ライフサイエンスコーパス: systems biology

1 n profiles is still an enormous challenge in systems biology.

2 broadly applicable across developmental and systems biology.

3 nk between evolution, molecular biology, and systems biology.

4 aluable resource for genetics, genomics, and systems biology.

5 tructured framework represent a challenge in systems biology.

6 iles has been one of the grand challenges in systems biology.

7 ny fields, including biomedical research and systems biology.

8 ion is a fundamental problem in evolutionary systems biology.

9 s via signal transduction is a core focus in systems biology.

10 m insufficient understanding of the strain's systems biology.

11 ecome a part of the routine data analysis in systems biology.

12 ematical models in biology is referred to as systems biology.

13 teractions constitutes a major bottleneck in systems biology.

14 ive and an ongoing challenge in the field of systems biology.

15 y, medical informatics, cancer genomics, and systems biology.

16 etworks are routinely used for prediction in systems biology.

17 rocesses, as well as cell-scale processes in systems biology.

18 ways and their crosstalk is a cornerstone of systems biology.

19 -throughput data is a challenging problem in systems biology.

20 Networks have become ubiquitous in systems biology.

21 on in silico, with promising applications in systems biology.

22 sion and interaction data is a major goal of systems biology.

23 of scientific exploration at the frontier of systems biology.

24 modeling and analysis techniques employed in systems biology.

25 interactions (PPIs) is a central problem in systems biology.

26 on prospective prediction using the tools of systems biology.

27 topology is one of the fundamental goals of systems biology.

28 bi and NYU New York Centers for Genomics and Systems Biology.

29 ing data into informational networks used in systems biology.

30 isualization library particularly suited for systems biology.

31 Ns) is an important but difficult problem in systems biology.

32 chemistry, molecular genetics, genomics, and systems biology.

33 d on developmental biology and computational systems biology.

34 ugh applying these techniques to omics data, systems biology addresses the problems posed by the comp

35 Cancer systems biology aims to understand cancer as an integrat

36 , and DLBCL patient serum samples leveraging systems biology analyses and droplet digital PCR (ddPCR)

37 Subsequent experiments and systems biology analyses confirm this prediction, and su

38 Systems biology analyses demonstrated that inflammatory

39 Systems biology analyses highlight both similarities and

40 Systems biology analyses identified multiple functional

41 ntellectual disability and CHD by conducting systems biology analyses of genes prioritized by mTADA.

42 rchers to study cellular senescence, and our systems biology analyses reveal new insights and gene re

43 entified from large-scale bioinformatics and systems biology analyses.

44 Systems biology analysis of 110 proteins with Benjamini-

45 Integrated systems biology analysis of transcriptomic, proteomic an

46 Systems biology analysis relating to cell behaviors and

47 RNA-seq and systems biology analysis revealed a striking sexual dimo

48 ene expression studies in conjunction with a systems biology analysis.

49 reveal these causes, we used a multi-omics, systems biology analytical approach using biomedical pro

50 to overcome target-based bottlenecks through systems biology analytics, such as protein-protein inter

51 The combination of systems biology and bioinformatics approaches, together

52 g genomics, molecular biology, biochemistry, systems biology and bioinformatics.

53 basic research, genome analysis, modelling, systems biology and education.

54 uracy on numeric and real data examples from systems biology and epidemiology.

55 nvaluable for our understanding of molecular systems biology and for characterizing novel gene functi

56 advances in the fields of computational and systems biology and highlight opportunities for research

57 glected diseases, especially in the areas of systems biology and immunology; ecology, evolution, and

58 Systems biology and innovative data integration can prov

59 pics in bioinformatics, medical informatics, systems biology and intelligent computing.

60 ted dimensionality will catalyze advances in systems biology and medical diagnostics.

61 odels (GSMMs) are increasingly important for systems biology and metabolic engineering research as th

62 ill be of great use to the wider biological, systems biology and modelling communities.

63 existing knowledge of COPD pathobiology, how systems biology and network medicine can improve underst

64 formation that existing technology provides (systems biology and network medicine) so diagnosis, stra

65 use of large linear and nonlinear models in systems biology and other applications involving multisc

66 Systems biology and synthetic biology are increasingly u

67 ynamics has been one of the core subjects in systems biology, and is the focus of this study.

