Quantitative Biology is an academic journal focused on the field of quantitative biology, covering multiple interdisciplinary disciplines such as systems biology, synthetic biology, bioinformatics, and computational biology. The journal is dedicated to publishing high quality research papers that use mathematical, statistical, computational and theoretical approaches to the study of biological problems.Systems biology is concerned with understanding the overall behavior and function of biological systems, including the complex interactions of cells, tissues, organs, and even entire organisms. Synthetic biology focuses on the design and construction of new biological components, devices, and systems, as well as the redesign of existing natural biological systems. Bioinformatics utilizes computational methods to analyze and interpret biological data, while computational biology involves the development and application of mathematical models to simulate biological processes.
定量生物学(Quantitative Biology)是一本由Higher Education Press出版的一本MATHEMATICAL & COMPUTATIONAL BIOLOGY学术刊物,主要报道MATHEMATICAL & COMPUTATIONAL BIOLOGY相关领域研究成果与实践。本刊已入选来源期刊,出版周期4 issues/year。2021-2022年最新版WOS分区等级:Q4,2023年发布的影响因子为0.6,CiteScore指数5,SJR指数0.631。本刊非开放获取期刊。 《Quantitative Biology》是一本专注于定量生物学领域的学术期刊,涵盖了系统生物学、合成生物学、生物信息学和计算生物学等多个交叉学科。该期刊致力于发表采用数学、统计学、计算和理论方法来研究生物学问题的高质量研究论文。系统生物学关注于理解生物系统的整体行为和功能,包括细胞、组织、器官乃至整个生物体的复杂相互作用。合成生物学则侧重于设计和构建新的生物部件、装置和系统,以及重新设计已存在的自然生物系统。生物信息学利用计算方法分析和解释生物数据,而计算生物学则涉及开发和应用数学模型来模拟生物过程。
Predicting enhancer-promoter interaction from genomic sequence with deep neural networks
来源期刊:Quantitative BiologyDOI:10.1007/s40484-019-0154-0
Progress in molecular docking
来源期刊:Quantitative BiologyDOI:10.1007/s40484-019-0172-y
Emerging deep learning methods for single-cell RNA-seq data analysis
来源期刊:Quantitative BiologyDOI:10.1007/s40484-019-0189-2
A survey of web resources and tools for the study of TCM network pharmacology
来源期刊:Quantitative BiologyDOI:10.1007/s40484-019-0167-8
Algorithmic approaches to clonal reconstruction in heterogeneous cell populations
来源期刊:Quantitative BiologyDOI:10.1007/s40484-019-0188-3
Current challenges and solutions of de novo assembly
来源期刊:Quantitative BiologyDOI:10.1007/s40484-019-0166-9
Overlap graphs and de Bruijn graphs: data structures for de novo genome assembly in the big data era
来源期刊:Quantitative BiologyDOI:10.1007/s40484-019-0181-x
Applications of single-cell technology on bacterial analysis
来源期刊:Quantitative BiologyDOI:10.1007/s40484-019-0177-6
WIPER: Weighted in-Path Edge Ranking for biomolecular association networks
来源期刊:Quantitative BiologyDOI:10.1007/s40484-019-0180-y
Insights into the antineoplastic mechanism of Chelidonium majus via systems pharmacology approach
来源期刊:Quantitative BiologyDOI:10.1007/S40484-019-0165-X
A survey of some tensor analysis techniques for biological systems
来源期刊:Quantitative BiologyDOI:10.1007/s40484-019-0186-5
Identifying MiRNA-disease association based on integrating miRNA topological similarity and functional similarity
来源期刊:Quantitative BiologyDOI:10.1007/s40484-019-0176-7
WEDeepT3: predicting type III secreted effectors based on word embedding and deep learning
来源期刊:Quantitative BiologyDOI:10.1007/s40484-019-0184-7
Molecular modeling studies of repandusinic acid as potent small molecule for hepatitis B virus through molecular docking and ADME analysis
来源期刊:Quantitative BiologyDOI:10.1007/s40484-019-0179-4
Computational prediction and functional analysis of arsenic-binding proteins in human cells
来源期刊:Quantitative BiologyDOI:10.1007/s40484-019-0169-6
A model of NSCLC microenvironment predicts optimal receptor targets
来源期刊:Quantitative BiologyDOI:10.1007/s40484-019-0171-z
Identification of candidate disease genes in patients with common variable immunodeficiency
来源期刊:Quantitative BiologyDOI:10.1007/s40484-019-0174-9
Predicting microRNA-disease association based on microRNA structural and functional similarity network
来源期刊:Quantitative BiologyDOI:10.1007/s40484-019-0170-0
EpiFIT: functional interpretation of transcription factors based on combination of sequence and epigenetic information
来源期刊:Quantitative BiologyDOI:10.1007/s40484-019-0175-8
2019 China Symposium on Single-Cell Genomics
来源期刊:Quantitative BiologyDOI:10.1007/s40484-019-0178-5
Differential methylation analysis for bisulfite sequencing using DSS
来源期刊:Quantitative BiologyDOI:10.1007/s40484-019-0183-8
Understanding traditional Chinese medicine via statistical learning of expert-specific Electronic Medical Records
来源期刊:Quantitative BiologyDOI:10.1007/s40484-019-0173-x