Low-resource complex named entity recognition focuses on identifying complex entities such as creative work, product name and so on, in scenarios where annotated training data is limited. Recent advanced works deal with this task through data augmentation and make substantial progress. However, existing methods ignore the influence of different types or levels of augmented data on model optimization in different learning stages. To address it, we propose a dual-perspective data augmentation and curriculum learning framework. Specifically, we first employ the large language model (LLM) to construct two kinds of augmented datasets from context-perspective and entity-perspective, respectively. Then, we present a multi-stage curriculum learning strategy including a novel adaptive curriculum arrangement algorithm to automatically select the most suitable kind of augmented set to optimize the target model at each training epoch, thus using the augmented data more effectively and controllably. Experimental results on the public benchmark across various low-resource settings show that our framework outperforms previous works.

Extracting structured information from all manner of webpages is an important problem with the potential to automate many real-world applications. Recent work has shown the effectiveness of leveraging DOM trees and pre-trained language models to describe and encode webpages. However, they typically optimize the model to learn the semantic co-occurrence of elements and labels in the same webpage, thus their effectiveness depends on sufficient labeled data, which is labor-intensive. In this paper, we further observe structural co-occurrences in different webpages of the same website: the same position in the DOM tree usually plays the same semantic role, and the DOM nodes in this position also share similar surface forms. Motivated by this, we propose a novel method, Structor, to effectively incorporate the structural co-occurrences over DOM tree and surface form into pre-trained language models. Such structural co-occurrences help the model learn the task better under low-resource settings, and we study two challenging experimental scenarios: website-level low-resource setting and webpage-level low-resource setting, to evaluate our approach. Extensive experiments on the public SWDE dataset show that Structor significantly outperforms the state-of-the-art models in both settings, and even achieves three times the performance of the strong baseline model in the case of extreme lack of training data.

The pathogenesis of many inflammatory diseases is a coordinated process involving metabolic dysfunctions and immune response-usually modulated by the production of cytokines and associated inflammatory molecules. In this work, we seek to understand how genes involved in pathogenesis which are often not associated with the immune system in an obvious way communicate with the immune system. We have embedded a network of human protein-protein interactions (PPI) from the STRING database with 14,707 human genes using feature learning that captures high confidence edges. We have found that our predicted Association Scores derived from the features extracted from STRING's high confidence edges are useful for predicting novel connections between genes, thus enabling the construction of a full map of predicted associations for all possible pairs between 14,707 human genes. In particular, we analyzed the pattern of associations for 126 cytokines and found that the six patterns of cytokine interaction with human genes are consistent with their functional classifications. To define the disease-specific roles of cytokines we have collected gene sets for 11,944 diseases from DisGeNET. We used these gene sets to predict disease-specific gene associations with cytokines by calculating the normalized average Association Scores between disease-associated gene sets and the 126 cytokines; this creates a unique profile of inflammatory genes (both known and predicted) for each disease. We validated our predicted cytokine associations by comparing them to known associations for 171 diseases. The predicted cytokine profiles correlate (p-value<0.0003) with the known ones in 95 diseases. We further characterized the profiles of each disease by calculating an "Inflammation Score" that summarizes different modes of immune responses. Finally, by analyzing subnetworks formed between disease-specific pathogenesis genes, hormones, receptors, and cytokines, we identified the key genes responsible for interactions between pathogenesis and inflammatory responses. These genes and the corresponding cytokines used by different immune disorders suggest unique targets for drug discovery.

Resampling is an operation to convert a digital speech from a given sampling rate to a different one. It can be used to interface two systems with different sampling rates. Unfortunately, resampling may also be intentionally utilized as a postoperation to remove the manipulated artifacts left by pitch shifting, splicing, etc. To detect the resampling, some forensic detectors have been proposed. Little consideration, however, has been given to the security of these detectors themselves. To expose weaknesses of these resampling detectors and hide the resampling artifacts, a dual‐path resampling antiforensic framework is proposed in this paper. In the proposed framework, 1D median filtering is utilized to destroy the linear correlation between the adjacent speech samples introduced by resampling on low‐frequency component. And for high‐frequency component, Gaussian white noise perturbation (GWNP) is adopted to destroy the periodic resampling traces. The experimental results show that the proposed method successfully deceives the existing resampling forensic algorithms while keeping good perceptual quality of the resampled speech.

