中文簡介

生物系統(tǒng)鼓勵將生物學(xué)、進化思維和信息處理科學(xué)聯(lián)系起來的實驗、計算和理論文章。鏈接區(qū)域形成一個圓,包括生物信息處理的基本性質(zhì)、計算復(fù)雜生物系統(tǒng)的建模,進化計算模型,應(yīng)用生物原理的設(shè)計新穎的計算系統(tǒng),以及利用生物分子材料的合成人工系統(tǒng),捕捉自然生物信息處理的基本原則。下面列出的類別和主題都是例子，編輯們會很樂意就其他主題的相關(guān)性發(fā)表意見:分子進化自我組織和自我復(fù)制系統(tǒng)遺傳機制的起源和演化生物信息處理分子識別細胞的控制Neuromolecular計算生物適應(yīng)性分子計算技術(shù)進化系統(tǒng)隨機進化算法進化優(yōu)化遺傳和生態(tài)系統(tǒng)的模擬應(yīng)用(神經(jīng)網(wǎng)絡(luò)，機器學(xué)習(xí)，機器人)

　　英文簡介

BioSystems encourages experimental, computational, and theoretical articles that link biology, evolutionary thinking, and the information processing sciences. The link areas form a circle that encompasses the fundamental nature of biological information processing, computational modeling of complex biological systems, evolutionary models of computation, the application of biological principles to the design of novel computing systems, and the use of biomolecular materials to synthesize artificial systems that capture essential principles of natural biological information processing.The categories and topics listed below are examples, the editors will be happy to comment on the relevance of other topics:Molecular EvolutionSelf-organizing and self-replicating systemsOrigins and evolution of the genetic mechanismBiological Information ProcessingMolecular recognitionCellular controlNeuromolecular computingBiological adaptabilityMolecular computing technologiesEvolutionary SystemsStochastic evolutionary algorithmsEvolutionary optimizationSimulation of genetic and ecological systemsApplications (neural nets, machine learning, robotics)

　　近年期刊自引率趨勢圖

　　JCR分區(qū)

JCR分區(qū)等級	JCR所屬學(xué)科	分區(qū)	影響因子
Q3	BIOLOGY	Q3	1.957
	MATHEMATICAL & COMPUTATIONAL BIOLOGY	Q3

　　近年期刊影響因子趨勢圖

　　CiteScore數(shù)值

CiteScore	SJR	SNIP	學(xué)科類別	分區(qū)	排名	百分位
4.00	0.522	0.902	大類：Mathematics 小類：Statistics and Probability	Q1	38 / 250	85%
			大類：Mathematics 小類：Applied Mathematics	Q1	121 / 590	79%
			大類：Mathematics 小類：Modeling and Simulation	Q2	92 / 303	69%
			大類：Mathematics 小類：General Biochemistry, Genetics and Molecular Biology	Q2	76 / 204	62%

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