AIMC Topic: Yeasts

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Learning unsupervised feature representations for single cell microscopy images with paired cell inpainting.

PLoS computational biology Sep 3, 2019
Cellular microscopy images contain rich insights about biology. To extract this information, researchers use features, or measurements of the patterns of interest in the images. Here, we introduce a convolutional neural network (CNN) to automatically...
Humans Neural Networks, Computer Unsupervised Machine Learning Cells, Cultured Microscopy Yeasts Single-Cell Analysis Image Processing, Computer-Assisted Computational Biology
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NNVA: Neural Network Assisted Visual Analysis of Yeast Cell Polarization Simulation.

IEEE transactions on visualization and computer graphics Aug 19, 2019
Complex computational models are often designed to simulate real-world physical phenomena in many scientific disciplines. However, these simulation models tend to be computationally very expensive and involve a large number of simulation input parame...
Yeasts Computer Graphics Neural Networks, Computer Computational Biology Models, Biological
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Multi-Factored Gene-Gene Proximity Measures Exploiting Biological Knowledge Extracted from Gene Ontology: Application in Gene Clustering.

IEEE/ACM transactions on computational biology and bioinformatics Jun 21, 2018
To describe the cellular functions of proteins and genes, a potential dynamic vocabulary is Gene Ontology (GO), which comprises of three sub-ontologies namely, Biological-process, Cellular-component, and Molecular-function. It has several application...
Animals Mice Computational Biology Humans Algorithms Molecular Sequence Annotation Gene Ontology Multigene Family Genes Cluster Analysis Semantics Proteins Yeasts
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Protein-Protein Interactions Prediction via Multimodal Deep Polynomial Network and Regularized Extreme Learning Machine.

IEEE journal of biomedical and health informatics Jun 12, 2018
Predicting the protein-protein interactions (PPIs) has played an important role in many applications. Hence, a novel computational method for PPIs prediction is highly desirable. PPIs endow with protein amino acid mutation rate and two physicochemica...
Helicobacter pylori Supervised Machine Learning Yeasts Proteins Databases, Protein Protein Interaction Mapping Humans Reproducibility of Results Deep Learning Protein Interaction Maps Computational Biology
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Neural network control of focal position during time-lapse microscopy of cells.

Scientific reports May 9, 2018
Live-cell microscopy is quickly becoming an indispensable technique for studying the dynamics of cellular processes. Maintaining the specimen in focus during image acquisition is crucial for high-throughput applications, especially for long experimen...
Yeasts Time Factors Image Processing, Computer-Assisted Microscopy Neural Networks, Computer Software
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Assessment of Beer Quality Based on a Robotic Pourer, Computer Vision, and Machine Learning Algorithms Using Commercial Beers.

Journal of food science Mar 30, 2018
UNLABELLED: Sensory attributes of beer are directly linked to perceived foam-related parameters and beer color. The aim of this study was to develop an objective predictive model using machine learning modeling to assess the intensity levels of senso...
Neural Networks, Computer Machine Learning Food Handling Smell Taste Viscosity Food Quality Computers Food Technology Beer Yeasts Color Humulus
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InfAcrOnt: calculating cross-ontology term similarities using information flow by a random walk.

BMC genomics Jan 19, 2018
BACKGROUND: Since the establishment of the first biomedical ontology Gene Ontology (GO), the number of biomedical ontology has increased dramatically. Nowadays over 300 ontologies have been built including extensively used Disease Ontology (DO) and H...
Models, Biological Humans Yeasts Gene Regulatory Networks Software Gene Ontology Protein Interaction Domains and Motifs Genomics Gene Expression Profiling Computational Biology
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Validation of a novel automatic deposition of bacteria and yeasts on MALDI target for MALDI-TOF MS-based identification using MALDI Colonyst robot.

PloS one Dec 29, 2017
Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) -based identification of bacteria and fungi significantly changed the diagnostic process in clinical microbiology. We describe here a novel technique for bact...
Bacteria Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization Robotics Automation Yeasts
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A deep auto-encoder model for gene expression prediction.

BMC genomics Nov 17, 2017
BACKGROUND: Gene expression is a key intermediate level that genotypes lead to a particular trait. Gene expression is affected by various factors including genotypes of genetic variants. With an aim of delineating the genetic impact on gene expressio...
Neural Networks, Computer Yeasts Gene Expression Profiling Fungal Proteins Polymorphism, Single Nucleotide Models, Genetic
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Error-Correcting Factorization.

IEEE transactions on pattern analysis and machine intelligence Oct 16, 2017
Error Correcting Output Codes (ECOC) is a successful technique in multi-class classification, which is a core problem in Pattern Recognition and Machine Learning. A major advantage of ECOC over other methods is that the multi-class problem is decoupl...
Yeasts Humans Machine Learning Electronic Data Processing Face Image Processing, Computer-Assisted Algorithms Pattern Recognition, Automated Models, Theoretical Escherichia coli
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