AIMC Topic: Molecular Sequence Annotation

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Combined Use of Three Machine Learning Modeling Methods to Develop a Ten-Gene Signature for the Diagnosis of Ventilator-Associated Pneumonia.

Medical science monitor : international medical journal of experimental and clinical research Feb 7, 2020
BACKGROUND This study aimed to use three modeling methods, logistic regression analysis, random forest analysis, and fully-connected neural network analysis, to develop a diagnostic gene signature for the diagnosis of ventilator-associated pneumonia ...
Machine Learning Algorithms Gene Expression Profiling Protein Interaction Maps Humans Genome, Human Molecular Sequence Annotation Gene Ontology Pneumonia, Ventilator-Associated Databases, Genetic Area Under Curve Gene Expression Regulation
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Discovery and annotation of novel microRNAs in the porcine genome by using a semi-supervised transductive learning approach.

Genomics Dec 6, 2019
Despite the broad variety of available microRNA (miRNA) prediction tools, their application to the discovery and annotation of novel miRNA genes in domestic species is still limited. In this study we designed a comprehensive pipeline (eMIRNA) for miR...
Algorithms Animals Transcriptome RNA-Seq Machine Learning RNA Precursors Nucleotide Motifs Genome Sequence Homology, Nucleic Acid Muscle, Skeletal Genomics Molecular Sequence Annotation Genetic Loci Liver Sus scrofa MicroRNAs
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Identifying pseudoenzymes using functional annotation: pitfalls of common practice.

The FEBS journal Dec 5, 2019
Pseudoenzymes are proteins that are evolutionary related to enzymes but lack relevant catalytic activity. They are usually evolved from enzymatic ancestors that have lost their catalytic activities. The loss of catalytic function is one extreme among...
Molecular Sequence Annotation Enzymes Proteins Humans Knowledge Bases
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Machine Learning Approaches for the Prioritization of Genomic Variants Impacting Pre-mRNA Splicing.

Cells Nov 26, 2019
Defects in pre-mRNA splicing are frequently a cause of Mendelian disease. Despite the advent of next-generation sequencing, allowing a deeper insight into a patient's variant landscape, the ability to characterize variants causing splicing defects ha...
RNA Splicing Genomics Genetic Predisposition to Disease Humans Molecular Sequence Annotation Models, Biological Computational Biology Machine Learning RNA Precursors Genetic Variation
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Learning a functional grammar of protein domains using natural language word embedding techniques.

Proteins Nov 25, 2019
In this paper, using Word2vec, a widely-used natural language processing method, we demonstrate that protein domains may have a learnable implicit semantic "meaning" in the context of their functional contributions to the multi-domain proteins in whi...
Natural Language Processing Protein Domains Datasets as Topic Semantics Humans Proteins Databases, Protein Molecular Sequence Annotation Gene Ontology
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Artificial intelligence in clinical and genomic diagnostics.

Genome medicine Nov 19, 2019
Artificial intelligence (AI) is the development of computer systems that are able to perform tasks that normally require human intelligence. Advances in AI software and hardware, especially deep learning algorithms and the graphics processing units (...
Deep Learning Genomics Genetic Association Studies Genetics, Medical Molecular Sequence Annotation Computational Biology Molecular Diagnostic Techniques Artificial Intelligence Algorithms Phenotype Humans Neural Networks, Computer
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Machine learning for discovering missing or wrong protein function annotations : A comparison using updated benchmark datasets.

BMC bioinformatics Sep 23, 2019
BACKGROUND: A massive amount of proteomic data is generated on a daily basis, nonetheless annotating all sequences is costly and often unfeasible. As a countermeasure, machine learning methods have been used to automatically annotate new protein func...
Humans Machine Learning Gene Ontology Proteomics Eukaryota Cluster Analysis Molecular Sequence Annotation
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Advances in gene ontology utilization improve statistical power of annotation enrichment.

PloS one Aug 15, 2019
Gene-annotation enrichment is a common method for utilizing ontology-based annotations in gene and gene-product centric knowledgebases. Effective utilization of these annotations requires inferring semantic linkages by tracing paths through edges in ...
Knowledge Bases Horses Molecular Sequence Annotation Gene Ontology Humans Female Breast Neoplasms Animals Computational Biology Databases, Genetic
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Machine learning approaches to predict lupus disease activity from gene expression data.

Scientific reports Jul 3, 2019
The integration of gene expression data to predict systemic lupus erythematosus (SLE) disease activity is a significant challenge because of the high degree of heterogeneity among patients and study cohorts, especially those collected on different mi...
Computational Biology Gene Expression Lupus Erythematosus, Systemic Gene Expression Regulation Molecular Sequence Annotation Transcriptome ROC Curve Gene Regulatory Networks Humans Machine Learning Disease Progression Gene Expression Profiling
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Analysis and prediction of human acetylation using a cascade classifier based on support vector machine.

BMC bioinformatics Jun 17, 2019
BACKGROUND: Acetylation on lysine is a widespread post-translational modification which is reversible and plays a crucial role in some biological activities. To better understand the mechanism, it is necessary to identify acetylation sites in protein...
Protein Processing, Post-Translational Proteins Gene Ontology Databases, Protein Computational Biology Protein Structure, Secondary Animals Amino Acid Sequence Acetylation Rats Position-Specific Scoring Matrices Lysine Mice Support Vector Machine Molecular Sequence Annotation Humans
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