AIMC Topic: Saccharomyces cerevisiae
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Democratized image analytics by visual programming through integration of deep models and small-scale machine learning.
Nature communications
Oct 7, 2019
Analysis of biomedical images requires computational expertize that are uncommon among biomedical scientists. Deep learning approaches for image analysis provide an opportunity to develop user-friendly tools for exploratory data analysis. Here, we us...
Closed-loop cycles of experiment design, execution, and learning accelerate systems biology model development in yeast.
Proceedings of the National Academy of Sciences of the United States of America
Aug 16, 2019
One of the most challenging tasks in modern science is the development of systems biology models: Existing models are often very complex but generally have low predictive performance. The construction of high-fidelity models will require hundreds/tho...
DeepPhagy: a deep learning framework for quantitatively measuring autophagy activity in .
Autophagy
Jun 20, 2019
Seeing is believing. The direct observation of GFP-Atg8 vacuolar delivery under confocal microscopy is one of the most useful end-point measurements for monitoring yeast macroautophagy/autophagy. However, manually labelling individual cells from larg...
Prosit: proteome-wide prediction of peptide tandem mass spectra by deep learning.
Nature methods
May 27, 2019
In mass-spectrometry-based proteomics, the identification and quantification of peptides and proteins heavily rely on sequence database searching or spectral library matching. The lack of accurate predictive models for fragment ion intensities impair...
Identification of D Modification Sites by Integrating Heterogeneous Features in .
Molecules (Basel, Switzerland)
Jan 22, 2019
As an abundant post-transcriptional modification, dihydrouridine (D) has been found in transfer RNA (tRNA) from bacteria, eukaryotes, and archaea. Nonetheless, knowledge of the exact biochemical roles of dihydrouridine in mediating tRNA function is s...
Application of deep convolutional neural networks in classification of protein subcellular localization with microscopy images.
Genetic epidemiology
Jan 4, 2019
Single-cell microscopy image analysis has proved invaluable in protein subcellular localization for inferring gene/protein function. Fluorescent-tagged proteins across cellular compartments are tracked and imaged in response to genetic or environment...
Physicochemical property based computational scheme for classifying DNA sequence elements of Saccharomyces cerevisiae.
Computational biology and chemistry
Dec 26, 2018
GenerationE of huge "omics" data necessitates the development and application of computational methods to annotate the data in terms of biological features. In the context of DNA sequence, it is important to unravel the hidden physicochemical signatu...
Prediction of protein self-interactions using stacked long short-term memory from protein sequences information.
BMC systems biology
Dec 21, 2018
BACKGROUND: Self-interacting Proteins (SIPs) plays a critical role in a series of life function in most living cells. Researches on SIPs are important part of molecular biology. Although numerous SIPs data be provided, traditional experimental method...
Deep learning architectures for prediction of nucleosome positioning from sequences data.
BMC bioinformatics
Nov 20, 2018
BACKGROUND: Nucleosomes are DNA-histone complex, each wrapping about 150 pairs of double-stranded DNA. Their function is fundamental for one of the primary functions of Chromatin i.e. packing the DNA into the nucleus of the Eukaryote cells. Several b...
Protein function prediction from protein-protein interaction network using gene ontology based neighborhood analysis and physico-chemical features.
Journal of bioinformatics and computational biology
Sep 19, 2018
Protein Function Prediction from Protein-Protein Interaction Network (PPIN) and physico-chemical features using the Gene Ontology (GO) classification are indeed very useful for assigning biological or biochemical functions to a protein. They also lea...
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