dataesr/scientific-paragraphs-categorization huggingface dataset:
CONCLUSION Our results show that macroecological patterns in occupancyabundance and rank-abundance curves, and their downstream effects on standardized alpha diversity and beta dissimilarity, are similar across gut microbiome datasets, and therefore studies that apply similar thresholds are likely to be comparable assuming sufficient sampling frequency. However, trends in alpha and beta diversity scores tended to diverge above 70% prevalence thresholds, and diversity scores at high prevalence thresholds tended to correlate poorly with original data. Therefore, setting high prevalence thresholds when filtering microbiome datasets may hinder cross-study comparisons. To reduce downstream effects of prevalence filtering, we recommend the use of diversity metrics that account for both phylogeny and abundance (such as BWPD and Weighted UniFrac), which we show represent the common core microbiome without the need for filtering.
(D) The relationship between sample size and predictions of the overall ASV pool. Dashed lines represent the final ASV pool prediction per dataset, which match those shown in Figure 1A. (E) ASV prevalence distribution per dataset, showing the proportion of ASVs found in just one sample (dark blue) to the proportion found in over eight samples (yellow). (F) ASV prevalence distribution per sample, showing mean proportion of ASVs per sample found in just that sample (dark blue) to proportion found in at least eight other samples (yellow). (G) Abundance-occupancy curves per dataset. (H) Rank-abundance curves per species dataset.
FUNDING MG was supported by a grant of the  German Science Foundation  ( DFG Gi 1065/2-1 ). The microbiome research on flamingos was also supported by the  TOTAL Foundation. The microbiome investigations of mammals were funded by the  DFG  grant to SS ( SO 428/10-1 ) and the  DFG Priority Program SPP 1596/2 Ecology and Species Barriers in Emerging Infectious Diseases  ( SO 428/9-1, 9-2  and  TS 81/7-1, 7-2 ). Collection of Red-necked stint data was supported by funding from the  Australian Research Council  ( DP1301041935 ).
, 2021 PRJNA715350 Gray-brown mouse Microcebus Madagascar 182 Feces RNAlater 515F/806R 49,910 Wasimuddin NCBI BioProject lemur grisorufus (V4) et al. , 2019 PRJNA715730 Greater flamingo Phoenicopterus France 552 Cloacal RNAlater 515RF/806R 27,970 Gillingham NCBI BioProject roseus swab (V4) et al. , 2019 PRJNA485732 Red-necked stint Calidris ruficollis Australia 98 Cloacal None 27F/519R 42,573 Risely et al. , NCBI BioProject swab (V1-3) 2018 PRJNA385545.
Experiments To evaluate the ability of this method to detect the CFA pattern correctly, we take 15 images from the Raise dataset  [6] , and demosaic them using the 7 algorithms available in LibRaw: Bilinear interpolation, AAHD, AHD, DCB, DHT, PPG and VNG. Eleven of these images are of size 4948 × 3280, the other 4 are of size 4310 × 2868. The selected images can be seen in Figure  6. (a) r002fc3e2t (b) r1ead3024t (c) r1ceba29dt (d) r0a2ff882t (e) r0a808003t (f) r0a966704t (g) r0e04cc91t (h) r0ea0825ft (i) r1a0f5585t (j) r1c9fdcf4t (k) r06aa7dabt (l) r07cfb432t (m) r07ffdc87t (n) r16da5576t (o) r191f3cdet Figure  6 : These 15 images from the Raise dataset  [6]  were used in our experiments.
Image Forgery Detection The ultimate goal of the method is to find mosaic inconsistencies in an image. We use forgeries from the Trace database  [2]  to evaluate the method. The Trace database is constituted of 1000 images taken from the Raise dataset. Two forgery masks are made for each image: the endomask, obtained by taking a random object from the image's automatic segmentation, and the exomask, which is simply the endomask of another image of the set and thus do not correlate to the contents of the image. The concept of the database is to process the image with two different pipelines, and merge them with one of the forgery masks. Of the six datasets that are proposed, two are of interest to us: • in the CFA Grid dataset, the two pipelines are the same, but the pattern of demosaicing changes (the algorithm is the same).
