scholarly article | Q13442814 |
P2093 | author name string | Robert Haas | |
Markus Ralser | |||
Aleksej Zelezniak | |||
StJohn Townsend | |||
Jacopo Iacovacci | |||
Stephan Kamrad | |||
P2860 | cites work | The metabolic background is a global player in Saccharomyces gene expression epistasis | Q42051779 |
Absolute quantification of transcription factors during cellular differentiation using multiplexed targeted proteomics | Q42823216 | ||
Single-cell transcriptomics for microbial eukaryotes | Q46805607 | ||
Global analysis of protein structural changes in complex proteomes | Q48270002 | ||
Global network reorganization during dynamic adaptations of Bacillus subtilis metabolism | Q51411191 | ||
Queueing phase transition: theory of translation | Q51821336 | ||
Sulfate radicals enable a non-enzymatic Krebs cycle precursor. | Q54908927 | ||
The structure and dynamics of multilayer networks | Q56601122 | ||
Mass spectrometry-based proteomics: principles, perspectives, and challenges | Q82202672 | ||
Central Dogma of Molecular Biology | Q22122430 | ||
Finishing the euchromatic sequence of the human genome | Q22122488 | ||
Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network | Q24561581 | ||
Ionomics: studying the social network of mineral nutrients | Q24657885 | ||
Scale-free flow of life: on the biology, economics, and physics of the cell | Q24658445 | ||
Deep learning for computational biology | Q26740441 | ||
Plant Ionomics: From Elemental Profiling to Environmental Adaptation | Q26747087 | ||
Dynamics in Transcriptomics: Advancements in RNA-seq Time Course and Downstream Analysis | Q26782685 | ||
Epigenomics and the structure of the living genome | Q26782740 | ||
Defining the metabolome: size, flux, and regulation | Q26865192 | ||
Growth control of the eukaryote cell: a systems biology study in yeast | Q27499776 | ||
Single cell transcriptomics: methods and applications | Q27690585 | ||
Network biology: understanding the cell's functional organization | Q27861027 | ||
Dynamic phosphoproteomics reveals TORC1-dependent regulation of yeast nucleotide and amino acid biosynthesis | Q27934628 | ||
Emerging concepts of epigenetic dysregulation in hematological malignancies | Q28074522 | ||
The large-scale organization of metabolic networks | Q29547498 | ||
A community-driven global reconstruction of human metabolism. | Q29614690 | ||
A global view of gene activity and alternative splicing by deep sequencing of the human transcriptome | Q29615877 | ||
Integration of metabolomics, lipidomics and clinical data using a machine learning method | Q30398446 | ||
A database of Caenorhabditis elegans behavioral phenotypes | Q30574505 | ||
Methods for the integration of multi-omics data: mathematical aspects | Q31040819 | ||
The Perseus computational platform for comprehensive analysis of (prote)omics data | Q31110953 | ||
Integrative analysis of human omics data using biomolecular networks | Q31120979 | ||
Simultaneous analysis of distinct Omics data sets with integration of biological knowledge: Multiple Factor Analysis approach | Q33401075 | ||
Personalized Nutrition by Prediction of Glycemic Responses. | Q33445995 | ||
Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis | Q33503982 | ||
How to visually interpret biological data using networks | Q33509500 | ||
Nutrient control of eukaryote cell growth: a systems biology study in yeast | Q33585808 | ||
Systems-level analysis of mechanisms regulating yeast metabolic flux | Q33629330 | ||
Combining inferred regulatory and reconstructed metabolic networks enhances phenotype prediction in yeast | Q33752043 | ||
Uncovering transcriptional regulation of metabolism by using metabolic network topology | Q33863545 | ||
Nonenzymatic gluconeogenesis-like formation of fructose 1,6-bisphosphate in ice. | Q33913600 | ||
Single-cell genome sequencing: current state of the science | Q34045945 | ||
A novel and versatile computational tool to model translation. | Q34082389 | ||
