dataesr/scientific-paragraphs-categorization huggingface dataset:
Method We present SumGNN in this section 1. We summarize problem settings in Section 3. 1 and describe our method in detail in Section 3. 2. Our method can be decomposed into three modules. First, we extract the local subgraph in the KG around drug pairs to obtain useful information. Then, we propose a summarization scheme to generate a mechanism pathway for drug interactions. After that, we describe a multi-channel neural encoding layer to predict the pharmacological effect.
The SumGNN Method SumGNN is composed of three modules: subgraph anchoring, knowledge summarization, and multi-channel neural encoding. For a given drug pair, we anchor to a subgraph of potential biomedical entities that are close to the pairs in the KG. Then, we propose a new graph neural network that has a summarization scheme to provide a condense pathway to reason about the drug interaction mechanism. Given this pathway graph, we use multi-channel neural encoding, to integrate diverse sources of available information to generate a sufficient drug pair representation. At last, a decoding classifier is followed to predict the interaction outcome. We initialize all entity embedding using KG method TransE  (Bordes et al. , 2013) , where a entity is denoted as h (0) u.
Experiment Setup Datasets (1) DrugBank dataset  (Wishart et al. , 2018)  contains 1,709 drugs (nodes) and 136,351 drug pairs (edges), which are associated with 86 types of pharmacological relations between drugs, such as increase of cardiotoxic activity, decrease of serum concentration and etc. Each drug pair can contain one or two relations. As more than 99. 8% of edges have only one edge type  (Ryu et al. , 2018) , we filtered the edge with more than one type in our study. (  2 ) TWOSIDES  (Tatonetti et al. , 2012)  dataset contains 645 drugs (nodes) and 46,221 drug-drug pairs (edges) with 200 different drug side effect types as labels. For each edge, it may be associated with multiple labels. Following  (Zitnik et al. , 2018a; Dai et al. , 2020) , we keep 200 commonly-occurring DDI types ranging from Top-600 to Top-800 to ensure every DDI type has at least 900 drug combinations.
(3) For external knowledge base, we use HetioNet  (Himmelstein and Baranzini, 2015) , which is a large heterogeneous knowledge graph merged from 29 public databases. To ensure no information leakage, we remove all the overlapping DDI edges between HetioNet and the dataset. In the end, we obtain 33,765 nodes out of 11 types (e. g. , gene, disease, pathway, molecular function and etc. ) with 1,690,693 edges from 23 relation types. Baselines We compare our models with several baselines 2. • MLP  (Rogers and Hahn, 2010)  uses a two-layer MLP on Morgan fingerprint to directly predict drug interactions. • Deepwalk  (Perozzi et al. , 2014)  first learns the embeddings for drugs in the network via random walk. Then, it predict the relation for drug pairs via a linear layer over embeddings. • LINE  (Tang et al.
• KG-DDI  (Karim et al. , 2019)  first extracts KG embeddings for drugs in the KG, then adopts a Conv-LSTM model using the embeddings for drug interaction prediction. • GraIL (Teru and Hamilton, 2020) is for inductive relation prediction on KGs, which uses local subgraph. • KGNN  (Lin et al. , 2020)  samples and aggregates neighborhoods for each node from their local receptives via GNN and with external knowledge graph, which achieves the state-ofthe-art result on binary DDI prediction problem. Metrics The task on the DrugBank dataset is a multi-class classification, thus we consider the following metrics: • F1 Score: average F1 score over different classes as F1 Score = 1 N N k=1 2P k •R k P k +R k , where N is the # of classes and P k , R k is the precision and recall for k-th class. Since it gives equal weights for each classes, they are more sensitive to the results for classes where samples are fewer.