68 egrated shotgun redox proteomics, structural systems biology, and machine learning to resolve propert

69 he perspectives of digital and analog logic, systems biology, and metabolic engineering, three areas

70 Metabolomics plays a pivotal role in systems biology, and NMR is a central tool with high pre

71 tion, gene regulatory networks, modeling and systems biology, and synthetic biology.

72 advances in genomics, molecular biology, and systems biology, and will continue to accelerate as acce

73 investigators to develop their own in silico systems biology applications.

74 exist, but have so far rarely been tested in systems biology applications.

75 se-related peripheral immune signatures in a systems biology approach covering a broad range of adapt

76 We therefore developed InFlo, a novel systems biology approach for characterizing complex biol

77 he promise of high-throughput "-omics"-based systems biology approach in providing greater insight to

78 ate tumor growth and provides a framework of systems biology approach in studying tumor-related immun

79 Walsh et al. took a systems biology approach integrating computational, in v

80 Moreover, such a systems biology approach may enable restoration of the p

81 entering the era of personalized medicine, a systems biology approach merging the numerous clinical p

82 Our systems biology approach offers a possible explanation f

83 both venous and arterial thrombosis, a Blood Systems Biology approach should provide metrics for rate

84 ly SIV infected AGMs and RMs, we developed a systems biology approach termed Conserved Gene Signature

85 Here, we present a systems biology approach that combines transcriptomic an

86 Metabolomics is a powerful systems biology approach that monitors changes in biomol

87 effort and propose research priorities for a systems biology approach to CF lung disease.

88 Here, we used a systems biology approach to characterize the temperature

89 study, the authors use for the first time a systems biology approach to comprehensively evaluate cli

90 Here, we use a systems biology approach to construct a mathematical mod

91 ators of the immune system, and so we used a systems biology approach to construct an miRNA regulator

92 Here we utilized a systems biology approach to decipher the regulatory prin

93 Here, we use a systems biology approach to define the contributions of

94 We employed a systems biology approach to delineate upper-airway gene

95 ide transcriptomics analysis combined with a systems biology approach to determine the molecular sign

96 We therefore utilized a systems biology approach to elucidate how UBE3A loss imp

97 Here, we apply a multistage, systems biology approach to elucidate the disease mechan

98 Here, we applied a systems biology approach to gain deeper insights into th

99 sults support the usefulness of integrative, systems biology approach to gain insights into complex n

100 We used a systems biology approach to identify host transcriptiona

101 Here, we delineate a framework for a systems biology approach to infectious disease in three

102 Here, we employ a systems biology approach to model auxin transport based

103 Using a systems biology approach to prioritize potential points

104 Here, we applied a systems biology approach to study immune responses in su

105 A systems biology approach was developed in tomato (Solanu

106 Here, a novel multi-tiered systems biology approach was used to predict metabolites

107 To address this challenge, a systems biology approach was used.

108 To gain insight into this we undertook a systems biology approach, aiming to generate a Boolean m

109 multi-cellular systems using an integrative systems biology approach, better understanding of protei

110 esenchymal subtype of ovarian cancer using a systems biology approach, involving a variety of molecul

111 oRNA-124 (miR-124), determined with a tiered systems biology approach, is responsible for increased e

112 Recently, a systems biology approach, using omics datasets, has reve

113 Using an integrative systems biology approach, we also revealed that M2 polar

114 Using a data-driven systems biology approach, we built a MB-specific interac

115 Using an integrative systems biology approach, we identified signals of treat

116 Using a systems biology approach, we identify a new network of z

117 Using a systems biology approach, we interrogated the AR-regulat

118 r a combination of three common POPs using a systems biology approach, which may link POP exposure to

119 ticosteroid response in asthma using a novel systems biology approach.

120 on sources was analysed, using an integrated systems biology approach.

121 cell biology, and computational biology - a systems biology approach.

