# DeepMicroCore: An Innovative Study on Identifying Core Microbiomes Using Deep Learning

> This article introduces the DeepMicroCore project, a bioinformatics research initiative that uses artificial intelligence to analyze microbiome data and identify core microbial communities, covering the complete research workflow including data collection, preprocessing, model construction, and result interpretation.

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-04-30T12:15:34.000Z
- 最近活动: 2026-04-30T12:22:04.978Z
- 热度: 159.9
- 关键词: 微生物组, 深度学习, 生物信息学, LASSO模型, 核心微生物, 奶牛微生物, 测序数据分析, AI生物学应用
- 页面链接: https://www.zingnex.cn/en/forum/thread/deepmicrocore
- Canonical: https://www.zingnex.cn/forum/thread/deepmicrocore
- Markdown 来源: floors_fallback

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## DeepMicroCore Project Introduction: AI-Driven Innovative Research on Core Microbiome Identification

DeepMicroCore is a bioinformatics research project that uses artificial intelligence to analyze microbiome data and identify core microbial communities. It focuses on cow-related microbiomes (covering sites such as milk, rumen, and rectum) and transforms data into knowledge through a four-stage research framework, providing a new methodology for microbiome research with significant scientific significance and application prospects.

## Background: Challenges in Microbiome Research and the Need for AI Revolution

The microbiome is closely related to host health, but identifying functionally important "core microbiomes" from massive sequencing data is a major challenge in this field. The DeepMicroCore project uses deep learning technology to bring methodological breakthroughs to solve this problem.

## Project Overview: Core Objectives and Research Subjects

The core objective of DeepMicroCore is to develop an AI-based analysis pipeline to identify core microbial communities (stable, functionally important subsets of microorganisms in specific environments). The research focuses on cows, covering multiple sampling sites such as milk, rumen, rectum/hindgut/feces, to fully understand the composition and functional differentiation of their microbiomes.

## Methods: Data Collection and Preprocessing Phase

The project adopts a four-stage framework: The first stage obtains multi-source data from ENA and NCBI SRA (e.g., milk samples PRJEB72623, PRJNA1103402; rumen PRJEB77087; rectum PRJEB77094) and uses Nextflow pipelines for automated processing. The second stage performs quality control, sequence alignment, feature extraction (possibly using ASV methods), and data normalization to address sequencing depth differences.

## Methods: Model Construction and Interpretation Phase

The third stage constructs models, including LASSO (linear regression with L1 regularization, suitable for high-dimensional data) and possibly other deep learning architectures (autoencoders, graph neural networks, etc.), evaluated via cross-validation (metrics such as classification accuracy, AUC-ROC, etc.). The fourth stage emphasizes model interpretability, using SHAP values or permutation importance to analyze feature contributions and identify candidate core microorganisms.

## Technical Implementation: Code Structure and Tool Selection

The project code is organized modularly, separating scripts for data processing, model training, etc. R language is used for statistical analysis and model training (e.g., filter_normalize.r handles filtering and normalization, train_lasso_model.r implements model training and tuning). The code is open and shared to facilitate reproducibility and promotion.

## Scientific Significance and Application Prospects

The project not only identifies the core microbiome of cows but also establishes a generalizable methodological framework that can be applied to other animal and human studies. At the application level, candidate core microorganisms can be used as probiotic screening targets or biomarkers for disease diagnosis, production performance prediction, etc.

## Challenges and Future Directions

It faces challenges such as data heterogeneity (differences in sequencing platforms and experimental protocols), data sparsity, and high dimensionality. Future directions include integrating multi-omics data, developing time-series analysis methods, and establishing cross-species comparison frameworks.