AIMC Topic: Transcriptome
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Application of Machine Learning in Developing a Novelty Five-Pseudogene Signature to Predict Prognosis of Head and Neck Squamous Cell Carcinoma: A New Aspect of "Junk Genes" in Biomedical Practice.
DNA and cell biology
Feb 11, 2020
Head and neck squamous cell carcinoma (HNSCC) is the sixth malignancy, which is characterized by poor prognosis or high mortality because of the lack of predicting markers. Aberrant cancer pseudogenes have been found predictive for prognosis. We aim ...
Investigating the gene expression profiles of cells in seven embryonic stages with machine learning algorithms.
Genomics
Feb 8, 2020
The development of embryonic cells involves several continuous stages, and some genes are related to embryogenesis. To date, few studies have systematically investigated changes in gene expression profiles during mammalian embryogenesis. In this stud...
scAI: an unsupervised approach for the integrative analysis of parallel single-cell transcriptomic and epigenomic profiles.
Genome biology
Feb 3, 2020
Simultaneous measurements of transcriptomic and epigenomic profiles in the same individual cells provide an unprecedented opportunity to understand cell fates. However, effective approaches for the integrative analysis of such data are lacking. Here,...
Machine Learning and Network Analyses Reveal Disease Subtypes of Pancreatic Cancer and their Molecular Characteristics.
Scientific reports
Jan 27, 2020
Given that the biological processes governing the oncogenesis of pancreatic cancers could present useful therapeutic targets, there is a pressing need to molecularly distinguish between different clinically relevant pancreatic cancer subtypes. To add...
Genome-scale transcriptional dynamics and environmental biosensing.
Proceedings of the National Academy of Sciences of the United States of America
Jan 23, 2020
Genome-scale technologies have enabled mapping of the complex molecular networks that govern cellular behavior. An emerging theme in the analyses of these networks is that cells use many layers of regulatory feedback to constantly assess and precisel...
Identification and transfer of spatial transcriptomics signatures for cancer diagnosis.
Breast cancer research : BCR
Jan 13, 2020
BACKGROUND: Distinguishing ductal carcinoma in situ (DCIS) from invasive ductal carcinoma (IDC) regions in clinical biopsies constitutes a diagnostic challenge. Spatial transcriptomics (ST) is an in situ capturing method, which allows quantification ...
Machine Learning Supports Long Noncoding RNAs as Expression Markers for Endometrial Carcinoma.
BioMed research international
Jan 10, 2020
Uterine corpus endometrial carcinoma (UCEC) is the second most common type of gynecological tumor. Several research studies have recently shown the potential of different ncRNAs as biomarkers for prognostics and diagnosis in different types of cancer...
De novo generation of hit-like molecules from gene expression signatures using artificial intelligence.
Nature communications
Jan 3, 2020
Finding new molecules with a desired biological activity is an extremely difficult task. In this context, artificial intelligence and generative models have been used for molecular de novo design and compound optimization. Herein, we report a generat...
Using transfer learning from prior reference knowledge to improve the clustering of single-cell RNA-Seq data.
Scientific reports
Dec 30, 2019
In many research areas scientists are interested in clustering objects within small datasets while making use of prior knowledge from large reference datasets. We propose a method to apply the machine learning concept of transfer learning to unsuperv...
Joint learning improves protein abundance prediction in cancers.
BMC biology
Dec 23, 2019
BACKGROUND: The classic central dogma in biology is the information flow from DNA to mRNA to protein, yet complicated regulatory mechanisms underlying protein translation often lead to weak correlations between mRNA and protein abundances. This is pa...
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