AIMC Topic: Transcriptome
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Unveiling the immune infiltrate modulation in cancer and response to immunotherapy by MIXTURE-an enhanced deconvolution method.
Briefings in bioinformatics
Jul 20, 2021
The accurate quantification of tumor-infiltrating immune cells turns crucial to uncover their role in tumor immune escape, to determine patient prognosis and to predict response to immune checkpoint blockade. Current state-of-the-art methods that qua...
Deep-joint-learning analysis model of single cell transcriptome and open chromatin accessibility data.
Briefings in bioinformatics
Jul 20, 2021
Simultaneous profiling transcriptomic and chromatin accessibility information in the same individual cells offers an unprecedented resolution to understand cell states. However, computationally effective methods for the integration of these inherent ...
Machine learning application identifies novel gene signatures from transcriptomic data of spontaneous canine hemangiosarcoma.
Briefings in bioinformatics
Jul 20, 2021
Angiosarcomas are soft-tissue sarcomas that form malignant vascular tissues. Angiosarcomas are very rare, and due to their aggressive behavior and high metastatic propensity, they have poor clinical outcomes. Hemangiosarcomas commonly occur in domest...
Discovery of molecular features underlying the morphological landscape by integrating spatial transcriptomic data with deep features of tissue images.
Nucleic acids research
Jun 4, 2021
Profiling molecular features associated with the morphological landscape of tissue is crucial for investigating the structural and spatial patterns that underlie the biological function of tissues. In this study, we present a new method, spatial gene...
DM3Loc: multi-label mRNA subcellular localization prediction and analysis based on multi-head self-attention mechanism.
Nucleic acids research
May 7, 2021
Subcellular localization of messenger RNAs (mRNAs), as a prevalent mechanism, gives precise and efficient control for the translation process. There is mounting evidence for the important roles of this process in a variety of cellular events. Computa...
Modeling multi-species RNA modification through multi-task curriculum learning.
Nucleic acids research
Apr 19, 2021
N6-methyladenosine (m6A) is the most pervasive modification in eukaryotic mRNAs. Numerous biological processes are regulated by this critical post-transcriptional mark, such as gene expression, RNA stability, RNA structure and translation. Recently, ...
Merged Affinity Network Association Clustering: Joint multi-omic/clinical clustering to identify disease endotypes.
Cell reports
Apr 13, 2021
Although clinical and laboratory data have long been used to guide medical practice, this information is rarely integrated with multi-omic data to identify endotypes. We present Merged Affinity Network Association Clustering (MANAclust), a coding-fre...
Female
Adult
Asthma
Male
Middle Aged
Gene Expression Profiling
Transcriptome
Case-Control Studies
Microbiota
Humans
Allergens
Precision Medicine
Proteomics
Epigenome
Cluster Analysis
Adolescent
Datasets as Topic
Unsupervised Machine Learning
Gene Expression Regulation
Child, Preschool
Benchmarking
Atlases as Topic
Child
Nasal Cavity
Feces
Deep learning in systems medicine.
Briefings in bioinformatics
Mar 22, 2021
Systems medicine (SM) has emerged as a powerful tool for studying the human body at the systems level with the aim of improving our understanding, prevention and treatment of complex diseases. Being able to automatically extract relevant features nee...
Integration and transfer learning of single-cell transcriptomes via cFIT.
Proceedings of the National Academy of Sciences of the United States of America
Mar 9, 2021
Large, comprehensive collections of single-cell RNA sequencing (scRNA-seq) datasets have been generated that allow for the full transcriptional characterization of cell types across a wide variety of biological and clinical conditions. As new methods...
iModulonDB: a knowledgebase of microbial transcriptional regulation derived from machine learning.
Nucleic acids research
Jan 8, 2021
Independent component analysis (ICA) of bacterial transcriptomes has emerged as a powerful tool for obtaining co-regulated, independently-modulated gene sets (iModulons), inferring their activities across a range of conditions, and enabling their ass...
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