[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 …
A selective review of multi-level omics data integration using variable selection
High-throughput technologies have been used to generate a large amount of omics data. In
the past, single-level analysis has been extensively conducted where the omics …
the past, single-level analysis has been extensively conducted where the omics …
[HTML][HTML] Proteogenomic characterization of human early-onset gastric cancer
We report proteogenomic analysis of diffuse gastric cancers (GCs) in young populations.
Phosphoproteome data elucidated signaling pathways associated with somatic mutations …
Phosphoproteome data elucidated signaling pathways associated with somatic mutations …
A fully Bayesian latent variable model for integrative clustering analysis of multi-type omics data
Identification of clinically relevant tumor subtypes and omics signatures is an important task
in cancer translational research for precision medicine. Large-scale genomic profiling …
in cancer translational research for precision medicine. Large-scale genomic profiling …
A novel approach for data integration and disease subtyping
Advances in high-throughput technologies allow for measurements of many types of omics
data, yet the meaningful integration of several different data types remains a significant …
data, yet the meaningful integration of several different data types remains a significant …
Integrative subtype discovery in glioblastoma using iCluster
Large-scale cancer genome projects, such as the Cancer Genome Atlas (TCGA) project, are
comprehensive molecular characterization efforts to accelerate our understanding of cancer …
comprehensive molecular characterization efforts to accelerate our understanding of cancer …
Deep-learning approach to identifying cancer subtypes using high-dimensional genomic data
Motivation Cancer subtype classification has the potential to significantly improve disease
prognosis and develop individualized patient management. Existing methods are limited by …
prognosis and develop individualized patient management. Existing methods are limited by …
Evaluation of integrative clustering methods for the analysis of multi-omics data
C Chauvel, A Novoloaca, P Veyre… - Briefings in …, 2020 - academic.oup.com
Recent advances in sequencing, mass spectrometry and cytometry technologies have
enabled researchers to collect large-scale omics data from the same set of biological …
enabled researchers to collect large-scale omics data from the same set of biological …
Multi-omics clustering for cancer subtyping based on latent subspace learning
The increased availability of high-throughput technologies has enabled biomedical
researchers to learn about disease etiology across multiple omics layers, which shows …
researchers to learn about disease etiology across multiple omics layers, which shows …
Review of statistical learning methods in integrated omics studies (an integrated information science)
ISL Zeng, T Lumley - Bioinformatics and biology insights, 2018 - journals.sagepub.com
Integrated omics is becoming a new channel for investigating the complex molecular system
in modern biological science and sets a foundation for systematic learning for precision …
in modern biological science and sets a foundation for systematic learning for precision …