AIMC Topic: Mutation
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Proteomics Analysis of FLT3-ITD Mutation in Acute Myeloid Leukemia Using Deep Learning Neural Network.
Annals of clinical and laboratory science
Jan 1, 2019
Deep Learning can significantly benefit cancer proteomics and genomics. In this study, we attempted to determine a set of critical proteins that were associated with the FLT3-ITD mutation in newly-diagnosed acute myeloid leukemia patients. A Deep Lea...
[Artificial Intelligence for Cancer Genomic Medicine: Understanding Cancer is Beyond Human Ability].
Brain and nerve = Shinkei kenkyu no shinpo
Jan 1, 2019
Cancer is a very complex disease that is caused by mutations in genomes and evolves spatiotemporally in a patient. Our institute implemented IBM Watson for Genomics and realized a turnaround time for patient diagnosis of less than four days that incl...
Combined multivariate analysis and machine learning reveals a predictive module of metabolic stress response in Arabidopsis thaliana.
Molecular omics
Dec 3, 2018
Abiotic stress exposure of plants induces metabolic reprogramming which is tightly regulated by signalling cascades connecting transcriptional with translational and metabolic regulation. Complexity of such interconnected metabolic networks impedes t...
ARIADNA: machine learning method for ancient DNA variant discovery.
DNA research : an international journal for rapid publication of reports on genes and genomes
Dec 1, 2018
Ancient DNA (aDNA) studies often rely on standard methods of mutation calling, optimized for high-quality contemporary DNA but not for excessive contamination, time- or environment-related damage of aDNA. In the absence of validated datasets and desp...
A systematic exploration of [Formula: see text] cutoff ranges in machine learning models for protein mutation stability prediction.
Journal of bioinformatics and computational biology
Oct 1, 2018
Discerning how a mutation affects the stability of a protein is central to the study of a wide range of diseases. Mutagenesis experiments on physical proteins provide precise insights about the effects of amino acid substitutions, but such studies ar...
A machine learning approach for somatic mutation discovery.
Science translational medicine
Sep 5, 2018
Variability in the accuracy of somatic mutation detection may affect the discovery of alterations and the therapeutic management of cancer patients. To address this issue, we developed a somatic mutation discovery approach based on machine learning t...
Cost function network-based design of protein-protein interactions: predicting changes in binding affinity.
Bioinformatics (Oxford, England)
Aug 1, 2018
MOTIVATION: Accurate and economic methods to predict change in protein binding free energy upon mutation are imperative to accelerate the design of proteins for a wide range of applications. Free energy is defined by enthalpic and entropic contributi...
Enhancing fluorescent protein photostability through robot-assisted photobleaching.
Integrative biology : quantitative biosciences from nano to macro
Jul 16, 2018
Improving fluorescent proteins through the use of directed evolution requires robust techniques for screening large libraries of genetic variants. Here we describe an effective and relatively low-cost system for screening libraries of fluorescent pro...
Classifying tumors by supervised network propagation.
Bioinformatics (Oxford, England)
Jul 1, 2018
MOTIVATION: Network propagation has been widely used to aggregate and amplify the effects of tumor mutations using knowledge of molecular interaction networks. However, propagating mutations through interactions irrelevant to cancer leads to erosion ...
Orchid: a novel management, annotation and machine learning framework for analyzing cancer mutations.
Bioinformatics (Oxford, England)
Mar 15, 2018
MOTIVATION: As whole-genome tumor sequence and biological annotation datasets grow in size, number and content, there is an increasing basic science and clinical need for efficient and accurate data management and analysis software. With the emergenc...
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