• Accuracy: Accuracy over all samples Accuracy = |Y k ∩ Ŷk | |Y k | , Y k is the predicted labels at k and Ŷk are the ground-truth labels. • Cohen's Kappa  (Cohen, 1960)  measures the inter-annotator agreement as κ = (p o -p e )/(1p e ), where p o is the observed agreement (identical to accuracy), p e is the probability of randomly seeing each class. The task on the TWOSIDES dataset is a multi-label prediction. We follow  (Zitnik et al. , 2018a)  and consider the following measure. For each side effect type, we calculate the performance individually and use the average performance over all side effects as the final result. • ROC-AUC is the average area under the receiver operating characteristics curve as ROC-AUC = • AP@50 is the average precision at 50, where AP@k = |Y k ∩ Ŷk | |Y k | , Y k is the predicted labels at k and Ŷk are the ground-truth labels.
Evaluation Strategy. For both datasets, we split it into 7:1:2 as train, development and test set. For the DrugBank dataset, since the label distribution is highly imbalanced, we ensure train/dev/test set contain samples from all classes. For the TWOSIDES dataset, we use the same method in  (Zitnik et al. , 2018a)  to generate negative counterparts for each positive edge by sampling the complement set of positive examples. For every experiment, we conduct five independent runs and select the best performing model based on the loss value on the validation set.
SumGNN achieves superior predictive performance We report the performance of our model and all baselines in table 1. From the result, we find that SumGNN achieves the best performance in DDI prediction on two datasets, accurately predicting the correct DDI pharmacological effect consistently. Particularly, SumGNN has 27. 19%, 5. 47%, 4. 65% absolute increase over the best baseline without KG on three metrics respectively on DrugBank dataset and 2. 84%, 2. 45%, 4. 50% increase on TWOSIDES dataset. Also, SumGNN achieves 5. 54%, 1. 77%, 1. 08% and 1. 97%, 2. 25%. 2. 54% absolute increase over the state-ofthe-art baselines with KG on two datasets. These results clearly verifies the efficacy of our method. Table  1 : SumGNN achieves the best predictive performance compared to state-of-the-art baselines in DDI prediction.
SumGNN excels at low-data imbalanced relations predictions We observe that the improvement of SumGNN is much more significant on DrugBank than TWOSIDES dataset. We take a closer look at this problem and find that the major difference of these two datasets is that the data distribution of DrugBank is more imbalanced -more than 30% of the relation types occurs less than 50 times in the training set while more than 10% of the relation types occurs more than 1000 times, as shown in Fig. 2(a). To examine the model's prediction performance on the size of training data associated with each relation, we first split the relations types into 5 groups with various amounts of training data and then plot the average F1 score of these bins in Figure  2. By comparing the performance of SumGNN against the strongest baseline on Accuracy (KGNN) and the variant of no KG  (Decagon) , we find that SumGNN can effectively boost the performance when the samples are extremely scarce.
External knowledge improves prediction We find that the first seven method cannot obtain satisfactory result compared to SumGNN. MLP, Deepwalk, LINE and Node2vec only use shallow embedding layers to learn the features for drugs while do not use graph neural networks for modeling the drug interactions, which have limited expression power. Moreover, although GAT considers the attention on different edges, it fails to consider the multi-relation information, as its performance is also not good. By comparing the latter four methods with the former five methods, we find the use of KG significantly improves DDI performance, highlighting the necessity of combining external knowledge usage with graph neural networks, as it can provide complementary information for DDI task. 4. 5 Knowledge summarization module is the best to capture external knowledge Comparing SumGNN with KGNN and KG-DDI, we show that our model can consistently outperform them on both datasets (more than 4% on average for DrugBank and 2% on average for TWOSIDES), which indicates that simply adopting KG embeddings as well as neighborhood sampling are insufficient to fully harness the KG information for DDI prediction.
Ablation Study To study the usage of KG, we remove the knowledge graph (SumGNN-KG) and perform prediction on the DDI graph. We see SumGNN has 8. 5% absolute increase in Macro F1 on DrugBank, highlighting the usefulness of KG. To evaluate the knowledge summarization module, we remove the summarization component (SumGNN-Sum) and use the raw local subgraph to predict the outcome. We see SumGNN has 3. 65% increase for DrugBank and 2. 24% increase for TWOSIDES on Macro F1, suggesting that the summarization further condenses the relevant information and elevate the performance. To study the effect of multi-channel neural encoding, we compare the result of SumGNN with several variants that remove specific channel information (i. e. subgraph features and chemical features), and we find that these channel information all contribute to the overall performance.
