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Low-affinity binding sites and the transcription factor specificity paradox in eukaryotes
Eukaryotic transcription factors (TFs) from the same structural family tend to bind similar DNA
sequences, despite the ability of these TFs to execute distinct functions in vivo. The cell …
sequences, despite the ability of these TFs to execute distinct functions in vivo. The cell …
Decoding enhancer complexity with machine learning and high-throughput discovery
Enhancers are genomic DNA elements controlling spatiotemporal gene expression. Their
flexible organization and functional redundancies make deciphering their sequence-function …
flexible organization and functional redundancies make deciphering their sequence-function …
Short tandem repeats bind transcription factors to tune eukaryotic gene expression
Short tandem repeats (STRs) are enriched in eukaryotic cis-regulatory elements and alter
gene expression, yet how they regulate transcription remains unknown. We found that STRs …
gene expression, yet how they regulate transcription remains unknown. We found that STRs …
Deep flanking sequence engineering for efficient promoter design using DeepSEED
Designing promoters with desirable properties is essential in synthetic biology. Human
experts are skilled at identifying strong explicit patterns in small samples, while deep …
experts are skilled at identifying strong explicit patterns in small samples, while deep …
Revealing enzyme functional architecture via high-throughput microfluidic enzyme kinetics
INTRODUCTION Enzymes possess extraordinary catalytic proficiency and specificity. These
properties ultimately derive from interactions not just between the active-site residues and …
properties ultimately derive from interactions not just between the active-site residues and …
Massively parallel assays and quantitative sequence–function relationships
Over the last decade, a rich variety of massively parallel assays have revolutionized our
understanding of how biological sequences encode quantitative molecular phenotypes …
understanding of how biological sequences encode quantitative molecular phenotypes …
Evaluating deep learning for predicting epigenomic profiles
Deep learning has been successful at predicting epigenomic profiles from DNA sequences.
Most approaches frame this task as a binary classification relying on peak callers to define …
Most approaches frame this task as a binary classification relying on peak callers to define …
Rational design of minimal synthetic promoters for plants
YM Cai, K Kallam, H Tidd, G Gendarini… - Nucleic Acids …, 2020 - academic.oup.com
Promoters serve a critical role in establishing baseline transcriptional capacity through the
recruitment of proteins, including transcription factors. Previously, a paucity of data for cis …
recruitment of proteins, including transcription factors. Previously, a paucity of data for cis …
Decoding non-coding variants: recent approaches to studying their role in gene regulation and human diseases
EG Peña-Martínez… - Frontiers in bioscience …, 2024 - pmc.ncbi.nlm.nih.gov
Genome-wide association studies (GWAS) have mapped over 90% of disease-and
quantitative-trait-associated variants within the non-coding genome. Non-coding regulatory …
quantitative-trait-associated variants within the non-coding genome. Non-coding regulatory …
Global importance analysis: An interpretability method to quantify importance of genomic features in deep neural networks
PK Koo, A Majdandzic, M Ploenzke… - PLoS computational …, 2021 - journals.plos.org
Deep neural networks have demonstrated improved performance at predicting the
sequence specificities of DNA-and RNA-binding proteins compared to previous methods …
sequence specificities of DNA-and RNA-binding proteins compared to previous methods …