Prediction of drug–target affinity by constructing both molecule and protein graphs.

Sodium-ion batteries (SIBs) have emerged as one of the most promising candidates for next-generation energy storage systems because sodium is abundant in nature. The practical application of SIBs critically depends on developing robust electrode materials with high specific capacity and long cycling life, developing suitable anode materials is even more challenging. Alloy-type anodes are attractive for their high gravimetric and volumetric specific capacities, demonstrating great potential for high-energy SIBs, however, huge volume swelling hampered their practical application. Given the encouraging breakthroughs on alloy anodes for SIBs, herein, we present a review of the up-to-date progress and works carried out with alloy-based anode materials for SIBs. We review the synthetic strategies and their detailed electrochemical performance. In particular, we extensively reveal the important roles of alloy-based anodes in the development of SIBs. Research progress of alloy-type anodes and their compounds for sodium storage is summarized. Specific efforts to enhance the electrochemical performance of the alloy-based anode materials are discussed. Finally, we proposed multi-component alloys/high-entropy alloys (HEAs) as further research directions for alloy-based anodes.

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Epilepsy is a disease in which patients undergo seizures caused by brain functionality disorder. Clinically, it is usually diagnosed by experienced clinicians according to continuous electroencephalography (cEEG), which is time consuming even for experienced doctors. Meanwhile, amplitude integrated electroencephalography (aEEG) has shown potential to detect epileptic seizures. Therefore, the paper proposes a hybrid seizure detection algorithm by combining cEEG-based seizure detection algorithm and aEEG-based seizure detection algorithm to detect seizures. In cEEG-based seizure detection algorithm, cEEG signals are divided into 5 s epoch with 4 s overlap and multi-domain features are extracted from each epoch. Then random forest classification is applied to do seizure detection. In aEEG-based seizure detection algorithm, morphological filter is applied to do spike detection and determine whether there are seizures after transforming the cEEG signals into aEEG signals. In order to evaluate the generality of the proposed method, experiments are performed on two independent datasets, including a publicly available EEG dataset (CHB-MIT) and an epileptic dataset collected by using the EEG device developed by the Hangzhou Neuro Science and Technology Co., Ltd. In the CHB-MIT dataset, the accuracy (AC), specificity (SP), sensitivity based on the event (SE), and false positive ratio based on the event (FPRE) obtained by the hybrid method are 99.36%, 82.98%, 99.41%, and 0.57 times/h, respectively. In the dataset we collected, the AC, SP, SE, and FPRE obtained by the hybrid method are 99.23%, 89.47%, 99.23%, and 0.71 times/h, respectively. The experimental results show that the performance of the proposed method is competitive with state-ofthe-art methods and results. Furthermore, basing on the hybrid method, this paper has developed a portable automatic seizure detection system, which can reduce the burden of clinicians in processing the large amounts of cEEG signals by detecting seizure automatically.INDEX TERMS Seizure detection, multi-domain feature, spike detection, hybrid method.

Motivation Recent technology developments have made it possible to generate various kinds of omics data, which provides opportunities to better solve problems such as disease subtyping or disease mapping using more comprehensive omics data jointly. Among many developed data-integration methods, the similarity network fusion (SNF) method has shown a great potential to identify new disease subtypes through separating similar subjects using multi-omics data. SNF effectively fuses similarity networks with pairwise patient similarity measures from different types of omics data into one fused network using both shared and complementary information across multiple types of omics data. Results In this article, we proposed an association-signal-annotation boosted similarity network fusion (ab-SNF) method, adding feature-level association signal annotations as weights aiming to up-weight signal features and down-weight noise features when constructing subject similarity networks to boost the performance in disease subtyping. In various simulation studies, the proposed ab-SNF outperforms the original SNF approach without weights. Most importantly, the improvement in the subtyping performance due to association-signal-annotation weights is amplified in the integration process. Applications to somatic mutation data, DNA methylation data and gene expression data of three cancer types from The Cancer Genome Atlas project suggest that the proposed ab-SNF method consistently identifies new subtypes in each cancer that more accurately predict patient survival and are more biologically meaningful. Availability and implementation The R package abSNF is freely available for downloading from https://github.com/pfruan/abSNF. Supplementary information Supplementary data are available at Bioinformatics online.

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