122 Here we applied an unbiased systems-biology approach to explore the cellular specifi

123 We thus finally discuss the potential of systems-biology approach to predict its occurrence and t

124 We conclude by discussing how systems biology approaches are a fruitful avenue for add

125 Systems biology approaches are establishing the links be

126 In this Analysis, we survey six major systems biology approaches for mapping and modelling can

127 ell as in integrative analytical strategies, systems biology approaches for the study of infectious d

128 Combining integrative genomics and systems biology approaches has revealed new and conserve

129 cent advances of multiomics technologies and systems biology approaches have generated large-scale he

130 Bioinformatics and systems biology approaches identified potential pathways

131 Systems biology approaches identify numerous master regu

132 In parallel, bioinformatics and systems biology approaches including genomic analysis, c

133 l cell biology remain relatively unexplored, systems biology approaches like mass spectrometry (MS) b

134 es, cell types, and organ systems, rendering systems biology approaches particularly amenable to thei

135 Systems biology approaches relying on gene ontologies, g

136 oaches that identify key organism traits and systems biology approaches that integrate traditional ph

137 iple model is an important step in employing systems biology approaches to analyze an intracellular s

138 rgets; and (4) to further the development of systems biology approaches to decipher the molecular mec

139 We used systems biology approaches to explore the alternative hy

140 We use systems biology approaches to identify a unique IPF prot

141 e performed RNA-sequencing analyses and used systems biology approaches to identify pathways that are

142 ing hepatocytes, we applied state-of-the-art systems biology approaches to models of liver regenerati

143 validation must still accompany these novel systems biology approaches to realize their full potenti

144 In this Pulmonary Perspective, we discuss systems biology approaches, especially but not limited t

145 grating specialized shear-stress models with systems biology approaches, including transcriptome, met

146 With systems biology approaches, interventions can be tailore

147 nt of biomarkers of BBB integrity along with systems biology approaches, should enable new personaliz

148 metabolic modeling and other nowadays common systems biology approaches-allowed them to anticipate th

149 serial integrated metabolomic and proteomic systems biology approaches.

150 ia has not been characterized extensively by systems biology approaches.

151 These data were deconvoluted using three systems biology approaches: "Orbital-deconvolution" eluc

152 challenges and opportunities in translating systems-biology approaches from cultured cells to living

153 Systems-biology approaches in immunology take various fo

154 Recent advances in systems biology are changing what is possible, however,

155 In this review, the principles of systems biology are described, and two different types o

156 us, emerging tools in big data analytics and systems biology are facilitating novel insights on glyca

157 metagenomics, proteomics, metabolomics, and systems biology are providing a new emphasis in research

158 The present review advocates single cell systems biology as the optimal level of analysis for rem

159 arch, especially the study on organism-level systems biology at multiple levels.

160 Using a systems biology-based approach to an assessment of 779 p

161 A systems biology-based approach would help to decipher th

162 Systems biology-based approaches can provide an unpreced

163 roarray measurements forms a core element of systems biology-based phenotyping.

164 enome annotation, genome analysis, modeling, systems biology, basic research and education.

165 edicine) have the potential to revolutionize systems biology by enabling researchers to study interac

166 oncept of network motifs was introduced into Systems Biology by Milo, Alon and colleagues in 2002, qu

167 Advances in systems biology can be exploited to comprehensively unde

168 Finally, the ways that quantitative and systems biology can help shed light on the plethora of o

169 Systems biology can unravel complex biology but has not

170 d pharmacological screens with an integrated systems-biology characterization.

171 oles of quantitative proteomics in molecular systems biology, clinical research and personalized medi

172 the key motivation behind recent worksin the systems biology community to employDNNs to solve importa

173 ata, Operating procedures and Models for the Systems Biology community.

174 alance analysis (FBA), has become a standard systems-biology computational method to study cellular m

175 istic approach that integrates synthetic and systems biology concepts to achieve outcomes not possibl

176 Our systems biology data analysis, in combination with real-

177 tegrative analysis of experimental molecular systems biology data and quantitative prediction of phys

178 Integrated systems biology-directed analyses of these data layers a

179 , immunology, metabolism, endocrinology, and systems biology discussed genetic and environmental dete

180 eflecting the common belief that cancer is a systems biology disease.

