
Yaowen ChenBeijing Institute of Medical Science, Beijing, China · Computational biology Lab
Yaowen Chen
Doctor of Philosophy
https://yaowenacademic.github.io/
About
11
Publications
1,688
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103
Citations
Citations since 2017
Introduction
Current research: metagenomics + immunology via Bioinformatics & Machine learning & Deep learning
page: https://yaowenacademic.github.io/
Additional affiliations
September 2011 - June 2016
home
Position
- Phd candicate
Publications
Publications (11)
Liver cirrhosis (LC) has been associated with gut microbes. However, the strain diversity of species and its association with LC have received little attention. Here, we constructed a computational framework to study the strain heterogeneity in the gut microbiome of patients with LC. Only Faecalibacterium prausnitzii shows different single-nucleoti...
Sequence logos are used to visually display sequence conservations and variations. They can indicate the fixed patterns or conserved motifs in a batch of DNA or protein sequences. However, most of the popular sequence logo generators can only draw logos for sequences of the same length, let alone for groups of sequences with different characteristi...
Background
Gut microbes play a critical role in human health and disease, and researchers have begun to characterize their genomes, the so-called gut metagenome. Thus far, metagenomics studies have focused on genus- or species-level composition and microbial gene sets, while strain-level composition and single-nucleotide polymorphism (SNP) have bee...
Protein-coding genes and non-coding RNAs cooperate mutually in cells. Integrative analysis of protein-coding and non-coding RNAs may facilitate characterizing tumor heterogeneity. We introduced integrated consensus clustering (ICC) method to integrate mRNA, miRNA and lncRNA expression profiles of 431 primary clear cell renal cell carcinomas (ccRCCs...
A bstract
Rapidly developing single-cell multi-omics sequencing technologies generate increasingly large bodies of multimodal data. Integrating multimodal data from different sequencing technologies, i.e . mosaic data, permits larger-scale investigation with more modalities and can help to better reveal cellular heterogeneity. However, mosaic integ...
Intestinal bacteria strains play crucial roles in maintaining host health. Researchers have increasingly recognized the importance of strain-level analysis in metagenomic studies. Many analysis tools and several cutting-edge sequencing techniques like single cell sequencing have been proposed to decipher strains in metagenomes. However, strain-leve...
Sequence logos are used to visually display conservations and variations in short sequences. They can indicate the fixed patterns or conserved motifs in a batch of DNA or protein sequences. However, most of the popular sequence logo generators are based on the assumption that all the input sequences are from the same homologous group, which will le...
BACKGROUND/AIM: Cetuximab in combination with chemotherapy is recommended as first-line therapy for metastatic colorectal cancer (mCRC) with wild-type RAS. However, drug resistance to cetuximab exists widely in mCRC and reduces the prognosis of patients. Although some genomic alterations have been demonstrated to drive acquired resistance to cetuxi...
This corrects the article DOI: 10.1038/srep45235.
Both tumor and adjacent normal tissues are valuable in cancer research. Transcriptional response profiles represent the changes of gene expression levels between paired tumor and adjacent normal tissues. In this study, we performed a pan-cancer analysis based on the transcriptional response profiles from 633 samples across 13 cancer types. We obtai...
Breast cancer is a disease with high heterogeneity. Many issues on tumorigenesis and progression are still elusive. It is critical to identify genes that play important roles in the progression of tumors, especially for tumors with poor prognosis such as basal-like breast cancer and tumors in very young women. To facilitate the identification of po...
Projects
Project (1)
To find over-looked knowledges of metagenomic sequencing data by using deep learning data.