The forgery thus has a different CFA pattern than the rest of the image. • in the CFA Algorithm dataset, the two pipelines are the same, but the algorithm of demosaicing changes. A new CFA pattern is also chosen at random for the forged region, with a 1 4 chance of being the same than the original image's. For the quantitative experiments, we use the CFA grid with exomasks dataset. For the qualitative experiments, we use samples from both the CFA grid and CFA algorithm datasets. Unless otherwise specified, quantitative experiments are done with the Matthews Correlation Coefficient (MCC)  [9]. This metric varies from -1 for a detection that is complementary to the ground truth, to 1 for a perfect detection. A score of 0 represents an uninformative result and is the expected performance of any random classifier.
The MCC is more representative than the F1 and IoU scores  [3, 4] , partly because it is less dependent on the proportion of positives in the ground truth, which is especially important given the large variety of forgery mask sizes in the database. It is defined by M CC = T P × T N -F P × F N (T P + F P ) • (T P + F N ) • (T N + F P ) • (T N + F N ) , where TP, FP, TN and FN respectively represent the numbers of true positives (TP), false positives (FP), true negatives (TN) and false negative (FN). The score is computed for each image, and then averaged over each dataset. As most surveyed methods do not provide a binary output but a continuous heatmap, we weight the confusion matrix using the heatmap. For several results, we also provide the Intersection over Union (IoU), the F1 score and the Precision and Recall.
Quantitative experimental results can be found in Table  2. In Table  2a , we can see that using bidirectional filters slightly improves the overall results. This corroborates the visual results of the previous subsection. Using thresholding not only improves the understandability of the method, it also provides significant improvements in the scores. Although this method does not work as well as Bammey  [1] , we note that it is also much simpler and more interpretable. Table  2b  shows results from taking only the results of the diagonal detection, the full pattern or their combination by pointwise maximum. The strategy of merging the two maps by pointwise maximum is the good one: it performs almost as well as the diagonal map on forgeries which do not share their diagonal, almost as well as the full map on forgeries that do share their diagonal (and are thus invisible in the diagonal map), and thus performs much better on the overall database than any of the maps taken separately.
As fewer windows get detected, a higher threshold systematically means that the recall is lower. However, a higher threshold does not necessarily improve the precision; the best precision (and best score overall) is achieved with a 0. 1 threshold, and higher thresholds yield a lower precision. This can be explained by the fact that the most confident detections often correspond to textured areas, where intermediate values are created by the texture more than the demosaicing, and are thus a source of false positives. Finally, Table  2e  shows the scores with different window sizes. While a window size of 32 × 32 yields the best results, this is inherently tied to the database in question. We saw earlier that increasing the window size would often lead to a better grid identification, but this also comes at the cost of missing small forgeries, and also failing to identify the borders of the bigger ones.
Image r06888d38t of the CFA Grid dataset with endomask, demosaiced with the VNG algorithm. The authentic region was demosaiced in the grbg pattern, the forged region in the gbrg pattern. Image r1594b4b3t of the CFA Grid dataset with exomask, demosaiced with the AAHD algorithm. The authentic region was demosaiced in the grbg pattern, the forged region in the bggr pattern.
rggb-bggr ∆ grbg-gbrg (a) Image r1d53fccat of the CFA Grid dataset, with endomask. Both regions are demosaiced with the AAHD algorithm, the authentic region in the gbrg pattern and the forged region in the grbg pattern. Because the method wrongly detects the •gg• over the whole image, it only compares the two patterns sharing that diagonal, and ∆ grid = ∆ rggb-bggr on almost all the image. As a consequence, no detection can be made on the grid level, and the forgery is not detected. Nevertheless, it appears clearly on ∆ grbg-grbg , which is not used in the algorithm. bggr ∆ grbg-gbrg (b) Image r15919202t of the CFA Grid dataset, with endomask. Both regions are demosaiced with the AAHD algorithm, the authentic region in the grbg pattern, the forged region in the bggr pattern. Although the method finds the wrong diagonal in both regions, it still finds that the two regions use a different diagonal.