181 , evolutionary mechanisms, neurobiology, and systems biology each has its ancestry in developmental b

182 ts that enabled genome-scale investigations, systems biology emerged as a field aiming to understand

183 We thus demonstrate that structural systems biology enables a proteome-wide, computational a

184 Finally, the application of systems biology for analyzing global regulatory structur

185 Approach in a Minority Community Integrating Systems-Biology for Promotion of Health [FAMILIA]; NCT02

186 Approach in a Minority Community Integrating Systems-Biology for Promotion of Health) study is a clus

187 individual enzymatic components, is to use a systems biology framework, such as a Genome-Scale Metabo

188 e two leads to the formation of a structural systems biology framework, which we have used to analyze

189 red five novel plant defense players using a systems biology-fueled top-to-bottom approach and demons

190 directing towards biological methods such as systems biology, genetic engineering and bio-refining fo

191 at differ in size, complexity and the use of Systems Biology Graphical Notation (SBGN) standards.

192 mathematical models were produced using the Systems Biology Graphical Notation and Systems Biology M

193 The field of systems biology has been rapidly developing in the past

194 Systems biology has been used to examine a range of micr

195 In turn, multitissue multiomics systems biology has emerged to comprehensively elucidate

196 Systems biology has matured as a discipline, and its met

197 Recent work in ecology, climate, and systems biology has shown that slowing down of recovery

198 Research in systems biology has taken advantage of these opportuniti

199 Polyomics, big data, and systems biology have demonstrated a profound complexity

200 merous approaches in functional genomics and systems biology have led to a greater understanding of p

201 ovative technologies, such as proteomics and systems biology, help to advance this research field and

202 Finally, systems biology identified unique metabolic versatility

203 contend that such successful applications of systems biology in elucidating the functional architectu

204 we offer our viewpoint of past successes of systems biology in elucidating the otherwise hidden acti

205 The growing interest in systems biology in executable models and their analysis

206 The power of systems biology in retrieving novel insights and formula

207 We have developed a new systems-biology-informed deep learning algorithm that in

208 We also discuss systems biology insights gleaned from the recent advance

209 tocols, interlink them in the context of the systems biology investigations that produced them, and t

210 Systems biology is increasingly being applied in nanosaf

211 Developmental systems biology is poised to exploit large-scale data fr

212 A grand challenge of systems biology is to predict the kinetic responses of l

213 An important and yet challenging task in systems biology is to reconstruct cellular signaling sys

214 One of the central tasks in systems biology is to understand how cells regulate thei

215 The goal of genomics and systems biology is to understand how complex systems of

216 A key goal of cancer systems biology is to use big data to elucidate the mole

217 In systems biology, it is of great interest to identify pre

218 w research tool in trauma by using metabolic systems biology, laying the foundation for personalized

219 col offers the ability to study tissues at a systems biology level and directly linking results to ti

220 es our understanding of pathogenicity at the systems biology level and provides enticing prospects fo

221 Systems biology maps also identify previously underappre

222 Our sample of 2,070 systems biology maps captures all literature-curated can

223 Boolean models in a standard output format, Systems Biology Marked Up Language-qualitative (SBML-qua

224 A community standard such as Systems Biology Markup Language (SBML) can serve as a ne

225 The input is based on systems biology markup language (SBML) format, which is

226 how query results are converted back to the Systems Biology Markup Language (SBML) standard format.

227 on system behaviour for models specified in Systems Biology Markup Language (SBML).

228 Any genome-scale model based on the Systems Biology Markup Language can be uploaded to the t

229 h and retrieval of parameter values from the Systems Biology Markup Language models stored in BioMode

230 g the Systems Biology Graphical Notation and Systems Biology Markup Language, respectively.

231 To help overcome this complexity, systems biology mathematical models have been generated

232 tors, some questions are best addressed with systems biology mathematical models.

233 Integrating epigenetics into systems biology may critically enhance research on psych

234 This knowledge can be obtained with systems biology/medicine approaches that account for the

235 Here we use a data-driven systems biology meta-analytical approach across three hu

236 Here, systems biology method of dynamics correlation network b

237 as a fertile platform for the application of systems biology methodologies in combination with tradit

238 then expand on the potential applications of systems biology methods to study complex systems, within

239 Here, we build on our previous systems biology model of VEGF transport and kinetics in

240 h to infer the structure and parameters of a systems biology model.

241 s a theoretical foundation for circuit-based systems biology modeling.

242 nable biological insights through optimizing systems biology modeling.

