AIMC Topic: Saccharomyces cerevisiae

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Novel symmetry-based gene-gene dissimilarity measures utilizing Gene Ontology: Application in gene clustering.

Gene Sep 2, 2018
In recent years DNA microarray technology, leading to the generation of high-volume biological data, has gained significant attention. To analyze this high volume gene-expression data, one such powerful tool is Clustering. For any clustering algorith...
Computational Biology Databases, Genetic Cluster Analysis Animals Gene Expression Profiling Mice Gene Ontology Oligonucleotide Array Sequence Analysis Saccharomyces cerevisiae Proteins Multigene Family Gene Expression Regulation Saccharomyces cerevisiae Algorithms
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MAPLE (modular automated platform for large-scale experiments), a robot for integrated organism-handling and phenotyping.

eLife Aug 17, 2018
Lab organisms are valuable in part because of large-scale experiments like screens, but performing such experiments over long time periods by hand is arduous and error-prone. Organism-handling robots could revolutionize large-scale experiments in the...
Saccharomyces cerevisiae Drosophila melanogaster Caenorhabditis elegans Social Behavior Molecular Biology Animals Robotics
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NMFGO: Gene Function Prediction via Nonnegative Matrix Factorization with Gene Ontology.

IEEE/ACM transactions on computational biology and bioinformatics Jul 30, 2018
Gene Ontology (GO) is a controlled vocabulary of terms that describe molecule function, biological roles, and cellular locations of gene products (i.e., proteins and RNAs), it hierarchically organizes more than 43,000 GO terms via the direct acyclic ...
Gene Ontology Genes Arabidopsis Saccharomyces cerevisiae Molecular Sequence Annotation Humans Algorithms Computational Biology
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Mining features for biomedical data using clustering tree ensembles.

Journal of biomedical informatics Jul 29, 2018
The volume of biomedical data available to the machine learning community grows very rapidly. A rational question is how informative these data really are or how discriminant the features describing the data instances are. Several biomedical datasets...
Cluster Analysis Decision Trees Metabolic Networks and Pathways Computational Biology Escherichia coli Data Mining Saccharomyces cerevisiae Humans Machine Learning Algorithms Databases, Factual Protein Interaction Maps Gene Regulatory Networks
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HECIL: A Hybrid Error Correction Algorithm for Long Reads with Iterative Learning.

Scientific reports Jul 2, 2018
Second-generation DNA sequencing techniques generate short reads that can result in fragmented genome assemblies. Third-generation sequencing platforms mitigate this limitation by producing longer reads that span across complex and repetitive regions...
Escherichia coli Sequence Analysis, DNA High-Throughput Nucleotide Sequencing Machine Learning Repetitive Sequences, Nucleic Acid Saccharomyces cerevisiae Mosquito Vectors
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Consistent prediction of GO protein localization.

Scientific reports May 17, 2018
The GO-Cellular Component (GO-CC) ontology provides a controlled vocabulary for the consistent description of the subcellular compartments or macromolecular complexes where proteins may act. Current machine learning-based methods used for the automat...
Software Animals Computational Biology Proteomics Molecular Sequence Annotation Gene Ontology Arabidopsis Solanum lycopersicum Proteins Saccharomyces cerevisiae Databases, Protein Drosophila melanogaster
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MiYA, an efficient machine-learning workflow in conjunction with the YeastFab assembly strategy for combinatorial optimization of heterologous metabolic pathways in Saccharomyces cerevisiae.

Metabolic engineering Apr 5, 2018
Facing boosting ability to construct combinatorial metabolic pathways, how to search the metabolic sweet spot has become the rate-limiting step. We here reported an efficient Machine-learning workflow in conjunction with YeastFab Assembly strategy (M...
Saccharomyces cerevisiae Metabolic Engineering Machine Learning Models, Biological Indoles Neural Networks, Computer
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PCLPred: A Bioinformatics Method for Predicting Protein-Protein Interactions by Combining Relevance Vector Machine Model with Low-Rank Matrix Approximation.

International journal of molecular sciences Mar 29, 2018
Protein-protein interactions (PPI) are key to protein functions and regulations within the cell cycle, DNA replication, and cellular signaling. Therefore, detecting whether a pair of proteins interact is of great importance for the study of molecular...
Saccharomyces cerevisiae Saccharomyces cerevisiae Proteins Software Models, Genetic Support Vector Machine Databases, Protein
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Completing sparse and disconnected protein-protein network by deep learning.

BMC bioinformatics Mar 22, 2018
BACKGROUND: Protein-protein interaction (PPI) prediction remains a central task in systems biology to achieve a better and holistic understanding of cellular and intracellular processes. Recently, an increasing number of computational methods have sh...
Saccharomyces cerevisiae Humans Neural Networks, Computer Machine Learning Proteins Area Under Curve Bayes Theorem Algorithms ROC Curve Protein Interaction Maps
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Enhanced prediction of recombination hotspots using input features extracted by class specific autoencoders.

Journal of theoretical biology Feb 17, 2018
In yeast and in some mammals the frequencies of recombination are high in some genomic locations which are known as recombination hotspots and in the locations where the recombination is below average are consequently known as coldspots. Knowledge of...
Saccharomyces cerevisiae Neural Networks, Computer Deep Learning Recombination, Genetic Algorithms Base Sequence Classification
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