AI Article Synopsis
- The text indicates that there is a correction to the previously published article with the DOI 10.1371/journal.pone.0156807.
- This correction likely addresses errors or updates related to the findings or information in the original publication.
- It highlights the importance of maintaining accuracy in academic research and publishing.
Article Abstract
[This corrects the article DOI: 10.1371/journal.pone.0156807.].
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4968831 | PMC |
| http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0160726 | PLOS |
Publication Analysis
Top Keywords
Similar Publications
Background: Cell-free DNA (cfDNA), a broadly applicable biomarker commonly sourced from urine or blood, is extensively used for research and diagnostic applications. In various settings, genetic and epigenetic information is derived from cfDNA. However, a unified framework for its processing is lacking, limiting the universal application of innovative analysis strategies and the joining of data sets.
View Article and Find Full Text PDFRepeat and haplotype aware error correction in nanopore sequencing reads with DeChat.
Commun Biol
December 2024
College of Biology, Hunan University, Changsha, China.
Error self-correction is crucial for analyzing long-read sequencing data, but existing methods often struggle with noisy data or are tailored to technologies like PacBio HiFi. There is a gap in methods optimized for Nanopore R10 simplex reads, which typically have error rates below 2%. We introduce DeChat, a novel approach designed specifically for these reads.
View Article and Find Full Text PDFAccurate RNA velocity estimation based on multibatch network reveals complex lineage in batch scRNA-seq data.
BMC Biol
December 2024
School of Computer Science and Technology, Xidian University, Xi'an 710071, Shaanxi, China.
RNA velocity, as an extension of trajectory inference, is an effective method for understanding cell development using single-cell RNA sequencing (scRNA-seq) experiments. However, existing RNA velocity methods are limited by the batch effect because they cannot directly correct for batch effects in the input data, which comprises spliced and unspliced matrices in a proportional relationship. This limitation can lead to an incorrect velocity stream.
View Article and Find Full Text PDFArticle Synopsis
- The study investigates the use of generative AI, like ChatGPT, to support precision oncology, specifically in non-small cell lung cancer (NSCLC), addressing challenges in biomarker testing and treatment recommendations.
- Researchers assessed the accuracy of different ChatGPT versions in generating NSCLC treatment recommendations against established guidelines, utilizing a Generative AI Performance Score (G-PS) to quantify their effectiveness.
- Results showed that GPT-4 significantly outperformed GPT-3.5, demonstrating better accuracy and fewer inaccuracies (hallucinations), indicating advancements in AI's role in personalized cancer treatment.
Impact of DNA extraction, PCR amplification, sequencing, and bioinformatic analysis on food-associated mock communities using PacBio long-read amplicon sequencing.
BMC Microbiol
December 2024
Institute of Nutritional and Food Sciences, Food Microbiology and Hygiene, University of Bonn, Friedrich-Hirzebruch-Allee 7, 53115, Bonn, Germany.
Background: Long-read 16S rRNA gene amplicon sequencing has a high potential for characterizing food-associated microbiomes. The advantage results from sequencing the full-length (1,500 bp) gene, enabling taxonomic resolution at species level. Here we present a benchmarking study using mock communities representative of milking machine biofilms and raw meat, revealing challenges relevant to food-associated habitats.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!