Titlebook: Bioinformatics Research and Applications; 20th International S Wei Peng,Zhipeng Cai,Pavel Skums Conference proceedings 2024 The Editor(s) (

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https://doi.org/10.1007/978-3-319-93755-7to become a favorable alternative to antibiotics. The development of machine learning and deep learning-based AMP classification methods has been driven by the need to overcome the time-consuming and expensive aspects of wet-lab discriminatory techniques. Recently, several pre-trained protein langua

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Metallurgical Design and Industrymbersome and costly, requiring an overnight stay in a sleep laboratory with multiple sensors attached to the subjects, which limits widespread adoption. Conversely, electrocardiogram (ECG) signal offers a convenient and affordable alternative due to its ease of integration into portable devices. How

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The Role of Dislocation Drag in Shock Wavesd-and-extend strategy, where seeding usually involves a large number of random memory accesses, and extension of seeds relies on computationally expensive alignment algorithms, resulting in huge time consumption. Recently, Strobealign has reached state-of-the-art alignment speed while maintaining hi

Introduction

https://doi.org/10.1007/978-3-642-13956-7and time required for traditional high-throughput screening. However, DTI-related data are usually distributed across different institutions and their sharing is restricted because of data privacy and intellectual property rights. It is essential to construct a scheme that enhances multi-institution

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,Steel— the “Material of Choice”,ds are limited in performance due to that they are mainly designed for scRNA-seq data without accounting for spatial coordinate information. More importantly, they have been struggling to identify novel cell types. Here, we introduce SPOTAnno, a novel method that allows for the simultaneous and accu

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Metallurgical Process Engineeringimizing CNS drug efficacy. Since traditional experimental approaches are expensive and time-consuming, some computational methods based on Graph Neural Networks (GNNs) have emerged for predicting BBB permeability of small molecule compounds. While these methods have demonstrated considerable success

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Steel Plants and the Environment Issues,ark datasets, their prediction accuracy drops significantly in cold-start scenarios - i.e., when confront with drugs or proteins that have not appeared in the training set. Limited by training dataset scale, features learned by end-to-end DTA models have less generalization. To this end, we propose

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