Integrating multiple genomic imaging data for the study of lung metastasis in sarcomas using multi-dimensional constrained joint non-negative matrix factorization

非负矩阵分解 模式识别(心理学) 计算机科学 特征(语言学) 矩阵分解 人工智能 计算生物学 生物 语言学 特征向量 物理 哲学 量子力学
作者
Jin Deng,Weiming Zeng,Sizhe Luo,Wei Kong,Yuhu Shi,Ying Li,Hua Zhang
出处
期刊:Information Sciences [Elsevier BV]
卷期号:576: 24-36 被引量:21
标识
DOI:10.1016/j.ins.2021.06.058
摘要

Integrative analysis of histopathology images and genomic data enables the discovery of potential biomarkers and multimodal association patterns. However, few studies have established effective association models for complex diseases, such as sarcoma, by combining histopathological images with multiple genetic variation data. Here, we present an integrative multiple genomic imaging framework called multi-dimensional constrained joint non-negative matrix factorization (MDJNMF) to identify modules related to lung metastasis of sarcomas based on sample-matched whole-solid image, DNA methylation, and copy number variation features. Three types of feature matrices were projected onto a common feature space, in which heterogeneous variables with large coefficients in the same projected direction form a common module. The correlation between image features and genetic variation features is used as network-regularized constraints to improve the module accuracy. Sparsity and orthogonal constraints are utilized to achieve the modular sparse solution. Multi-level analysis indicates that our method effectively discovers biologically functional modules associated with sarcoma or lung metastasis. The representative module reveals a significant correlation between image features and genetic variation features and excavates potential diagnostic biomarkers. In summary, the proposed method provides new clues for identifying association patterns and biomarkers using multiple types of data sources for other diseases.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
康康完成签到,获得积分10
刚刚
2秒前
烟花应助沉海采纳,获得10
2秒前
2秒前
陈小军完成签到 ,获得积分10
3秒前
stilem完成签到,获得积分10
3秒前
libaojunok发布了新的文献求助10
3秒前
3秒前
4秒前
6秒前
张可爱完成签到,获得积分10
7秒前
orixero应助额我认为采纳,获得10
7秒前
8秒前
彭于晏发布了新的文献求助10
9秒前
9秒前
SICHEN发布了新的文献求助20
9秒前
张可爱发布了新的文献求助20
9秒前
爆米花应助眼睛大寻冬采纳,获得10
9秒前
jojo发布了新的文献求助10
11秒前
jaytotti完成签到,获得积分10
11秒前
13秒前
kanwenxian发布了新的文献求助10
14秒前
77发布了新的文献求助10
15秒前
桐桐应助顺心火龙果采纳,获得10
15秒前
小彤完成签到 ,获得积分10
16秒前
TCB发布了新的文献求助10
17秒前
leo_twli发布了新的文献求助10
17秒前
18秒前
18秒前
冷傲迎梦完成签到,获得积分20
19秒前
传奇3应助lyyyy采纳,获得10
20秒前
wu关注了科研通微信公众号
20秒前
量子星尘发布了新的文献求助10
21秒前
bkagyin应助LKF采纳,获得10
21秒前
苇一发布了新的文献求助10
22秒前
TCB完成签到,获得积分10
22秒前
22秒前
啵清啵发布了新的文献求助20
23秒前
Irony发布了新的文献求助10
23秒前
24秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
Cognitive Neuroscience: The Biology of the Mind 1000
Technical Brochure TB 814: LPIT applications in HV gas insulated switchgear 1000
Toward a Combinatorial Approach for the Prediction of IgG Half-Life and Clearance 500
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Picture Books with Same-sex Parented Families: Unintentional Censorship 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3970008
求助须知:如何正确求助?哪些是违规求助? 3514711
关于积分的说明 11175563
捐赠科研通 3250077
什么是DOI,文献DOI怎么找? 1795198
邀请新用户注册赠送积分活动 875630
科研通“疑难数据库(出版商)”最低求助积分说明 804931