AIMC Topic: Saccharomyces cerevisiae Proteins

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Protein Abundance Prediction Through Machine Learning Methods.

Journal of molecular biology
Proteins are responsible for most physiological processes, and their abundance provides crucial information for systems biology research. However, absolute protein quantification, as determined by mass spectrometry, still has limitations in capturing...

A Deep Learning Framework for Identifying Essential Proteins by Integrating Multiple Types of Biological Information.

IEEE/ACM transactions on computational biology and bioinformatics
Computational methods including centrality and machine learning-based methods have been proposed to identify essential proteins for understanding the minimum requirements of the survival and evolution of a cell. In centrality methods, researchers are...

Using Weighted Extreme Learning Machine Combined With Scale-Invariant Feature Transform to Predict Protein-Protein Interactions From Protein Evolutionary Information.

IEEE/ACM transactions on computational biology and bioinformatics
Protein-Protein Interactions (PPIs) play an irreplaceable role in biological activities of organisms. Although many high-throughput methods are used to identify PPIs from different kinds of organisms, they have some shortcomings, such as high cost an...

Identification of self-interacting proteins by integrating random projection classifier and finite impulse response filter.

BMC genomics
BACKGROUND: Identification of protein-protein interactions (PPIs) is crucial for understanding biological processes and investigating the cellular functions of genes. Self-interacting proteins (SIPs) are those in which more than two identical protein...

GO2Vec: transforming GO terms and proteins to vector representations via graph embeddings.

BMC genomics
BACKGROUND: Semantic similarity between Gene Ontology (GO) terms is a fundamental measure for many bioinformatics applications, such as determining functional similarity between genes or proteins. Most previous research exploited information content ...

Democratized image analytics by visual programming through integration of deep models and small-scale machine learning.

Nature communications
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...

DeepPhagy: a deep learning framework for quantitatively measuring autophagy activity in .

Autophagy
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...

Prediction of Self-Interacting Proteins from Protein Sequence Information Based on Random Projection Model and Fast Fourier Transform.

International journal of molecular sciences
It is significant for biological cells to predict self-interacting proteins (SIPs) in the field of bioinformatics. SIPs mean that two or more identical proteins can interact with each other by one gene expression. This plays a major role in the evolu...

Application of deep convolutional neural networks in classification of protein subcellular localization with microscopy images.

Genetic epidemiology
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...

Integrating network topology, gene expression data and GO annotation information for protein complex prediction.

Journal of bioinformatics and computational biology
The prediction of protein complexes based on the protein interaction network is a fundamental task for the understanding of cellular life as well as the mechanisms underlying complex disease. A great number of methods have been developed to predict p...