AIMC Topic: Yeasts

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Accurate Classification of Protein Subcellular Localization from High-Throughput Microscopy Images Using Deep Learning.

G3 (Bethesda, Md.) May 5, 2017
High-throughput microscopy of many single cells generates high-dimensional data that are far from straightforward to analyze. One important problem is automatically detecting the cellular compartment where a fluorescently-tagged protein resides, a ta...
Machine Learning Proteome Yeasts Fungal Proteins Protein Transport Microscopy, Fluorescence High-Throughput Screening Assays Image Interpretation, Computer-Assisted
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Amino acid composition predicts prion activity.

PLoS computational biology Apr 10, 2017
Many prion-forming proteins contain glutamine/asparagine (Q/N) rich domains, and there are conflicting opinions as to the role of primary sequence in their conversion to the prion form: is this phenomenon driven primarily by amino acid composition, o...
Prions Asparagine Glutamine Humans Machine Learning Computational Biology Amino Acid Sequence Amyloid Yeasts
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Detecting Essential Proteins Based on Network Topology, Gene Expression Data, and Gene Ontology Information.

IEEE/ACM transactions on computational biology and bioinformatics Oct 7, 2016
The identification of essential proteins in protein-protein interaction (PPI) networks is of great significance for understanding cellular processes. With the increasing availability of large-scale PPI data, numerous centrality measures based on netw...
Transcriptome Proteins Molecular Sequence Annotation Gene Ontology Protein Interaction Mapping Databases, Protein Yeasts
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Batch Mode TD($\lambda$ ) for Controlling Partially Observable Gene Regulatory Networks.

IEEE/ACM transactions on computational biology and bioinformatics Jul 28, 2016
External control of gene regulatory networks (GRNs) has received much attention in recent years. The aim is to find a series of actions to apply to a gene regulation system making it avoid its diseased states. In this work, we propose a novel method ...
Computer Simulation Melanoma Gene Regulatory Networks Machine Learning Algorithms Yeasts Computational Biology Least-Squares Analysis Gene Expression Regulation Humans
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Protein-protein interaction inference based on semantic similarity of Gene Ontology terms.

Journal of theoretical biology Apr 23, 2016
Identifying protein-protein interactions is important in molecular biology. Experimental methods to this issue have their limitations, and computational approaches have attracted more and more attentions from the biological community. The semantic si...
Gene Ontology Protein Interaction Mapping Support Vector Machine Protein Interaction Domains and Motifs Humans Yeasts Machine Learning Molecular Sequence Annotation
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Incorporation of negative rules and evolution of a fuzzy controller for yeast fermentation process.

Bioprocess and biosystems engineering Apr 16, 2016
The control of bioprocesses can be very challenging due to the fact that these kinds of processes are highly affected by various sources of uncertainty like the intrinsic behavior of the used microorganisms. Due to the reason that these kinds of proc...
Fuzzy Logic Fermentation Yeasts
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Exploring information from the topology beneath the Gene Ontology terms to improve semantic similarity measures.

Gene Apr 12, 2016
Measuring the similarity between pairs of biological entities is important in molecular biology. The introduction of Gene Ontology (GO) provides us with a promising approach to quantifying the semantic similarity between two genes or gene products. T...
Semantics Yeasts Terminology as Topic Gene Ontology Gene Expression Profiling Proteins Databases, Protein Humans Algorithms
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Vision-based Nano Robotic System for High-throughput Non-embedded Cell Cutting.

Scientific reports Mar 4, 2016
Cell cutting is a significant task in biology study, but the highly productive non-embedded cell cutting is still a big challenge for current techniques. This paper proposes a vision-based nano robotic system and then realizes automatic non-embedded ...
Image Processing, Computer-Assisted Robotics Nanotechnology Yeasts Microscopy, Electron, Scanning Intracellular Space Humans Automation, Laboratory Single-Cell Analysis
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Use of Semisupervised Clustering and Feature-Selection Techniques for Identification of Co-expressed Genes.

IEEE journal of biomedical and health informatics Jul 20, 2015
Studying the patterns hidden in gene-expression data helps to understand the functionality of genes. In general, clustering techniques are widely used for the identification of natural partitionings from the gene expression data. In order to put cons...
Algorithms Yeasts Fuzzy Logic Arabidopsis Humans Animals Cluster Analysis Gene Expression Profiling Supervised Machine Learning Computational Biology Rats Databases, Genetic
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Integrating biological knowledge based on functional annotations for biclustering of gene expression data.

Computer methods and programs in biomedicine Mar 18, 2015
Gene expression data analysis is based on the assumption that co-expressed genes imply co-regulated genes. This assumption is being reformulated because the co-expression of a group of genes may be the result of an independent activation with respect...
Gene Ontology Knowledge Bases Data Mining Databases, Genetic Algorithms Yeasts Cluster Analysis Gene Expression Profiling Genes, Fungal Unsupervised Machine Learning Molecular Sequence Annotation
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