Phenomics: the next challenge | Q34150453 | ||
Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods | Q34257056 | ||
Deep learning in neural networks: an overview | Q34451415 | ||
High-resolution genome-wide scan of genes, gene-networks and cellular systems impacting the yeast ionome | Q34476697 | ||
Coming of age: ten years of next-generation sequencing technologies | Q34526801 | ||
Estimating binding properties of transcription factors from genome-wide binding profiles | Q34883141 | ||
Chemical methods for the proteome-wide identification of posttranslationally modified proteins | Q35019528 | ||
Contribution of network connectivity in determining the relationship between gene expression and metabolite concentration changes | Q35156770 | ||
The Mitochondrial Proteome and Human Disease | Q35427460 | ||
Functional genomics and proteomics as a foundation for systems biology | Q35829000 | ||
Systematic Analysis of Transcriptional and Post-transcriptional Regulation of Metabolism in Yeast | Q36245407 | ||
Detection of gene communities in multi-networks reveals cancer drivers | Q36350132 | ||
Normalizing single-cell RNA sequencing data: challenges and opportunities | Q36371785 | ||
Chemical Methods for Encoding and Decoding of Posttranslational Modifications | Q36654176 | ||
Sequencing the yeast genome: an international achievement | Q36703344 | ||
Genome-wide approaches to studying chromatin modifications | Q37077306 | ||
Ionomics and the study of the plant ionome. | Q37077679 | ||
Scan-o-matic: High-Resolution Microbial Phenomics at a Massive Scale. | Q37242091 | ||
Predictive biophysical modeling and understanding of the dynamics of mRNA translation and its evolution. | Q37401102 | ||
Mitochondrial protein functions elucidated by multi-omic mass spectrometry profiling. | Q37401998 | ||
Mass spectrometry-based proteomics and network biology | Q37996108 | ||
Plant proteomics: current status and future prospects | Q38077394 | ||
Metabolomics for laboratory diagnostics | Q38314001 | ||
Metabolomics in pharmaceutical research and development | Q38434187 | ||
Protein function prediction using guilty by association from interaction networks. | Q38460064 | ||
Machine learning applications in genetics and genomics | Q38463061 | ||
Phenomics for photosynthesis, growth and reflectance in Arabidopsis thaliana reveals circadian and long-term fluctuations in heritability. | Q38487610 | ||
Quantitative proteomics: challenges and opportunities in basic and applied research | Q38676266 | ||
Trans-Omics: How To Reconstruct Biochemical Networks Across Multiple 'Omic' Layers | Q38710125 | ||
Proteome complexity and the forces that drive proteome imbalance. | Q38954697 | ||
Inference and Prediction of Metabolic Network Fluxes | Q38961555 | ||
The Mouse Genomes Project: a repository of inbred laboratory mouse strain genomes | Q38989970 | ||
Integrating quantitative proteomics and metabolomics with a genome-scale metabolic network model | Q39866153 | ||
The potential of single-cell profiling in plants | Q39869473 | ||
Functional Metabolomics Describes the Yeast Biosynthetic Regulome | Q41367252 | ||
Genomewide landscape of gene-metabolome associations in Escherichia coli | Q41967441 | ||
P304 | page(s) | 37-45 | |
P577 | publication date | 2017-12-01 | |
P1433 | published in | Current Opinion in Systems Biology | Q53952949 |
P1476 | title | Designing and interpreting 'multi-omic' experiments that may change our understanding of biology | |
P478 | volume | 6 |
Q55038642 | Cost-effective generation of precise label-free quantitative proteomes in high-throughput by microLC and data-independent acquisition. |
Q54204592 | Escherichia coli as a host for metabolic engineering |
Q101157747 | Extraction and Integration of Genetic Networks from Short-Profile Omic Data Sets |
Q63383919 | Identification of bioactive metabolites using activity metabolomics |
Q63351951 | Systems Biology and Multi-Omics Integration: Viewpoints from the Metabolomics Research Community. |
Search more.