Figure18: Image r040b3002t of the CFA Algorithm dataset, with exomask. The authentic region is demosaiced with the aahd algorithm in the grbg pattern, the forged region is demosaiced with the DCB algorithm in the bggr pattern. Because the method consistently finds the wrong diagonal on AAHD-demosaiced images, but detects the correct diagonal on DCB-demosaiced images, it believes that the two regions share the same diagonal, even though they do not.
Acknowledgements. We thank  T. Maris  (https://orcid. org/0000-0002-9819-6771),  De Vlaamse Waterweg NV , and  Rijkswaterstaat  for providing all data presented in this paper,  F. Azémar  (https://orcid. org/0000-0002-3098-8676) and  C. A. Sossou  for their contribution to the zooplankton data set,  T. Van Engeland  (https://orcid. org/0000-0002-3590-8831) for the approval to use the ScheldeData package to generate the illustration of the Scheldt estuary (see Fig. 1),  S. Jacobs  (https://orcid. org/0000-0003-4831-0844) for his critical review on the land-inward shift of zooplankton and the connection to the evolution in Chl-a concentration (see Fig. 3), and H. M. Schuttelaars (https://orcid. org/0000 -0001-8191-6296 ) for support, inspiration, and critical review of this work. Author D. M.
Code and data availability. The data presented in this paper in the Belgian and Dutch region of the Scheldt estuary is third party data and was accessed through http://www. omes-monitoring. be/en/data and https://waterinfo. rws. nl/, respectively. The iFlow version 2. 9 code with the flocculation extension and an input file example are available through https://doi. org/10. 5281/zenodo. 4560637.
Background: With the rapid advancement of genomic sequencing techniques, massive production of gene expression data is becoming possible, which prompts the development of precision medicine. Deep learning is a promising approach for phenotype prediction (clinical diagnosis, prognosis, and drug response) based on gene expression profile. Existing deep learning models are usually considered as black-boxes that provide accurate predictions but are not interpretable. However, accuracy and interpretation are both essential for precision medicine. In addition, most models do not integrate the knowledge of the domain. Hence, making deep learning models interpretable for medical applications using prior biological knowledge is the main focus of this paper.
Results: In this paper, we propose a new self-explainable deep learning model, called Deep GONet, integrating the Gene Ontology into the hierarchical architecture of the neural network. This model is based on a fully-connected architecture constrained by the Gene Ontology annotations, such that each neuron represents a biological function. The experiments on cancer diagnosis datasets demonstrate that Deep GONet is both easily interpretable and highly performant to discriminate cancer and non-cancer samples.
In the last decade, deep learning has become the source of the most impressive improvements in machine learning  [2]. It shows its superiority in many domains such as image analysis or natural language processing. Its main advantage is that it constructs high levels of data abstraction by stacking multiple linear and non-linear processing units. Deep learning has recently been applied to classification based on gene expression problems. Unlike images or texts, gene expression data have no structure. The architectures used in the literature are, therefore, mainly autoencoders and multilayer perceptrons (MLP)  [3]. For instance, Stacked Denoising Autoencoders  [4, 5]  are exploited to extract a lower dimension from the data, then a classifier (such as support vector machine (SVM) or MLP) is applied to perform classification.
All of these methods, post-hoc and self-explainable, consider that the interpretation of a model consists of the identification of the inputs and the part of the model, in case of deep learning the set of neurons, that support the predictions. In the context of phenotype prediction from gene expression, these methods generally do not provide an understandable explanation. The explanation must be completed with knowledge of the domain. For example, we have to explain which biological functions are represented in the model and which ones are used to compute the patient outcomes. Few works have been published on the construction of self-explainable neural networks based on gene expression data using prior biological knowledge. Prior knowledge comes from the ontologies such as Gene Ontology (GO)  [20] , Kyoto Encyclopedia of Genes and Genomes (KEGG)  [21] , Reactome  [22] , or Search Tool for the Retrieval of Interacting Genes/Proteins (STRING)  [23].
