Machine learning meets omics: applications and perspectives
The innovation of biotechnologies has allowed the accumulation of omics data at an
alarming rate, thus introducing the era of 'big data'. Extracting inherent valuable knowledge …
alarming rate, thus introducing the era of 'big data'. Extracting inherent valuable knowledge …
Machine learning applications for mass spectrometry-based metabolomics
The metabolome of an organism depends on environmental factors and intracellular
regulation and provides information about the physiological conditions. Metabolomics helps …
regulation and provides information about the physiological conditions. Metabolomics helps …
Machine learning in bioprocess development: from promise to practice
Fostered by novel analytical techniques, digitalization, and automation, modern bioprocess
development provides large amounts of heterogeneous experimental data, containing …
development provides large amounts of heterogeneous experimental data, containing …
Teasing out missing reactions in genome-scale metabolic networks through hypergraph learning
Abstract GEnome-scale Metabolic models (GEMs) are powerful tools to predict cellular
metabolism and physiological states in living organisms. However, due to our imperfect …
metabolism and physiological states in living organisms. However, due to our imperfect …
[HTML][HTML] Automating the design-build-test-learn cycle towards next-generation bacterial cell factories
Automation is playing an increasingly significant role in synthetic biology. Groundbreaking
technologies, developed over the past 20 years, have enormously accelerated the …
technologies, developed over the past 20 years, have enormously accelerated the …
Deep learning meets metabolomics: a methodological perspective
P Sen, S Lamichhane, VB Mathema… - Briefings in …, 2021 - academic.oup.com
Deep learning (DL), an emerging area of investigation in the fields of machine learning and
artificial intelligence, has markedly advanced over the past years. DL techniques are being …
artificial intelligence, has markedly advanced over the past years. DL techniques are being …
Genome-scale modeling of yeast metabolism: retrospectives and perspectives
Yeasts have been widely used for production of bread, beer and wine, as well as for
production of bioethanol, but they have also been designed as cell factories to produce …
production of bioethanol, but they have also been designed as cell factories to produce …
Machine learning for the advancement of genome-scale metabolic modeling
Constraint-based modeling (CBM) has evolved as the core systems biology tool to map the
interrelations between genotype, phenotype, and external environment. The recent …
interrelations between genotype, phenotype, and external environment. The recent …
[HTML][HTML] Metabolic modelling approaches for describing and engineering microbial communities
B García-Jiménez, J Torres-Bacete… - Computational and …, 2021 - Elsevier
Microbes do not live in isolation but in microbial communities. The relevance of microbial
communities is increasing due to growing awareness of their influence on a huge number of …
communities is increasing due to growing awareness of their influence on a huge number of …
Performance comparison of deep learning autoencoders for cancer subtype detection using multi-omics data
Simple Summary Here, we compared the performance of four different autoencoders:(a)
vanilla,(b) sparse,(c) denoising, and (d) variational for subtype detection on four cancer …
vanilla,(b) sparse,(c) denoising, and (d) variational for subtype detection on four cancer …