AIMC Topic: Base Sequence

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Sequence-to-function deep learning frameworks for engineered riboregulators.

Nature communications Oct 7, 2020
While synthetic biology has revolutionized our approaches to medicine, agriculture, and energy, the design of completely novel biological circuit components beyond naturally-derived templates remains challenging due to poorly understood design rules....
Humans Computer Simulation Genome, Human Genome, Viral Mutagenesis Natural Language Processing Genetic Engineering Biotechnology Datasets as Topic Riboswitch Synthetic Biology Base Sequence Deep Learning Models, Genetic Structure-Activity Relationship
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LncLocation: Efficient Subcellular Location Prediction of Long Non-Coding RNA-Based Multi-Source Heterogeneous Feature Fusion.

International journal of molecular sciences Oct 1, 2020
Recent studies uncover that subcellular location of long non-coding RNAs (lncRNAs) can provide significant information on its function. Due to the lack of experimental data, the number of lncRNAs is very limited, experimentally verified subcellular l...
RNA, Long Noncoding Benchmarking Exosomes Datasets as Topic Animals Computational Biology Support Vector Machine Databases, Genetic Terminology as Topic Cytoplasm Ribosomes Eukaryotic Cells Cell Nucleus Humans Gene Expression Regulation Software Base Sequence Genome, Human
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Identification of the human DPR core promoter element using machine learning.

Nature Sep 9, 2020
The RNA polymerase II (Pol II) core promoter is the strategic site of convergence of the signals that lead to the initiation of DNA transcription, but the downstream core promoter in humans has been difficult to understand. Here we analyse the human ...
Datasets as Topic Gene Expression Regulation Base Sequence Promoter Regions, Genetic Support Vector Machine High-Throughput Nucleotide Sequencing HeLa Cells Consensus Sequence TATA Box Mutagenesis Transcription, Genetic Cells RNA Polymerase II Humans Computer Simulation Models, Genetic
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A machine learning approach to optimizing cell-free DNA sequencing panels: with an application to prostate cancer.

BMC cancer Aug 28, 2020
BACKGROUND: Cell-free DNA's (cfDNA) use as a biomarker in cancer is challenging due to genetic heterogeneity of malignancies and rarity of tumor-derived molecules. Here we describe and demonstrate a novel machine-learning guided panel design strategy...
Aged Aged, 80 and over Adult Biomarkers, Tumor Male Base Sequence Middle Aged Prostatic Neoplasms Genome, Human Cohort Studies Mutation Whole Genome Sequencing Circulating Tumor DNA Sequence Analysis, DNA Humans Machine Learning
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Enhancing the interpretability of transcription factor binding site prediction using attention mechanism.

Scientific reports Aug 7, 2020
Transcription factors (TFs) regulate the gene expression of their target genes by binding to the regulatory sequences of target genes (e.g., promoters and enhancers). To fully understand gene regulatory mechanisms, it is crucial to decipher the relat...
Binding Sites Transcription Factors Gene Expression Regulation Base Sequence Neural Networks, Computer Forecasting Deep Learning Protein Binding Computational Biology Models, Theoretical
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Sequencing enabling design and learning in synthetic biology.

Current opinion in chemical biology Aug 6, 2020
The ability to read and quantify nucleic acids such as DNA and RNA using sequencing technologies has revolutionized our understanding of life. With the emergence of synthetic biology, these tools are now being put to work in new ways - enabling de no...
Synthetic Biology Machine Learning Base Sequence
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Prediction and analysis of prokaryotic promoters based on sequence features.

Bio Systems Aug 2, 2020
Promoter recognition is an important part of functional genomic annotation but a difficult problem. Many studies have been carried out to address this issue. However, they still cannot meet application needs. Most of the methods exhibit specificity, ...
Pseudomonas aeruginosa Base Sequence Promoter Regions, Genetic Entropy Base Composition Neural Networks, Computer Escherichia coli Bacillus subtilis
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DNC4mC-Deep: Identification and Analysis of DNA N4-Methylcytosine Sites Based on Different Encoding Schemes By Using Deep Learning.

Cells Jul 22, 2020
N4-methylcytosine as one kind of modification of DNA has a critical role which alters genetic performance such as protein interactions, conformation, stability in DNA as well as the regulation of gene expression same cell developmental and genomic im...
DNA, Plant Cytosine Fragaria Rosa Genome, Plant Deep Learning Computational Biology Models, Statistical Base Sequence
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Enhancer recognition and prediction during spermatogenesis based on deep convolutional neural networks.

Molecular omics Jun 22, 2020
MOTIVATION: enhancers play an important role in the regulation of gene expression during spermatogenesis. The development of ChIP-Chip and ChIP-Seq sequencing technology has enabled researchers to focus on the relationship between enhancers and DNA s...
Neural Networks, Computer Deep Learning Genome Animals Mice Male Enhancer Elements, Genetic Support Vector Machine Internet Databases, Genetic Base Sequence Spermatogenesis Binding Sites
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CRISPRpred(SEQ): a sequence-based method for sgRNA on target activity prediction using traditional machine learning.

BMC bioinformatics Jun 1, 2020
BACKGROUND: The latest works on CRISPR genome editing tools mainly employs deep learning techniques. However, deep learning models lack explainability and they are harder to reproduce. We were motivated to build an accurate genome editing tool using ...
ROC Curve Databases, Genetic Area Under Curve CRISPR-Cas Systems Humans Algorithms Machine Learning Reproducibility of Results HEK293 Cells Deep Learning Base Sequence Gene Editing RNA, Guide, CRISPR-Cas Systems
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