple datasets and experimental methods, they could not be directly compared. However, individual binding datasets often come with P-values or other confidence scores. The largest such dataset for yeast is the comprehensive ChIP-Chip data  (Harbison et al.
, 2004). We considered two ways of using this binding dataset to rank network edges. Method 1 is to simply rank the edges from most significant P-value to least significant. Method 2 is to choose a threshold on P-values, assign all edges below the threshold a score of 0 (bottom of ranking), and rank the edges above threshold by their NP3 scores. NP3 was used in integration mode with the 3% of most significant ChIP edges labeled positive and the remainder labeled negative. Both Method 1 and Method 2 guarantee that all the top scoring edges are supported by ChIP data. To compare them, we used the GO metric and the PPI metric, which are independent of binding data. The overall performance was lower than when training on the curated network, likely due to lower-quality binding data. However, thresholding the ChIP-Chip P-values and rescoring the edges above threshold with NP3 greatly improved the results, compared to ranking the positive edges using their original P-values from ChIP-Chip (Supplementary Fig.
Discussion NP3 exploits a supervised machine learning framework in order to predict the direct, functional targets of a TF from gene expression data. The features are built from gene expression datasets by a combination of linear-and tree-based regression algorithms and DE analysis (Fig. 1A ). However, the framework is general enough to incorporate any number of features, such as edge scores from other network mapping algorithms or from scanning TF bindingspecificity models across each gene's regulatory DNA. The binary training labels are constructed from TF-binding location data, with 1 indicating that the available data suggests the TF binds in the cisregulatory DNA of the target. This approach reflects our goal, which is to find direct, functional targets of each TF. The system learns to recognize the patterns in the gene expression data that are characteristic of directly bound targets.
Binding datasets are far from perfect and, even when a TF does bind a gene's regulatory DNA, it may not regulate that gene. However, it is unlikely that NP3 would predict targets that are bound but not functional, since its predictions are based on gene expression features that reflect functional regulation. NP3 can be used to predict direct, functional targets of TFs for which gene expression data, but not binding data, are available. When working with an organism for which at least 40 TFs have binding data, NP3 can be trained specifically for the gene expression and binding datasets from that organism. When working with an organism for which few TFs have binding data, NP3 models trained on yeast data can be used. In this application, the XGBoost machine learning framework is being used in the typical way, to generalize from training data (TFs with binding information) to previously unseen test data (TFs without binding information).
A limitation of NP3 is that its good performance depends on the availability of gene expression data after direct TF perturbations. Unlike purely regression-based algorithms, NP3 benefits greatly from DE analysis (Fig. 2A-C ). In addition, the regression methods that NP3 uses, LASSO and BART, also receive a boost in accuracy from TF perturbation data, even though they do not use any information about which TF was perturbed in each sample. That is likely because direct TF perturbations increase the variability of the TF expression levels in the dataset, making it easier to detect correlations between the expression levels of TFs and their targets. This suggests that other regression-based algorithms will also perform better with TF perturbation data than without. For TFs that have both perturbation-response data and binding location data, NP3 can be used to forge a consensus that integrates these two data types.
In this application, a separate XGBoost model is trained for each TF. The predictions of this model are then used to NP3 in integration mode outperforms the curated binary binding network that was used to train it, by the GO and PPI metrics, but the binding network has, by construction, 100% of edges supported by binding data. When the binary binding network is scored by NP3 predictions, binding support remains 100% but the performance with GO and PPI is greatly improved score the same (TF, target) edges that were used in training. Here, XGBoost is not being used for generalization to data not seen during training, but rather to integrate the expression datasets, from which the features are derived, with the binding datasets, from which the labels are derived. The binding-based labels are used only during training, but because the model is intentionally overfit to them, their influence is incorporated into the model and hence the predictions.
Some also had their small-scale fields measured with the Zeeman intensification using optical spectra  (Kochukhov et al. 2020 ) and, more recently, with NIR SPIRou data  (Petit et al. 2021). The goal of this study is to investigate if the smallscale magnetic fields measured using CRIRES + spectra agree with those measured with optical spectra as well as seeing if the strength of the large-and small-scale fields behave in a similar way. The rest of this paper is organised as follows. In Sect. 2, the observations and targets analysed in this study are introduced. This section also describes the data reduction process that was performed to obtain the final spectra. In Sect. 3, we discuss the impact of departures from local thermodynamic equilibrium (LTE) for Sun-like stars in the H-band as well as the selection of suitable lines for a magnetic field investigation.
