Connecting omics signatures and revealing biological mechanisms with iLINCS
There are only a few platforms that integrate multiple omics data types, bioinformatics tools,
and interfaces for integrative analyses and visualization that do not require programming …
and interfaces for integrative analyses and visualization that do not require programming …
[HTML][HTML] Digital microbiology
Background Digitalization and artificial intelligence have an important impact on the way
microbiology laboratories will work in the near future. Opportunities and challenges lie …
microbiology laboratories will work in the near future. Opportunities and challenges lie …
Path to improving the life cycle and quality of genome-scale models of metabolism
Y Seif, BØ Palsson - Cell systems, 2021 - cell.com
Genome-scale models of metabolism (GEMs) are key computational tools for the systems-
level study of metabolic networks. Here, we describe the" GEM life cycle," which we …
level study of metabolic networks. Here, we describe the" GEM life cycle," which we …
A mechanism-aware and multiomic machine-learning pipeline characterizes yeast cell growth
Metabolic modeling and machine learning are key components in the emerging next
generation of systems and synthetic biology tools, targeting the genotype–phenotype …
generation of systems and synthetic biology tools, targeting the genotype–phenotype …
Integrating genome-scale metabolic modelling and transfer learning for human gene regulatory network reconstruction
Motivation Gene regulation is responsible for controlling numerous physiological functions
and dynamically responding to environmental fluctuations. Reconstructing the human …
and dynamically responding to environmental fluctuations. Reconstructing the human …
Connecting omics signatures of diseases, drugs, and mechanisms of actions with iLINCS
There are only a few platforms that integrate multiple omics data types, bioinformatics tools,
and interfaces for integrative analyses and visualization that do not require programming …
and interfaces for integrative analyses and visualization that do not require programming …
Deep learning prediction of adverse drug reactions in drug discovery using open TG–GATEs and FAERS databases
Machine learning techniques are being increasingly used in the analysis of clinical and
omics data. This increase is primarily due to the advancements in Artificial intelligence (AI) …
omics data. This increase is primarily due to the advancements in Artificial intelligence (AI) …
[HTML][HTML] Clinical stratification improves the diagnostic accuracy of small omics datasets within machine learning and genome-scale metabolic modelling methods
G Magazzù, G Zampieri, C Angione - Computers in Biology and Medicine, 2022 - Elsevier
Background: Recently, multi-omic machine learning architectures have been proposed for
the early detection of cancer. However, for rare cancers and their associated small datasets …
the early detection of cancer. However, for rare cancers and their associated small datasets …
Whole‐genome sequencing and genome‐scale metabolic modeling of Chromohalobacter canadensis 85B to explore its salt tolerance and biotechnological use
Salt tolerant organisms are increasingly being used for the industrial production of high‐
value biomolecules due to their better adaptability compared to mesophiles …
value biomolecules due to their better adaptability compared to mesophiles …
[HTML][HTML] Towards a hybrid model-driven platform based on flux balance analysis and a machine learning pipeline for biosystem design
D Wu, F Xu, Y Xu, M Huang, Z Li, J Chu - Synthetic and Systems …, 2024 - Elsevier
Metabolic modeling and machine learning (ML) are crucial components of the evolving next-
generation tools in systems and synthetic biology, aiming to unravel the intricate relationship …
generation tools in systems and synthetic biology, aiming to unravel the intricate relationship …