ArticleBriefings in bioinformatics2021
Chrom-Lasso: a lasso regression-based model to detect functional interactions using Hi-C data.
Article in Briefings in bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
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- Construction and validation of nomogram to predict surgical site infection after hysterectomy: a retrospective study.Scientific reports · 2024Article
- Experimental confirmation and bioinformatics reveal biomarkers of immune system infiltration and hypertrophy ligamentum flavum.JOR spine · 2024Article
- Identification of immune-associated biomarkers of diabetes nephropathy tubulointerstitial injury based on machine learning: a bioinformatics multi-chip integrated analysis.BioData mining · 2024Article
- Screening and Analysis of Core Genes for Osteoporosis Based on Bioinformatics Analysis and Machine Learning Algorithms.Indian journal of orthopaedics · 2024Article
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- Bioinformatics-based discovery of intervertebral disc degeneration biomarkers and immune-inflammatory infiltrates.JOR spine · 2024Article
- Risk factors for hemoglobin decline in gastric cancer patients undergoing postoperative chemotherapy: a retrospective analysis.American journal of translational research · 2024Article
- Construction and validation of a clinical prediction model for deep vein thrombosis in patients with digestive system tumors based on a machine learning.American journal of cancer research · 2024Article
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- Construction of a prognostic prediction model for renal clear cell carcinoma combining clinical traits.Scientific reports · 2023Article
- Identification and verification of diagnostic biomarkers based on mitochondria-related genes related to immune microenvironment for preeclampsia using machine learning algorithms.Frontiers in immunology · 2023Article
- Fatty Acid Metabolism-Related lncRNAs are Potential Biomarkers for Predicting Prognoses and Immune Responses in Patients with Skin Cutaneous Melanoma.Clinical, cosmetic and investigational dermatology · 2023Article
- Identification and validation of a novel mitochondrion-related gene signature for diagnosis and immune infiltration in sepsis.Frontiers in immunology · 2023Article
- Risk factors for postoperative pulmonary infection in patients with non-small cell lung cancer: analysis based on regression models and construction of a nomogram prediction model.American journal of translational research · 2023Article
- Development and Validation of a nomogram for forecasting survival of alcohol related hepatocellular carcinoma patients.Frontiers in oncology · 2022Article
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4 authors.
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Abstract
Hi-C is a genome-wide assay based on Chromosome Conformation Capture and high-throughput sequencing to decipher 3D chromatin organization in the nucleus. However, computational methods to detect functional interactions utilizing Hi-C data face challenges including the correction for various sources of biases and the identification of functional interactions with low counts of interacting fragments. We present Chrom-Lasso, a lasso linear regression model that removes complex biases assumption-free and identifies functional interacting loci with increased power by combining information of local reads distribution surrounding the area of interest. We showed that interacting regions identified by Chrom-Lasso are more enriched for 5C validated interactions and functional GWAS hits than that of GOTHiC and Fit-Hi-C. To further demonstrate the ability of Chrom-Lasso to detect interactions of functional importance, we performed time-series Hi-C and RNA-seq during T cell activation and exhaustion. We showed that the dynamic changes in gene expression and chromatin interactions identified by Chrom-Lasso were largely concordant with each other. Finally, we experimentally confirmed Chrom-Lasso's finding that Erbb3 was co-regulated with distinct neighboring genes at different states during T cell activation. Our results highlight Chrom-Lasso's utility in detecting weak functional interaction between cis-regulatory elements, such as promoters and enhancers.
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