Combining Zhegalkin Polynomials and SAT Solving for Context-specific Boolean Modeling of Biological Systems [0.03%]
结合切加林多项式和SAT解决技术进行生物系统的上下文相关布尔建模
Vincent Deman,Marine Ciantar,Laurent Naudin et al.
Vincent Deman et al.
Large amounts of knowledge regarding biological processes are readily available in the literature and aggregated in diverse databases. Boolean networks are powerful tools to render that knowledge into models that can mimic and simulate biol...
Yang Hua,Zhenhua Feng,Xiaoning Song et al.
Yang Hua et al.
Recently, mask-fill-based 3D Molecular Generation (MG) methods have become very popular in virtual drug design. However, the existing MG methods ignore the chemical properties of atoms and contain inappropriate atomic position training data...
Bridging Between Deviation Indices for Non-Tree-Based Phylogenetic Networks [0.03%]
非树基于谱系网络的偏差指数之间的桥梁
Takatora Suzuki,Han Guo,Momoko Hayamizu
Takatora Suzuki
Phylogenetic networks are a useful model that can represent reticulate evolution and complex biological data. In recent years, mathematical and computational aspects of tree-based networks have been well studied. However, not all phylogenet...
CTsynther: Contrastive Transformer model for end-to-end retrosynthesis prediction [0.03%]
基于对比学习的端到端逆合成预测模型CTsynther
Hao Lu,Zhiqiang Wei,Kun Zhang et al.
Hao Lu et al.
Retrosynthesis prediction is a fundamental problem in organic chemistry and drug synthesis. We proposed an end-to-end deep learning model called CTsynther (Contrastive Transformer for single-step retrosynthesis prediction model) that could ...
Zhijing Li,Liwei Tian,Yiping Jiang et al.
Zhijing Li et al.
Relation extraction, a crucial task in understanding the intricate relationships between entities in biomedical domains, has predominantly focused on binary relations within single sentences. However, in practical biomedical scenarios, rela...
Integrating Similarities Via Local Interaction Consistency and Optimizing Area Under the Curve Measures Via Matrix Factorization for Drug-Target Interaction Prediction [0.03%]
基于局部交互一致性的相似性整合和基于矩阵分解的面积优化算法在药物-靶点相互作用预测中的应用研究
Bin Liu,Grigorios Tsoumakas
Bin Liu
In drug discovery, identifying drug-target interactions (DTIs) via experimental approaches is a tedious and expensive procedure. Computational methods efficiently predict DTIs and recommend a small part of potential interacting pairs for fu...
LKLPDA: A Low-Rank Fast Kernel Learning Approach for Predicting piRNA-Disease Associations [0.03%]
一种低秩快速核学习方法用于预测piRNA-疾病关联
Qingzhou Shi,Kai Zheng,Haoyuan Li et al.
Qingzhou Shi et al.
Piwi-interacting RNAs (piRNAs) are increasingly recognized as potential biomarkers for various diseases. Investig-ating the complex relationship between piRNAs and diseases through computational methods can reduce the costs and risks associ...
MMD-DTA: A multi-modal deep learning framework for drug-target binding affinity and binding region prediction [0.03%]
MMD-DTA：一种用于药物-靶标结合亲和力和结合区预测的多模态深度学习框架
Qi Zhang,Yuxiao Wei,Bo Liao et al.
Qi Zhang et al.
The prediction of drug-target affinity (DTA) plays a crucial role in drug development and the identification of potential drug targets. In recent years, computer-assisted DTA prediction has emerged as a significant approach in this field. I...
Development and Validation of a Comprehensive Analysis of the Competing Endogenous circRNA/miRNA/mRNA Network for the Identification of Immune-Related Targets in Esophageal Squamous Cell Carcinoma [0.03%]
开发和验证全面分析竞争性内源性的circRNA/miRNA/mRNA网络，以识别食管鳞状细胞癌中免疫相关的靶点及其验证
Chu-Ting Yu,Bo Tian,Qian-Qian Meng et al.
Chu-Ting Yu et al.
Immunotherapy for esophageal squamous cell carcinoma (ESCC) exhibits notable variability in efficacy. Concurrently, recent research emphasizes circRNAs' impact on the ESCC tumor microenvironment. To further explore the relationship, we leve...
Contrasting Multi-Source Temporal Knowledge Graphs for Biomedical Hypothesis Generation [0.03%]
基于多源时序知识图谱的生物医学假设生成方法研究
Huiwei Zhou,Wenchu Li,Weihong Yao et al.
Huiwei Zhou et al.
Hypothesis Generation (HG) aims to expedite biomedical researches by generating novel hypotheses from existing scientific literature. Most existing studies focused on modeling static snapshots of the corpus, neglecting the temporal evolutio...