AIMC Topic: Models, Genetic

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GNL-Scorer: a generalized model for predicting CRISPR on-target activity by machine learning and featurization.

Journal of molecular cell biology Dec 3, 2020
Humans Regression Analysis Bayes Theorem CRISPR-Cas Systems RNA, Guide, CRISPR-Cas Systems Machine Learning Software Models, Genetic
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ModelTeller: Model Selection for Optimal Phylogenetic Reconstruction Using Machine Learning.

Molecular biology and evolution Nov 1, 2020
Statistical criteria have long been the standard for selecting the best model for phylogenetic reconstruction and downstream statistical inference. Although model selection is regarded as a fundamental step in phylogenetics, existing methods for this...
Phylogeny Models, Genetic Machine Learning
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Uncovering tissue-specific binding features from differential deep learning.

Nucleic acids research Mar 18, 2020
Transcription factors (TFs) can bind DNA in a cooperative manner, enabling a mutual increase in occupancy. Through this type of interaction, alternative binding sites can be preferentially bound in different tissues to regulate tissue-specific expres...
Animals Deep Learning Protein Binding Embryo, Mammalian Organ Specificity Protein Isoforms Branchial Region Gene Expression Regulation, Developmental Myeloid Ecotropic Viral Integration Site 1 Protein Chromatin Immunoprecipitation Binding Sites Homeodomain Proteins Poisson Distribution Mice RNA Computational Biology High-Throughput Nucleotide Sequencing Models, Genetic
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DeepPod: a convolutional neural network based quantification of fruit number in Arabidopsis.

GigaScience Mar 1, 2020
BACKGROUND: High-throughput phenotyping based on non-destructive imaging has great potential in plant biology and breeding programs. However, efficient feature extraction and quantification from image data remains a bottleneck that needs to be addres...
Software Phenotype Deep Learning Arabidopsis Models, Genetic Fruit Quantitative Trait, Heritable
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Towards reliable named entity recognition in the biomedical domain.

Bioinformatics (Oxford, England) Jan 1, 2020
MOTIVATION: Automatic biomedical named entity recognition (BioNER) is a key task in biomedical information extraction. For some time, state-of-the-art BioNER has been dominated by machine learning methods, particularly conditional random fields (CRFs...
Computational Biology Models, Genetic Information Storage and Retrieval Software Deep Learning Machine Learning
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Min3: Predict microRNA target gene using an improved binding-site representation method and support vector machine.

Journal of bioinformatics and computational biology Oct 1, 2019
MicroRNAs are single-stranded noncoding RNAs known to down-regulate target genes at the protein or mRNA level. Computational prediction of targets is essential for elucidating the detailed functions of microRNA. However, prediction specificity and se...
Gene Knockdown Techniques Binding Sites Gene Knockout Techniques 3' Untranslated Regions Databases, Genetic MicroRNAs HeLa Cells RNA, Messenger Models, Genetic Support Vector Machine Software Computational Biology Humans
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Deep learning: new computational modelling techniques for genomics.

Nature reviews. Genetics Jul 1, 2019
As a data-driven science, genomics largely utilizes machine learning to capture dependencies in data and derive novel biological hypotheses. However, the ability to extract new insights from the exponentially increasing volume of genomics data requir...
Models, Genetic Unsupervised Machine Learning Supervised Machine Learning Base Sequence Computer Simulation Humans Deep Learning Neural Networks, Computer Genomics
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Machine learning-based predictors for immune checkpoint inhibitor therapy of non-small-cell lung cancer.

Annals of oncology : official journal of the European Society for Medical Oncology Apr 1, 2019
B7-H1 Antigen Models, Genetic Carcinoma, Non-Small-Cell Lung Biopsy Programmed Cell Death 1 Receptor Predictive Value of Tests Mutation Middle Aged Lung Lung Neoplasms Adult Humans Biomarkers, Tumor Aged Drug Resistance, Neoplasm Machine Learning Male Aged, 80 and over
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Machine Learning as an Effective Method for Identifying True Single Nucleotide Polymorphisms in Polyploid Plants.

The plant genome Mar 1, 2019
Single nucleotide polymorphisms (SNPs) have many advantages as molecular markers since they are ubiquitous and codominant. However, the discovery of true SNPs in polyploid species is difficult. Peanut ( L.) is an allopolyploid, which has a very low r...
Machine Learning Polymorphism, Single Nucleotide Datasets as Topic Models, Genetic Arachis Polyploidy
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GOF/LOF knowledge inference with tensor decomposition in support of high order link discovery for gene, mutation and disease.

Mathematical biosciences and engineering : MBE Feb 20, 2019
For discovery of new usage of drugs, the function type of their target genes plays an important role, and the hypothesis of "Antagonist-GOF" and "Agonist-LOF" has laid a solid foundation for supporting drug repurposing. In this research, an active ge...
Molecular Sequence Annotation Genetic Diseases, Inborn Interleukin-2 Receptor alpha Subunit Interleukin-2 Humans Lymphoma Algorithms Machine Learning Mutation Drug Repositioning Recombinant Proteins Software Genetic Variation Models, Genetic Cost-Benefit Analysis Myelodysplastic Syndromes
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