Case Study The usefulness of this model lies in twofolds. First, given the high predictive performance, it can identify novel drug-drug interactions that are flagged high by the model while not in the dataset. Second, using the external knowledge summarization module, we are able to discover signal edges, which provide biological pathways to hint at the potential mechanism of DDIs. We provide a case study of a novel drug pair predicted by the model, Paroxetine and Hydroflumethiazide. Paroxetine is an antidepressant, and Hydroflumethiazide is used to treat hypertension and edema. SumGNN assigns highest probability for the DDI type "increase of the central nervous system  We see that the model significantly reduces irrelevant nodes and edges in the subgraph of the KG and focuses on a sparse set of nodes to make prediction.
Parameter studies We study the effect of key parameters. When evaluating one parameter, we fix other parameters to their default values. • Effect of the hop of the subgraph k: Figure  4  shows the result of SumGNN with varying k. From the result, we find that for DrugBank dataset, the performance first increase when k is small. However, when k increases from 3 to 4, we observe slight performance drops on F1 score. These indicate that the larger subgraph can bring more useful information while when k is too large, it may also bring some noise and hurt the performance. For TWOSIDES dataset, we find the result is more stable with different k, indicating even 1-hop subgraph provides adequate information for DDI task. Moreover, to show how subgraph formulation drives efficiency, we compare the training time between SumGNN with varying subgraph size and SumGNN with the entire KG to propagate (See Table  2 ).
Conclusion In this paper, we propose a new method SumGNN: knowledge summarization graph neural network for multi-typed DDI predictions, which is mainly enabled by a local subgraph module that can efficiently anchor on relevant subgraphs from a KG, a self-attention based subgraph summarization scheme that can generate reasoning path within the subgraph, and a multi-channel knowledge and data integration module that utilizes massive external biomedical knowledge for significantly improved multi-typed DDI predictions. Experiments on real-world datasets demonstrated the strong performance of SumGNN. In addition, computational approaches depend heavily on the training data. If the number of training data associated with one specific drug interaction type is low, it is difficult to predict accurately.
Introduction When navigating an unfamiliar city, people orient themselves based on a few landmarks, use a map for detailed navigation, and take advice from locals and previous visitors about where to go and which areas to avoid. Navigating a highly specialized research field, such as nonhuman primate (NHP) neuroimaging, is conceptually very similar. In order to orient, both access to basic information and support from knowledgeable peers are invaluable. Here we present the PRIMatE Resource Exchange (PRIME-RE, https://prime-re. github. io ), a new communitydriven platform that promotes communication among peers and facilitates the exchange of specialized knowledge and resources related to NHP neuroimaging. Translational neuroimaging in animals bridges the gap between noninvasive imaging studies in humans and invasive neural recordings that are only carried out in animal models (and occasionally in neurosurgical patients).
, 2020 ). For NHP neuroimaging, these issues may be even more pressing due to the custom-solutions that are often required to make existing software compatible with NHP data. The field of human neuroimaging has started to address these issues with initiatives for standardized, reproducible analysis pipelines (BIDS, Nipype, etc)  ( Gorgolewski et al. , 2011 ( Gorgolewski et al. , , 2016)) , data sharing  (OpenfMRI, Zenodo, Neurovault, etc)   ( Gorgolewski et al. , 2015 ; Poldrack et al. , 2013 ) , and collaborative and open coding  (GitHub, GitLab, etc). NHP neuroimaging has been lagging behind in adopting such collaborative open science initia-tives, but people's willingness to share and be open about the resource solutions they developed is steadily increasing  ( Balbastre et al. , 2017 ; Tasserie et al.
, 2020 )  and new developments in information technology promote these initiatives, while creating avenues to assign explicit credit to developers. The global NHP neuroimaging consortium  ( Milham et al. , 2020 ( Milham et al. , , 2018) )  aims to facilitate this progress with the introduction and curation of the PRIMatE Resource Exchange (PRIME-RE, https://primere. github. io ), a community-driven online platform for the exchange of knowledge and resources concerning the acquisition, analysis, and visualization of NHP neuroimaging data. PRIME-RE acts as an open hub where researchers from all countries and career stages can find and contribute information and resources related to the challenges and advantages of NHP neuroimaging. Dynamic collections of software tools are grouped by analysis-category to provide an overview of existing solutions to common challenges.
