AIMC Topic: High-Throughput Nucleotide Sequencing

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A Survey of Autoencoder Algorithms to Pave the Diagnosis of Rare Diseases.

International journal of molecular sciences Oct 8, 2021
Rare diseases (RDs) concern a broad range of disorders and can result from various origins. For a long time, the scientific community was unaware of RDs. Impressive progress has already been made for certain RDs; however, due to the lack of sufficien...
Algorithms Prognosis High-Throughput Nucleotide Sequencing Rare Diseases Humans Neural Networks, Computer Machine Learning
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PARROT is a flexible recurrent neural network framework for analysis of large protein datasets.

eLife Sep 17, 2021
The rise of high-throughput experiments has transformed how scientists approach biological questions. The ubiquity of large-scale assays that can test thousands of samples in a day has necessitated the development of new computational approaches to i...
Humans Neural Networks, Computer Software Databases, Protein Phosphorylation High-Throughput Nucleotide Sequencing Sequence Analysis, Protein Deep Learning Computational Biology Proteins
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NanoCaller for accurate detection of SNPs and indels in difficult-to-map regions from long-read sequencing by haplotype-aware deep neural networks.

Genome biology Sep 6, 2021
Long-read sequencing enables variant detection in genomic regions that are considered difficult-to-map by short-read sequencing. To fully exploit the benefits of longer reads, here we present a deep learning method NanoCaller, which detects SNPs usin...
INDEL Mutation Chromosome Mapping Major Histocompatibility Complex Nanopore Sequencing Haplotypes Base Sequence Genome, Human Humans Neural Networks, Computer Polymorphism, Single Nucleotide Alleles High-Throughput Nucleotide Sequencing Nanoparticles Benchmarking
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NGS and phenotypic ontology-based approaches increase the diagnostic yield in syndromic retinal diseases.

Human genetics Aug 26, 2021
Syndromic retinal diseases (SRDs) are a group of complex inherited systemic disorders, with challenging molecular underpinnings and clinical management. Our main goal is to improve clinical and molecular SRDs diagnosis, by applying a structured pheno...
Gene Ontology Eye Diseases, Hereditary Ciliopathies Genetic Testing High-Throughput Nucleotide Sequencing Retinal Diseases Female Male Cohort Studies Mutation Humans Syndrome Genetic Association Studies Phenotype Prospective Studies Molecular Diagnostic Techniques Retrospective Studies
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Predicting base editing outcomes with an attention-based deep learning algorithm trained on high-throughput target library screens.

Nature communications Aug 25, 2021
Base editors are chimeric ribonucleoprotein complexes consisting of a DNA-targeting CRISPR-Cas module and a single-stranded DNA deaminase. They enable transition of C•G into T•A base pairs and vice versa on genomic DNA. While base editors have great ...
Algorithms Deep Learning High-Throughput Nucleotide Sequencing Gene Library Genome Gene Editing Humans Base Pairing
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HELLO: improved neural network architectures and methodologies for small variant calling.

BMC bioinformatics Aug 14, 2021
BACKGROUND: Modern Next Generation- and Third Generation- Sequencing methods such as Illumina and PacBio Circular Consensus Sequencing platforms provide accurate sequencing data. Parallel developments in Deep Learning have enabled the application of ...
INDEL Mutation Humans Neural Networks, Computer High-Throughput Nucleotide Sequencing
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Discovering differential genome sequence activity with interpretable and efficient deep learning.

PLoS computational biology Aug 9, 2021
Discovering sequence features that differentially direct cells to alternate fates is key to understanding both cellular development and the consequences of disease related mutations. We introduce Expected Pattern Effect and Differential Expected Patt...
Humans Neural Networks, Computer Deep Learning Genome, Human Transcription Factors Sequence Analysis, DNA High-Throughput Nucleotide Sequencing
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Predicting mutant outcome by combining deep mutational scanning and machine learning.

Proteins Jul 31, 2021
Deep mutational scanning provides unprecedented wealth of quantitative data regarding the functional outcome of mutations in proteins. A single experiment may measure properties (eg, structural stability) of numerous protein variants. Leveraging the ...
Proteins Protein Interaction Maps High-Throughput Nucleotide Sequencing Machine Learning Mutation Computational Biology Algorithms DNA Mutational Analysis Models, Statistical
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The use of a next-generation sequencing-derived machine-learning risk-prediction model (OncoCast-MPM) for malignant pleural mesothelioma: a retrospective study.

The Lancet. Digital health Jul 28, 2021
BACKGROUND: Current risk stratification for patients with malignant pleural mesothelioma based on disease stage and histology is inadequate. For some individuals with early-stage epithelioid tumours, a good prognosis by current guidelines can progres...
New York Aged Neoplasm Staging Retrospective Studies Risk Male Cohort Studies Mesothelioma, Malignant Female Middle Aged Pleural Neoplasms Humans Lung Neoplasms Risk Factors High-Throughput Nucleotide Sequencing Machine Learning Survival Analysis Prognosis
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Prediction model for malignant pulmonary nodules based on cfMeDIP-seq and machine learning.

Cancer science Jul 21, 2021
Cell-free methylated DNA immunoprecipitation and high-throughput sequencing (cfMeDIP-seq) is a new bisulfite-free technique, which can detect the whole-genome methylation of blood cell-free DNA (cfDNA). Using this technique, we identified differentia...
Sensitivity and Specificity Male Middle Aged DNA Methylation Cell-Free Nucleic Acids Case-Control Studies Lung Neoplasms Female Aged Aged, 80 and over Adult Multiple Pulmonary Nodules Humans High-Throughput Nucleotide Sequencing Machine Learning Immunoprecipitation Prognosis
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