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## PHILM2Web: A high-throughput database of macromolecular host-pathogen interactions on the Web.

#### In Database : the journal of biological databases and curation During infection, the pathogen's entry into the host organism, breaching the host immune defense, spread and multiplication are frequently mediated by multiple interactions between the host and pathogen proteins. Systematic studying of host-pathogen interactions (HPIs) is a challenging task for both experimental and computational approaches and is critically dependent on the previously obtained knowledge about these interactions found in the biomedical literature. While several HPI databases exist that manually filter HPI protein-protein interactions from the generic databases and curated experimental interactomic studies, no comprehensive database on HPIs obtained from the biomedical literature is currently available. Here, we introduce a high-throughput literature-mining platform for extracting HPI data that includes the most comprehensive to date collection of HPIs obtained from the PubMed abstracts. Our HPI data portal, PHILM2Web (Pathogen-Host Interactions by Literature Mining on the Web), integrates an automatically generated database of interactions extracted by PHILM, our high-precision HPI literature-mining algorithm. Currently, the database contains 23 581 generic HPIs between 157 host and 403 pathogen organisms from 11 609 abstracts. The interactions were obtained from processing 608 972 PubMed abstracts, each containing mentions of at least one host and one pathogen organisms. In response to the coronavirus disease 2019 (COVID-19) pandemic, we also utilized PHILM to process 25 796 PubMed abstracts obtained by the same query as the COVID-19 Open Research Dataset. This COVID-19 processing batch resulted in 257 HPIs between 19 host and 31 pathogen organisms from 167 abstracts. The access to the entire HPI dataset is available via a searchable PHILM2Web interface; scientists can also download the entire database in bulk for offline processing. Database URL: http://philm2web.live.Le Tuan-Dung, Nguyen Phuong D, Korkin Dmitry, Thieu Thanh2022-Jun-30

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## Multi-view cross-subject seizure detection with information bottleneck attribution.

#### In Journal of neural engineering ; h5-index 52.0 OBJECTIVE : Signiﬁcant progress has been witnessed in within-subject seizure detection from Electroencephalography (EEG) signals. Consequently, more and more works have been shifted from within-subject seizure detection to cross-subject scenarios. However, the progress is hindered by inter-patient variations caused by gender, seizure type, etc. Approach: To tackle this problem, we propose a multi-view cross-object seizure detection model with information bottleneck attribution. Feature representations speciﬁc to seizures are learned from raw EEG data by adversarial deep learning. Combined with the manually designed discriminative features, the model can detect seizures across diﬀerent subjects. In addition, we introduce information bottleneck attribution to provide insights into the decision-making of the adversarial learning process, thus enhancing the interpretability of the model.MAIN RESULTS : Extensive experiments are conducted on two benchmark datasets. The experimental results verify the eﬃcacy of the model.Zhao Yanna, Zhang Gaobo, Zhang Yongfeng, Xiao Tiantian, Wang Ziwei, Xu Fangzhou, Zheng Yuanjie2022-Jun-29EEG, adversarial learning, cross-subject, information bottleneck attribution, seizure detection

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## Self-Supervised Self-Organizing Clustering Network: A Novel Unsupervised Representation Learning Method.

#### In IEEE transactions on neural networks and learning systems Deep learning-based clustering methods usually regard feature extraction and feature clustering as two independent steps. In this way, the features of all images need to be extracted before feature clustering, which consumes a lot of calculation. Inspired by the self-organizing map network, a self-supervised self-organizing clustering network (S 3 OCNet) is proposed to jointly learn feature extraction and feature clustering, thus realizing a single-stage clustering method. In order to achieve joint learning, we propose a self-organizing clustering header (SOCH), which takes the weight of the self-organizing layer as the cluster centers, and the output of the self-organizing layer as the similarities between the feature and the cluster centers. In order to optimize our network, we first convert the similarities into probabilities which represents a soft cluster assignment, and then we obtain a target for self-supervised learning by transforming the soft cluster assignment into a hard cluster assignment, and finally we jointly optimize backbone and SOCH. By setting different feature dimensions, a Multilayer SOCHs strategy is further proposed by cascading SOCHs. This strategy achieves clustering features in multiple clustering spaces. S 3 OCNet is evaluated on widely used image classification benchmarks such as Canadian Institute For Advanced Research (CIFAR)-10, CIFAR-100, Self-Taught Learning (STL)-10, and Tiny ImageNet. Experimental results show that our method significant improvement over other related methods. The visualization of features and images shows that our method can achieve good clustering results.Li Shuo, Liu Fang, Jiao Licheng, Chen Puhua, Li Lingling2022-Jun-29

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