Observations Observational data analysed in this study were taken from the ESO's observing period 108 between October 2021 and March 2022. The observations were carried out using CRIRES + , the recently upgraded NIR spectrograph at the 8-m UT3 Very Large Telescope. The CRIRES + instrument is a cross-dispersed echelle spectrometer and spectropolarimeter operating in conjunction with the MACAO adaptive optics system. The spectrometer is able to reach a spectral resolving power of 100 000 and obtain observations in the Y, J, H, K, L, and M bands using a mosaic of three 2K×2K detectors. The CRIRES + instrument is also capable of polarimetric observations in the Y, J, H, and K bands. For the magnetic field investigation of Sun-like stars, we focused on unpolarised spectra in the H-band, which are known to have several magnetically sensitive spectral lines that have been used in previous studies on both the Sun and other stars (e.
Data reduction Most of the observations were obtained in the adaptive optics mode using the 0. 2 slit. In all cases, nodding was employed to allow accurate background removal. The observing log is summarised in Table  A. 1, which gives the observing date, the total exposure time for each setting, and the resulting signal-to-noise ratio per pixel. The data were reduced using the CRIRES pipeline 1. The reduction process included the standard steps of obtaining master dark and flat field images, deriving order positions and wavelength calibration with U-Ne and Fabry-Perot etalon exposures, and optimally extracting the stellar spectra at the two nodding positions. The resulting spectra were then co-added and divided by the blaze function (derived from the master flat field) for each wavelength setting.
This issue manifests in two different ways. First, a stellar spectral line that is blended with a telluric line will cause Molecfit to interpret the reduced flux as a stronger telluric contribution to the spectra, effectively resulting in an overestimation of the telluric contamination. Secondly, stellar lines that are not blended with any tellurics will instead be interpreted as a reduced continuum flux, as Molecfit has no information about the presence of stellar features. To mitigate these issues, we produced a mask using a synthetic spectrum generated with SYNMAST  (Kochukhov et al. 2007 (Kochukhov et al. , 2010)) , line data from the Vienna Atomic Line Database (VALD;  Ryabchikova et al. 2015) , and plane-parallel MARCS model atmospheres  (Gustafsson et al. 2008). By identifying the regions with stellar absorption, we could remove as many stellar features as possible from the telluric removal procedure by masking out any wavelengths with significant absorption in the synthetic stellar spectra shifted to the stellar rest frame.
The spectral model grids were generated with SYNMAST using MARCS model atmospheres and VALD line lists modified by the procedure outlined in Sect. 3. 2. The rotational and radialtangential macroturbulent broadening was implemented in the same way as in the SME code as described by  Valenti & Piskunov (1996) , that is, by applying convolution with appropriate kernels to the synthetic spectra generated at seven different limb angles. When using SYNMAST, we assumed a uniform radial magnetic field configuration. This assumption is commonly used when modelling magnetic field effects in the unpolarised stellar spectra. Its accuracy has been tested by  Kochukhov (2021) , who showed that no significant deviations between different assumptions of uniform field orientation can be detected for discintegrated stellar spectra.
Acknowledgements. A. H. and O. K. acknowledge support by the  Swedish Research Council  (project  2019-03548 ). D. S. acknowledges funding from project  PID2021-126365NB-C21 ( MCI / AEI/FEDER, UE)  and financial support from the grant  CEX2021-001131-S  funded by  MCIN/AEI/ 10. 13039/501100011033. Based on observations collected at the  European Organisation for Astronomical Research  in the Southern Hemisphere under  ESO programme  0108. D-0659 (A). CRIRES+ is an ESO upgrade project carried out by  Thüringer Landessternwarte Tautenburg ,  Georg-August Universität Göttingen , and  Uppsala University. The project is funded by the  Federal Ministry of Education and Research (Germany)  through Grants  05A11MG3 ,  05A14MG4 ,  05A17MG2  and the  Knut and Alice Wallenberg Foundation. Analysis of this work has also used matplotlib (Hunter 2007), numpy (Harris et al.
In many real-world database systems, a large fraction of the data is represented by strings: sequences of letters over some alphabet. This is because strings can easily encode data arising from different sources. It is often crucial to represent such string datasets in a compact form but also to simultaneously enable fast pattern matching queries. This is the classic text indexing problem. The four absolute measures anyone should pay attention to when designing or implementing a text index are: (i) index space; (ii) query time; (iii) construction space; and (iv) construction time. Unfortunately, however, most (if not all) widely-used indexes (e. g. , suffix tree, suffix array, or their compressed counterparts) are not optimized for all four measures simultaneously, as it is difficult to have the best of all four worlds.
Here, we take an important step in this direction by showing that text indexing with locally consistent anchors (lcanchors) offers remarkably good performance in all four measures, when we have at hand a lower bound ℓ on the length of the queried patterns -which is arguably a quite reasonable assumption in practical applications. Specifically, we improve on the construction of the index proposed by Loukides and Pissis, which is based on bidirectional string anchors (bd-anchors), a new type of lc-anchors, by: (i) designing an average-case linear-time algorithm to compute bd-anchors; and (ii) developing a semi-external-memory implementation to construct the index in small space using near-optimal work. We then present an extensive experimental evaluation, based on the four measures, using real benchmark datasets.
