Identification and extraction of characterized information from complex high-dimensional biological data is a very
meaningful issue. The dimensionality reduction fusion method based on random forest, feature extraction and neural
network is proposed to recognize and classify two datasets of mRNA and lncRNA. It is shown that the proposed fusion
method achieved accurate identification/classification of cancer and non-cancer groups, and simultaneously selected
identity variables that have biological relevance to lung cancer (tumor) as potential biomarkers from a large number of
variables. It is considered as an effective tool and theoretical support for lung cancer identification in clinical application,
and it can be extended to other kinds of cancer or biological data. Ultimately, an advanced method for feature extraction
and classification analysis of high-dimensional data is provided.
语种：
外文
第一作者：
第一作者机构：[1]河北大学附属医院
通讯作者：
通讯机构：[1]河北大学附属医院
推荐引用方式(GB/T 7714)：
王志敏,李艳坤.Feature Extraction and Classification Analysis of High-Dimensional Biological Data Based on Dimensionality Reduction Fusion Method[J].2024,9(1):1-7.
APA：
王志敏&李艳坤.(2024).Feature Extraction and Classification Analysis of High-Dimensional Biological Data Based on Dimensionality Reduction Fusion Method.,9,(1)
MLA：
王志敏,et al."Feature Extraction and Classification Analysis of High-Dimensional Biological Data Based on Dimensionality Reduction Fusion Method". 9..1(2024):1-7