Methods This Methods section will offer a brief description of the main structure of the PRIME-RE platform, including its main components, methods for people to contribute and interact, and possibilities for future growth. The lightweight PRIME-RE website ( https://prime-re. github. io/ ) is intended to be dynamic and continuously evolve based on input from the research community. It can serve both as a starting point for researchers that are new to the field of NHP neuroimaging and as a meeting ground for more experienced researchers in the field. This initiative emerged from the Global Collaborative Workshop 2019 of the PRIME-DE consortium and subsequent BrainHack  Milham et al. (2020)  and has since attracted a critical mass of active contributors dedicated to open and collaborative NHP neuroimaging.
Results The first version of the PRIME-RE platform went live in November 2019. It currently describes over 30 custom resources from contributors all over the world and houses an extensive primer on NHP neuroimaging in the form of a wiki with references to the resources listed on PRIME-RE. It also provides a comprehensive overview of neuroimaging tools that are not necessarily tailored towards NHP neuroimaging (although some of these packages do offer built-in solutions to facilitate working with non-human data). Here, we will present a synopsis of the wiki document ( https://github. com/PRIME-RE/prime-re. github. io/wiki ) and an overview of some of the specialized resources that are part of PRIME-RE at this time.
NHP data, especially data from awake NHP neuroimaging studies, tend to require a lot more motion and distortion correction than the typical human dataset. The section on diffusion-weighted imaging discusses the differences between in vivo and post-mortem ( ex vivo ) diffusion MRI and their consequences for data analysis. Several caveats and solutions for tractography in NHPs are also laid out. The wiki ends with a section on cross-species comparison. While challenging, this is also one of the most powerful uses of NHP neuroimaging since it has the potential to yield important translational.
PRIME-RE resources The resources that are currently listed on PRIME-RE are divided into a number of categories based on their general purpose ( Fig. 1  ). Below we highlight these categories and a selection of tools from each. Please consult the website for a complete and up-to-date overview of the available resources ( https://prime-re. github. io/resources ). All resources listed on PRIME-RE, including those described here, were developed and assessed with datasets collected in accordance with locally approved guides and legislation concerning animal wellbeing, including the NIH Guide for Care and Use of Laboratory Animals, the U. K. Animals (Scientific Procedures) Act, 1986, European Directive 2010/63/EU, and the Australian Code for the Care and Use of Animals for Scientific Purposes.
Templates & atlases Alignment of an individual's (f)MRI data to a standard template space provides several advantages. Firstly, it provides a detailed anatomical image for evaluating and presenting functional results or other maps. Secondly, it presents a standard orientation and coordinate system for reporting findings, comparing results across subjects and labs, and aligning data for group analysis. Thirdly, templates frequently include ancillary datasets such as brainmasks, tissue segmentation masks, anatomical atlases, surfaces, morphometry, and data from other modal-ities that put a variety of analysis tools at the user's disposal. These resources can eliminate time-consuming manual processing and encourage the use of consistent and reproducible processing streams. For example, a brainmask can be used for template-based brain extraction in the subject's space or an atlas can be used for a region of interest (ROI) analysis in the template or subject space.
, 1999 ). The SARM offers neuroimaging researchers a complete subcortical segmentation of the rhesus macaque brain. The atlas can be used for a range of structural and functional analyses and has been specifically tailored to work with the CHARM and NMT v2  ( Jung et al. , 2020 ). The SARM is also available to download via Zenodo, and has conveniently been incorporated with the atlases available in AFNI. Additional information is available in the accompanying article  ( Hartig et al. , 2020 ).