Our objective is to build an accurate and relevant interpretable model for cancer detection. Each neuron is associated with a GO function and the links between these functions are represented by the network connections. A prediction of the network can, therefore, be directly explained by the set of biological functions. The paper is organized as follows. We first describe the proposed novel model, Deep GONet, for biological interpretation. Then, the model is evaluated on two public datasets and compared with other approaches. We also provide how to obtain the explanations of outcomes and their biological significations at three levels (disease, subdisease, and patient). The conclusions to this paper and some future research directions are finally presented.
Dataset We validate our model on two datasets. The first one comes from a cross-experimental study compiling microarray data of over 40. 000 publicity available Affymetrix HG-U133Plus2 chip arrays  [28]. These arrays were produced under different experimental protocols and concerned seventeen types of tissue. The dataset contains 54675 probes for 22309 samples whose 14749 (66. 11%) are cancer and 7560 (33. 89%) are non-cancer. We standardize it to a mean of zero and a standard deviation of one, and split it into a training set of 17847 examples (11799 cancer, 6048 non-cancer) and a test set of 4462 examples (2950 cancer, 1512 non-cancer). Note that the original proportions of cancer and non-cancer samples are preserved in each set. The training set is used to train predictive models and the test set to evaluate their performances.
The second dataset is RNA-Seq data from The Cancer Genome Atlas (TCGA) combining 5982 samples of 11 cancer types and 482 normal samples from different tissues. Table  1  lists the number of cancer samples by cancer type. The number of input features is 56602. Before standardization, the data have been pre-normalized with FPKM (fragments per kilobase per million mapped reads) and transformed using log 2. 80% of the dataset goes into a training set and the remaining 20% into the test set.
Performances and sensitivity analysis In this first experiment, we compare the performances of Deep GONet on cancer prediction from gene expression profile with the state-of-the-art. Binary classification is evaluated on the microarray dataset with a sigmoid function in the output layer whereas multiclass classification is performed on the RNA-Seq data with a softmax. Deep GONet model is learned from the training set using a standard learning procedure. The number of layers and nodes are determined by the levels chosen in GO-BP. Different levels of GO-BP have been tested. For both datasets, we fix the architecture with levels 7 to 2 of GO-BP since it gives us the best performance. We test different values of the training hyperparameters and select the following settings. The weights and biases are initialized with He initializer.
On the microarray dataset, dropout layers with a ratio of 0. 6 are added after each hidden layer to reduce overfitting. The network parameters are optimized using adam with an adaptive learning rate of 0. 001. On the RNA-Seq dataset, we choose the stochastic gradient descent with a momentum equal to 0. 9. The number of epochs of the training is set up to 600. Different values of the hyperparameter α , controlling the regularization term L GO , are tested in the interval [0, 10 1 ]. The accu- racy of the model is estimated from the test set according to the value of α to investigate the impact of this hyperparameter on the performance of the model. To reduce the variability of the results coming from the random initialization of the model parameters, 10 models for each value of α are learned with different random seeds for the initialization of the parameters.
Our method is compared with classical fully-connected networks using L 2 or L 1 regularization terms. These regularization terms apply a penalty on all the connections regardless of the type (GO or noGO). L 2 is the squared magnitude of the weights L 2 = L l=1 �W (l) � 2 2 , and L 1 is the absolute value of the magnitude of the weights L 1 = L l=1 |W (l) |. These regularization terms are also controlled by a hyperparameter α. In addition, a model without any regularization is tested for comparison at α = 0. Note that all these models use the same baseline described in Fig. 1. They are trained and tested with the same procedure used for Deep GONet. The accuracy of each model is estimated from the test set. All the experiments have been executed on a GPU RTX 2080Ti using Tensorflow 1. 12. In what follows, the results on the microarray dataset (Fig.
2a-c ) are commented, but similar results are observed on the TCGA dataset (Fig. 2d-f  ). Figure  2a  (resp. Fig. 2d ) plots the average and the standard deviation of the accuracy of a model with a L 1 , L 2 , and L GO penalty according to α. The three curves begin at the same point since α = 0 corresponds to a model without regularization. We can see that the model without regularization and the one with L GO and L 2 at α = 10 -5 achieve the best accuracy (0. 945). Note that L GO and L 2 outperform L 1. We also test classical machine learning methods with scikit-learn python package: Random Forest (Gini criterion, number of trees=100), SVM (linear kernel, C=1. 0), XGboost (number of trees=100, learning rate=0. 1), and MLP (three layers with respectively 1000, 500 and 200 nodes). Metrics performance for each method are detailed in Table 2 (resp.