243 ls Parameters will be a valuable service for systems biology modellers.

244 Systems biology models are often characterised as either

245 Systems biology models are used to understand complex bi

246 One of the major bottlenecks in building systems biology models is identification and estimation

247 Systems Biology models reveal relationships between sign

248 f simplifying assumptions is crucial to make systems biology models tractable while still representat

249 These methods combine systems biology models with population and single-cell q

250 used to perform Bayesian analysis of complex systems biology models.

251 eful to analyse complex and high dimensional Systems Biology models.

252 In the era of systems biology, multi-target pharmacological strategies

253 nce insights into the structural biology and systems biology of cell signaling.

254 rom DNA, with important implications for the systems biology of gene regulation by Fis.

255 he model provides opportunities to study the systems biology of innate immunity and to determine how

256 Moreover, it contributes to the systems biology of natural variation, as a substantial n

257 particular, we have focussed on analysing a systems biology of the brain using both simulated and me

258 Existing meta-analysis approaches in systems biology often focus on hypothesis testing and ne

259 orts spread across Europe were combined with systems biology (omics, IgE measurement using microarray

260 ferentiation/development, tumorigenesis, and systems biology on a global, genome-wide level.

261 clustering approach taken from genomics and systems biology on two large independent cognitive datas

262 y is the next great chapter in synthetic and systems biology, particularly for microorganisms.

263 approaches for DTI prediction that adopt the systems biology perspective generally exploit the ration

264 This work provided a systems biology perspective on understanding the antibio

265 harmacokinetics/pharmacodynamics (PK/PD) and systems biology/pharmacology.

266 mational spatial analytics computational and systems biology platform (SpAn) that predicts clinical o

267 on known genetic factors in NAFLD to build a systems biology prediction model that includes functiona

268 s control many biological processes, so that systems biology provides a valuable approach in this fie

269 Modern systems biology requires extensive, carefully curated me

270 es a complex immune response that requires a systems biology research approach.

271 A big challenge in current systems biology research arises when different types of

272 ccessible repository for storing and sharing systems biology research assets.

273 ome these limitations would fill the void in systems biology research, catalyze clinical innovations,

274 ty between formats is a recurring problem in systems biology research.

275 derived biological models and computer-based systems biology simulations.

276 rmulated in terms of a well-known concept in systems biology, statistics, and control theory-that of

277 Our approach promotes a comprehensive systems biology strategy for the exploitation of high-th

278 Recent efforts in systems biology studies of infectious diseases have resu

279 analysis of much larger datasets obtained by systems biology studies on a genomic scale.

280 ing the reproducibility and impact of cancer systems biology studies will require widespread method a

281 e common in modern datasets, particularly in systems biology studies.

282 We conducted a prospective systems biology study of children who differed in their

283 f a large multicenter cohort (BIOSTAT-CHF [A Systems Biology Study to Tailored Treatment in Chronic H

284 ghput microbial genomic sequencing and other systems biology techniques have given novel insight into

285 of limb tendons in adult mice and rats using systems biology techniques.

286 ic dynamical models such as those studied in systems biology there is currently a great need for both

287 c, epigenetic, and endophenotype traits with systems biology to annotate genetic variants, and to fac

288 become one of the most challenging tasks in systems biology to automatically identify protein comple

289 As TGFbeta signalling is complex, we used systems biology to combine experimental and computationa

290 applied to de novo pathway discovery and in systems biology to discover underground metabolism due t

291 bled the fields of metabolic engineering and systems biology to make great strides in interrogating c

292 We have developed a computational systems biology tool, DrugComboExplorer, to identify dri

293 Using the synthetic and systems biology toolbox, this plug-and-play biosynthetic

294 diators of epithelial dysfunction using both systems biology tools and causality-driven laboratory ex

295 h-dimensional data and computation - defines systems biology, typically accompanied by some notion of

296 need to train students in computational and systems biology using research-grade technologies.

297 Taking a systems biology view we show how genetic and epigenetic

298 is an important and widely used algorithm in systems biology, with applications in protein function p

299 nstrate how EnsembleFBA can be included in a systems biology workflow by predicting essential genes i

300 Here we describe a systems biology workflow employing plate-based sample pr