INTRODUCTION In many real-world database systems, including bioinformatics systems  [83] , Enterprise Resource Planning (ERP) systems  [75] , or This work is licensed under the Creative Commons BY-NC-ND 4. 0 International License. Visit https://creativecommons. org/licenses/by-nc-nd/4. 0/ to view a copy of this license. For any use beyond those covered by this license, obtain permission by emailing info@vldb. org. Copyright is held by the owner/author(s). Publication rights licensed to the VLDB Endowment. Proceedings of the VLDB Endowment, Vol. 16, No. 9 ISSN 2150-8097. doi:10. 14778/3598581. 3598586 Business Intelligence (BI) systems  [88] , a large fraction of the data is represented by strings: sequences of letters over some alphabet. This is because strings can easily encode data arising from different sources such as: nucleic acid sequences read by sequencing machines (e.
g. , short or long DNA reads); natural language text generated by humans (e. g. , description or comment fields); or identifiers generated by machines (e. g. , URLs, email addresses, IP addresses). Given the ever increasing size of such datasets, it is crucial to represent them compactly  [14]  but also to simultaneously enable fast pattern matching queries. This is the classic text indexing problem  [23, 46, 78]. More formally, text indexing asks to preprocess a string 𝑆 of length 𝑛 over an alphabet Σ of size 𝜎, known as the text, into a compact data structure that supports efficient pattern matching queries; i. e. , decide if a pattern 𝑃 occurs or not in 𝑆 or report the set of all positions in 𝑆 where an occurrence of 𝑃 starts.
Our Contributions The purpose of this paper is to show that text indexing with locally consistent anchors (lc-anchors, in short) offers remarkably good performance in all four measures, when we have at hand a lower bound ℓ on the length of the queried patterns. This is arguably a quite reasonable assumption in practical applications. For instance, in bioinformatics  [50, 67, 90] , the length of sequencing reads (patterns) ranges from a few hundreds to 30 thousand  [67]. Even when at most 𝑘 errors must be accommodated for matching, at least one out of 𝑘 + 1 fragments must be matched exactly. In natural language processing, the queried patterns can also be long  [86]. Examples of such patterns are queries in question answering systems  [45] , description queries in TREC datasets  [3, 10] , and representative phrases in documents  [73].
We present an extensive experimental evaluation using real benchmark datasets. First, we show that our new algorithm for computing A ℓ (𝑆) is more than two orders of magnitude faster as ℓ increases than the simple Θ(𝑛ℓ)-time algorithm, while using a very similar amount of memory. We then go on to examine the index construction based on the above four measures. The results show that, for long patterns, the index constructed using our improved algorithms compares favorably to all classic indexes (implemented using sdsl-lite): (compressed) suffix tree; (compressed) suffix array; and the FM-index. For instance, our index offers about 30% faster query time, for all ℓ ∈  [16, 1024] , compared to the suffix array (which performs best among the competitors in this measure), while occupying up to two orders of magnitude less space.
EXPERIMENTS 6. 1 Experimental Setup We considered five datasets (texts) from the Pizza&Chili corpus  [27]  -see Table  1  for their characteristics. We generated patterns of length 16, 32, 64, 128, 256, 512, and 1024, for all datasets, with 500,000 patterns created per length. The patterns were generated by selecting occurrences uniformly at random from the datasets. We also considered the Homo sapiens genome (version GRCh38. p14), which we denote by HUMAN. Its length 𝑛 is 3,136,895,129 and the alphabet size 𝜎 is 30. We generated patterns of length 64, 256, 1,024, 4,096, and 16,384 for HUMAN with 500,000 patterns created per length. Similar to the datasets of Table  1 , these patterns were generated by selecting occurrences uniformly at random from HUMAN. The experiments ran on an Intel Xeon Gold 648 at 2.
5GHz with 192GB RAM. All programs were compiled with g++ version 9. 4. 0 at the -O3 optimization level. Our code and datasets are freely available at https://github. com/lorrainea/BDA-index. Parameters. For BDA-compute, BDA-index I and BDA-index II, we set ℓ to the pattern length, 𝑏 to 25K (unless stated otherwise), and 𝑟 to ⌈4 log ℓ/log 𝜎⌉; we set 𝑀 (RAM) to Θ(|A ℓ,𝑟 |) as this is anyway the final size of index I ℓ,𝑟 (𝑆). Measures. Four measures were used: index size; query time; construction space; and construction time (see Section 1). To measure the query and construction time, the steady_clock class of C++ was used with the elapsed time measured in nanoseconds (ns). To measure the index size for each implementation as accurately as possible, the data structures required for querying the patterns were output to a file in secondary memory.