On the microarray dataset, the models at α = 10 -2 achieve an average accuracy around 0. 92 and an average ratio of 10 3. Since they represent a good trade-off between noGO connections penalization and accuracy, we analyze in-depth and interpret biologically one of the models learned with α = 10 -2 in the rest of this paper. The study will focus on the microarray dataset, but similar analyses can be conducted on the other dataset.
Biological significance of the neurons In this section, we check that the neurons of our network actually represent their corresponding GO term, i. e. , the activation of a given neuron represents the expression of the corresponding biological function. For that, we use the fact that each GO term  (5) R (l) i = N l+1 j=0 a (l) i w i,j k a (l) k w k,j + ǫ R (l+1) j Fig. 3  Sorting of incoming connections from each layer according to their absolute weight value in GO is associated with a set of probes. If a given neuron really represents its corresponding GO term, the set of probes associated with this GO term should activate the neuron more than any other set of probes. We propose a procedure illustrated in Fig. 4  based on this principle to test the biological significance of the neurons and evaluate the relationship with the importance of the neurons by using LRP with the package innvestigate  [29].
In this paper, the architecture of Deep GONet is based on GO-BP, but any other ontologies structured as a DAG (such as GO-CC, GO-MF) can be implemented in the neural network with the same approach. In addition, the model can be applied to other gene expression datasets, or other prediction tasks such as predicting the type of cancer or the prognostic, but it requires a retraining of the model. In future works, we plan to improve Deep GONet by adding neurons to deal with the genes without GO annotations and a second branch representing the pathways to enrich the biological interpretations. We will finally investigate the links between the activation of a neuron and the activation of the corresponding biological function.
Most mechanistic predator-prey modelling has involved either parameterization from process rate data or inverse modelling. Here, we take a median road: we aim at identifying the potential benefits of combining datasets, when both population growth and predation processes are viewed as stochastic. We fit a discrete-time, stochastic predator-prey model of the Leslie type to simulated time series of densities and kill rate data. Our model has both environmental stochasticity in the growth rates and interaction stochasticity, i. e. , a stochastic functional response. We examine what the kill rate data brings to the quality of the estimates, and whether estimation is possible (for various time series lengths) solely with time series of population counts or biomass data. Both Bayesian and frequentist estimation are performed, providing multiple ways to check model identifiability.
How could the uncertainty on parameter estimates of stochastic community models be decreased? One strategy that has enjoyed a great success in population ecology is to use a combination of datasets  (Besbeas et al. , 2002; Maunder, 2004; Schaub and Abadi, 2011). Combining multiple types of data increases the precision of parameters that could be estimated using a single data type (but with a large uncertainty), and often allows to estimate parameters that were not identifiable when the models were fit to a single data type  (Besbeas et al. , 2002). In the case of mechanistic predator-prey modelling, such strategy would be equivalent to combining process rate data with population densities, which is what we attempt here. Ours is not, of course, the first study to consider the relative benefits of forward modelling (process rate modelling) vs inverse modelling (e.
In this article, two key features are therefore that (1) the functional response comprises process rather than observational noise, and (2) kill rate data is added to population densities to help estimating functional response and other predator-prey parameters. To evaluate our ability to infer our stochastic predator-prey system parameters, we use simulations of parametric models. We consider a ground truth dynamical model for predator-prey systems with process noise on both growth rates and kill rates, in discrete time. We first simulate datasets using the model, and then fit this model and a variant to the simulated data. We use both frequentist and Bayesian estimation paradigms, to check that our results are congruent between the two frameworks and therefore robust. Identifiability is examined in both frameworks, using the Fisher Information Matrix in a frequentist setting and the posteriorprior overlap in a Bayesian setting, with or without kill rate data.