Searching journal content for articles similar to Li et al. 16 (3): 414.

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  1. ...relevance, integrating the origin of DNA and protein elements (cis and trans) to evaluate false-positive and false-negative risks across experimental systems. Moreover, we explore how perturbation strategies—gain and loss of function—can complement steady-state profiling to establish causality in gene...
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  2. ..., and interpretable exploration of causal GRNs with prior knowledge and multi-omics data.Gene regulatory networks (GRNs), which encapsulate the complex interactions among transcription factors (TFs), target genes, and various regulatory elements, constitute the core machinery of gene regulation (Levine and Davidson...
  3. ...workflows. 32 Introduction 33 Gene networks provide essential frameworks for understanding the complex molecular interactions 34 that regulate gene expression in biological systems. At the core of these networks lies the inference of 35 gene regulation, which reflects the binding of transcription factors...
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  4. ...-based transcriptional regulatory networks. Overall, these results illustrate the power of an approach combining genetic perturbation with high-resolution epigenomic profiling; the latter enables a close examination of the interplay between TFs and nucleosomes -wide, providing a deeper, more mechanistic understanding...
  5. ...at similar times during maturation (Supplemental Fig. S1G,H). This suggests that some ASD-linked transcriptional regulators have more robust effects on neurons during the early maturation stages compared with others because of their functional relevance to transcription in this culture system rather than...
  6. ...remains limited owing to dearth of subsequent proteogenomic consequences. To coalesce the genomic information embedded in exons with isoform sequences, we present an innovative framework, “Exon Nomenclature And Classification of Transcripts” (ENACT). This centralizes exonic loci such that protein sequence...
  7. ...the variant of interest affects the function or expression of the gene of interest. For some genes, well-established functional assays can be utilized (e.g., enzymatic assay, transcriptional reporter assay, cell survival/proliferation assay). However, for most genes, there are no standardized tests that have...
  8. ...specifically to deep learning.ML can be primarily divided into three types according to the desired outcomes: classification, clustering, and regression. Both classification and regression are considered “supervised” learning, for which the model parameters are established based on samples with known class...
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  9. ....ResultsIdentification of Arabidopsis mC readersTo identify potential mC readers in Arabidopsis, we performed a DNA pull-down affinity assay of nuclear proteins using methylated DNA oligonucleotides. For each methylated sequence context (CG, CHG, CHH), DNA probes were designed to maximize the relevance of the binding proteins...
  10. ...genetic variation impacts transcription factor (TF) binding remains a major challenge, limiting our ability to model disease-associated variants. Here, we used a highly controlled system of F1 crosses with extensive genetic diversity to profile allele-specific binding of four TFs at several time points...
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