Motivation: Kinases play a significant role in diverse disease signaling pathways and understanding kinase inhibitor selectivity, the tendency of drugs to bind to off-targets, remains a top priority for kinase inhibitor design and clinical safety assessment. Traditional approaches for kinase selectivity analysis using biochemical activity and binding assays are useful but can be costly and are often limited by the kinases that are available. On the other hand, current computational kinase selectivity prediction methods are computational intensive and can rarely achieve sufficient accuracy for large-scale kinome wide inhibitor selectivity profiling. Results: Here, we present a KinomeFEATURE database for kinase binding site similarity search by comparing protein microenvironments characterized using diverse physiochemical descriptors. Initial selectivity prediction of 15 known kinase inhibitors achieved an >90% accuracy and demonstrated improved performance in comparison to commonly used kinase inhibitor selectivity prediction methods. Additional kinase ATP binding site similarity assessment (120 binding sites) identified 55 kinases with significant promiscuity and revealed unexpected inhibitor cross-activities between PKR and FGFR2 kinases. Kinome-wide selectivity profiling of 11 kinase drug candidates predicted novel as well as experimentally validated off-targets and suggested structural mechanisms of kinase cross-activities. Our study demonstrated potential utilities of our approach for large-scale kinase inhibitor selectivity profiling that could contribute to kinase drug development and safety assessment. Availability and implementation: The KinomeFEATURE database and the associated scripts for performing kinase pocket similarity search can be downloaded from the Stanford SimTK website (https://simtk.org/projects/kdb). Supplementary information: Supplementary data are available at Bioinformatics online.

Failure to demonstrate efficacy and safety issues are important reasons that drugs do not reach the market. An incomplete understanding of how drugs exert their effects hinders regulatory and pharmaceutical industry projections of a drug's benefits and risks. Signaling pathways mediate drug response and while many signaling molecules have been characterized for their contribution to disease or their role in drug side effects, our knowledge of these pathways is incomplete. To better understand all signaling molecules involved in drug response and the phenotype associations of these molecules, we created a novel method, PathFX, a non-commercial entity, to identify these pathways and drug-related phenotypes. We benchmarked PathFX by identifying drugs' marketed disease indications and reported a sensitivity of 41%, a 2.7-fold improvement over similar approaches. We then used PathFX to strengthen signals for drug-adverse event pairs occurring in the FDA Adverse Event Reporting System (FAERS) and also identified opportunities for drug repurposing for new diseases based on interaction paths that associated a marketed drug to that disease. By discovering molecular interaction pathways, PathFX improved our understanding of drug associations to safety and efficacy phenotypes. The algorithm may provide a new means to improve regulatory and therapeutic development decisions.

The recent increase in antibiotic resistance in pathogenic bacteria calls for new approaches to drug-target selection and drug development. Targeting the mechanisms of action of proteins involved in bacterial cell division bypasses problems associated with increasingly ineffective variants of older antibiotics; to this end, the essential bacterial cytoskeletal protein FtsZ is a promising target. Recent work on its allosteric inhibitor, PC190723, revealed in vitro activity on Staphylococcus aureus FtsZ and in vivo antimicrobial activities. However, the mechanism of drug action and its effect on FtsZ in other bacterial species are unclear. Here, we examine the structural environment of the PC190723 binding pocket using PocketFEATURE, a statistical method that scores the similarity between pairs of small-molecule binding sites based on 3D structure information about the local microenvironment, and molecular dynamics (MD) simulations. We observed that species and nucleotide-binding state have significant impacts on the structural properties of the binding site, with substantially disparate microenvironments for bacterial species not from the Staphylococcus genus. Based on PocketFEATURE analysis of MD simulations of S. aureus FtsZ bound to GTP or with mutations that are known to confer PC190723 resistance, we predict that PC190723 strongly prefers to bind Staphylococcus FtsZ in the nucleotide-bound state. Furthermore, MD simulations of an FtsZ dimer indicated that polymerization may enhance PC190723 binding. Taken together, our results demonstrate that a drug-binding pocket can vary significantly across species, genetic perturbations, and in different polymerization states, yielding important information for the further development of FtsZ inhibitors.

The recognition of cryptic small-molecular binding sites in protein structures is important for understanding off-target side effects and for recognizing potential new indications for existing drugs. Current methods focus on the geometry and detailed chemical interactions within putative binding pockets, but may not recognize distant similarities where dynamics or modified interactions allow one ligand to bind apparently divergent binding pockets. In this paper, we introduce an algorithm that seeks similar microenvironments within two binding sites, and assesses overall binding site similarity by the presence of multiple shared microenvironments. The method has relatively weak geometric requirements (to allow for conformational change or dynamics in both the ligand and the pocket) and uses multiple biophysical and biochemical measures to characterize the microenvironments (to allow for diverse modes of ligand binding). We term the algorithm PocketFEATURE, since it focuses on pockets using the FEATURE system for characterizing microenvironments. We validate PocketFEATURE first by showing that it can better discriminate sites that bind similar ligands from those that do not, and by showing that we can recognize FAD-binding sites on a proteome scale with Area Under the Curve (AUC) of 92%. We then apply PocketFEATURE to evolutionarily distant kinases, for which the method recognizes several proven distant relationships, and predicts unexpected shared ligand binding. Using experimental data from ChEMBL and Ambit, we show that at high significance level, 40 kinase pairs are predicted to share ligands. Some of these pairs offer new opportunities for inhibiting two proteins in a single pathway.