A Paxinos-style cortical atlas  ( Majka et al. , 2016 )  was fused and refined into the high-resolution MRI template to provide interoperability to other marmoset databases. The atlas set provides a readily usable multi-modal template space with multi-level anatomical labels that can facilitate various neuroimaging studies of marmosets. 2) Version 2 focuses on fine-detailed white matter pathways  ( Liu et al. , 2020 ). In this version, ex-vivo MRI data of the marmoset brain was collected with the highest spatial resolution available to date, including 80 m and 64 m multi-shell dMRI, 80 m MTR, and 50 m T2w and T2 * w images. The data allowed building a fine-grained 3D white matter atlas, which depicts many fiber pathways that were either omitted or incorrectly described in previous MRI datasets or atlases of the primate brain.
These results can be downloaded in 3D volume format in the template space (Majka et al. , this issue), so they can be compared with MRI-based topologies. The portal also makes data available in a machine-mineable way ( http://analytics. marmosetbrain. org/wiki/api ) to facilitate large-scale models and simulations. Apart from the tracer-based connectivity data ( Fig. 3 C ), the portal hosts a collection of other unique datasets typically inaccessible in non-invasive imaging, such as high-resolution images of cytoarchitectural characteristics for each of the marmoset cortical areas, stained with a neuron-specific antibody  ( Atapour et al. , 2019 )  ( http://www. marmosetbrain. org/cell_density ). Moreover, a unique histology-based average morphology of the adult marmoset brain provides the basis for probabilistic registration of digital datasets to cytoarchitectural areas  (Majka et al.
Alignment and registration tools. Having properly oriented MRI datasets is important for many neuroimaging workflows and often crucial for further segmentation or registration across functional, anatomical and template images. For non-human scans (both in vivo and ex vivo ), orientations and field-of-views can vary tremendously, which means that a correction through reorientation and cropping is often required before any standard tools can be used. Group analysis of (f)MRI data is commonly performed in a standard template space to which individual data are registered. Here we describe tools for aligning datasets to a template or to each other as well as precalculated registrations between templates (RheMAP). These tools include Reorient, which aids in visual rigid body alignment, and nonlinear alignment tools such as macapype and @animal_warper (see Section 3.
2. 2. 4 ). 3. 2. 2. 2. 1. Reorient. An intuitive web tool for reorienting and cropping MRI data. Reorient ( https://neuroanatomy. github. io/reorient ) is an open source web application for the intuitive manual alignment and cropping of MRI NifTI volumes. The MRI scan is dragged onto a web interface and visualized in an interactive stereotaxic viewer. Users can then translate and rotate the brain by simply dragging inside the three view planes. An adjustable selection box determines the cropping of an image. Resulting affine matrices, selection boxes, as well as the reoriented and cropped volume can be saved. The affine matrix and selection box can then be used in scripted workflows to make these steps reproducible. Existing rotation matrices can also be loaded or appended in the web interface.
RheMAP. Precomputed nonlinear warps between common rhesus macaque brain templates. The generation of accurate nonlinear registrations between different brain images is a time-consuming and computationally expensive operation. The RheMAP project was created to generate a set of pre-computed nonlinear registration warps that allow the direct remapping of (f)MRI data across different common macaque template brains. The non-linear warps were generated using ANTs  ( Avants et al. , 2008 )  and the Python code that was used in RheMAP to compute the warps is available on GitHub ( https://github. com/PRIME-RE/RheMAP ) together with documentation and example code that explains how to use the warps to remap data between template spaces or register a single anatomical scan to one or more template spaces  ( Klink and Sirmpilatze, 2020 ).
The dataset of computed warps is freely available for download from Zenodo  ( Sirmpilatze and Klink, 2020 ). Registration quality can be visually assessed with the help of images that are distributed with the code and the dataset. RheMAP already includes a comprehensive set of templates -NMT  ( Jung et al. , this issue ; Seidlitz et al. , 2018 ) , D99  ( Reveley et al. , 2017 ) , INIA19  ( Rohlfing et al. , 2012 ) , MNI macaque  ( Frey et al. , 2011 )  and Yerkes19  ( Donahue et al. , 2016 )  -but the provided code allows for easy inclusion of additional template brains as well. Moreover, the general warp computation workflow can be easily adapted for other animal species, since the underlying ANTs registration functions do not rely on prior knowledge about brain size and shape.