AIMC Topic: Models, Genetic

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Modeling positional effects of regulatory sequences with spline transformations increases prediction accuracy of deep neural networks.

Bioinformatics (Oxford, England) Apr 15, 2018
MOTIVATION: Regulatory sequences are not solely defined by their nucleic acid sequence but also by their relative distances to genomic landmarks such as transcription start site, exon boundaries or polyadenylation site. Deep learning has become the a...
Genomics Sequence Analysis, RNA Models, Genetic Machine Learning Software Protein Binding DNA Humans RNA Neural Networks, Computer K562 Cells Regulatory Sequences, Nucleic Acid Sequence Analysis, DNA Hep G2 Cells Proteins
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Extreme learning machines for reverse engineering of gene regulatory networks from expression time series.

Bioinformatics (Oxford, England) Apr 1, 2018
MOTIVATION: The reconstruction of gene regulatory networks (GRNs) from genes profiles has a growing interest in bioinformatics for understanding the complex regulatory mechanisms in cellular systems. GRNs explicitly represent the cause-effect of regu...
Models, Genetic Escherichia coli Supervised Machine Learning Gene Expression Regulation Software Saccharomyces cerevisiae Gene Regulatory Networks Computational Biology
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Improved ontology-based similarity calculations using a study-wise annotation model.

Database : the journal of biological databases and curation Jan 1, 2018
https://phenomics.github.io/.
Databases, Genetic Gene Ontology Data Curation Humans Algorithms Phenotype Models, Genetic
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A heuristic method for simulating open-data of arbitrary complexity that can be used to compare and evaluate machine learning methods.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Jan 1, 2018
A central challenge of developing and evaluating artificial intelligence and machine learning methods for regression and classification is access to data that illuminates the strengths and weaknesses of different methods. Open data plays an important...
Humans Artificial Intelligence Machine Learning Genetic Association Studies Computational Biology Systems Biology Models, Genetic Software Algorithms Computer Simulation Databases, Genetic Heuristics Stochastic Processes
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TITER: predicting translation initiation sites by deep learning.

Bioinformatics (Oxford, England) Jul 15, 2017
MOTIVATION: Translation initiation is a key step in the regulation of gene expression. In addition to the annotated translation initiation sites (TISs), the translation process may also start at multiple alternative TISs (including both AUG and non-A...
Peptide Chain Initiation, Translational Codon, Initiator Computational Biology Models, Genetic Open Reading Frames Software Animals Mice Neural Networks, Computer Machine Learning Humans
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DEEP MOTIF DASHBOARD: VISUALIZING AND UNDERSTANDING GENOMIC SEQUENCES USING DEEP NEURAL NETWORKS.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Jan 1, 2017
Deep neural network (DNN) models have recently obtained state-of-the-art prediction accuracy for the transcription factor binding (TFBS) site classification task. However, it remains unclear how these approaches identify meaningful DNA sequence signa...
Databases, Genetic DNA Genome, Human Binding Sites Computational Biology Models, Genetic Transcription Factors Humans Neural Networks, Computer Genomics
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A unified model based multifactor dimensionality reduction framework for detecting gene-gene interactions.

Bioinformatics (Oxford, England) Sep 1, 2016
MOTIVATION: Gene-gene interaction (GGI) is one of the most popular approaches for finding and explaining the missing heritability of common complex traits in genome-wide association studies. The multifactor dimensionality reduction (MDR) method has b...
Computer Simulation Genome-Wide Association Study Polymorphism, Single Nucleotide Models, Genetic Epistasis, Genetic Multifactor Dimensionality Reduction Humans
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Finding causative genes from high-dimensional data: an appraisal of statistical and machine learning approaches.

Statistical applications in genetics and molecular biology Aug 1, 2016
Modern biological experiments often involve high-dimensional data with thousands or more variables. A challenging problem is to identify the key variables that are related to a specific disease. Confounding this task is the vast number of statistical...
Sample Size Humans Data Interpretation, Statistical Machine Learning Computer Simulation Male Neoplasms Models, Genetic
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Novel applications of multitask learning and multiple output regression to multiple genetic trait prediction.

Bioinformatics (Oxford, England) Jun 15, 2016
UNLABELLED: Given a set of biallelic molecular markers, such as SNPs, with genotype values encoded numerically on a collection of plant, animal or human samples, the goal of genetic trait prediction is to predict the quantitative trait values by simu...
Animals Polymorphism, Single Nucleotide Genotype Humans Models, Genetic Quantitative Trait Loci Machine Learning Plants Algorithms Linear Models Phenotype
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Genetics. Predicting the impact of genomic variation.

Nature methods Mar 1, 2016
Sequence Analysis, DNA Molecular Sequence Data Genetic Variation Models, Statistical Chromosome Mapping Humans Unsupervised Machine Learning Base Sequence Computer Simulation Genome, Human Models, Genetic
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