AIMC Topic: Genome

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Enabling interpretable machine learning for biological data with reliability scores.

PLoS computational biology May 26, 2023
Machine learning tools have proven useful across biological disciplines, allowing researchers to draw conclusions from large datasets, and opening up new opportunities for interpreting complex and heterogeneous biological data. Alongside the rapid gr...
Genome Reproducibility of Results Machine Learning
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Streamline unsupervised machine learning to survey and graph indel-based haplotypes from pan-genomes.

Molecular plant May 18, 2023
Algorithms Genome INDEL Mutation Haplotypes Unsupervised Machine Learning
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Hybrid Multitask Learning Reveals Sequence Features Driving Specificity in the CRISPR/Cas9 System.

Biomolecules Apr 3, 2023
CRISPR/Cas9 technology is capable of precisely editing genomes and is at the heart of various scientific and medical advances in recent times. The advances in biomedical research are hindered because of the inadvertent burden on the genome when genom...
CRISPR-Cas Systems Machine Learning Genome
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Enabling technology and core theory of synthetic biology.

Science China. Life sciences Feb 6, 2023
Synthetic biology provides a new paradigm for life science research ("build to learn") and opens the future journey of biotechnology ("build to use"). Here, we discuss advances of various principles and technologies in the mainstream of the enabling ...
Artificial Intelligence Synthetic Biology Biotechnology Gene Editing Genome
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MultiScale-CNN-4mCPred: a multi-scale CNN and adaptive embedding-based method for mouse genome DNA N4-methylcytosine prediction.

BMC bioinformatics Jan 18, 2023
N4-methylcytosine (4mC) is an important epigenetic mechanism, which regulates many cellular processes such as cell differentiation and gene expression. The knowledge about the 4mC sites is a key foundation to exploring its roles. Due to the limitatio...
Mice Epigenesis, Genetic Animals Genome DNA Software Neural Networks, Computer
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Cross-species cell-type assignment from single-cell RNA-seq data by a heterogeneous graph neural network.

Genome research Dec 16, 2022
Cross-species comparative analyses of single-cell RNA sequencing (scRNA-seq) data allow us to explore, at single-cell resolution, the origins of the cellular diversity and evolutionary mechanisms that shape cellular form and function. Cell-type assig...
Biomarkers Neural Networks, Computer Gene Expression Profiling Animals Humans Sequence Analysis, RNA Gene Regulatory Networks Mice Single-Cell Gene Expression Analysis Single-Cell Analysis Genome
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Machine learning for predicting phenotype from genotype and environment.

Current opinion in biotechnology Dec 2, 2022
Predicting phenotype with genomic and environmental information is critically needed and challenging. Machine learning methods have emerged as powerful tools to make accurate predictions from large and complex biological data. Here, we review the pro...
Genome Genomics Genotype Phenotype Machine Learning
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Machine learning applications for transcription level and phenotype predictions.

IUBMB life Nov 17, 2022
Predicting phenotypes and complex traits from genomic variations has always been a big challenge in molecular biology, at least in part because the task is often complicated by the influences of external stimuli and the environment on regulation of g...
Genome Phenotype Genomics Reproducibility of Results Machine Learning
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Fuzzy Logic as a Strategy for Combining Marker Statistics to Optimize Preselection of High-Density and Sequence Genotype Data.

Genes Nov 11, 2022
The high dimensionality of genotype data available for genomic evaluations has presented a motivation for developing strategies to identify subsets of markers capable of increasing the accuracy of predictions compared to the current commercial single...
Genomics Genome Genotype Polymorphism, Single Nucleotide Fuzzy Logic
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DLoopCaller: A deep learning approach for predicting genome-wide chromatin loops by integrating accessible chromatin landscapes.

PLoS computational biology Oct 7, 2022
In recent years, major advances have been made in various chromosome conformation capture technologies to further satisfy the needs of researchers for high-quality, high-resolution contact interactions. Discriminating the loops from genome-wide conta...
Chromosomes Chromatin Transcription Factors Genome Deep Learning
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