Systems Biology

4.

Bioinformatics and Data Analysis

4.1.

Data Management and Quality Control

4.1.1.

Data Storage and Formats

4.1.1.1.

4.1.1.2.

Database Systems

4.1.1.3.

Cloud Computing Platforms

4.1.2.

Quality Assessment

4.1.2.1.

Sequencing Quality Metrics

4.1.2.2.

Contamination Detection

4.1.2.3.

Batch Effect Identification

4.1.3.

Data Preprocessing

4.1.3.1.

Normalization Methods

4.1.3.2.

Filtering Strategies

4.1.3.3.

Missing Data Handling

4.2.

Statistical Analysis Methods

4.2.1.

Univariate Analysis

4.2.1.1.

4.2.1.2.

4.2.1.3.

Non-Parametric Tests

4.2.2.

Multivariate Analysis

4.2.2.1.

Principal Component Analysis

4.2.2.2.

Factor Analysis

4.2.2.3.

Canonical Correlation Analysis

4.2.3.

Machine Learning Approaches

4.2.3.1.

Supervised Learning

4.2.3.1.1.

Classification Algorithms

4.2.3.1.2.

Regression Methods

4.2.3.2.

Unsupervised Learning

4.2.3.2.1.

Clustering Algorithms

4.2.3.2.2.

Dimensionality Reduction

4.2.3.3.

4.2.3.3.1.

Neural Network Architectures

4.2.3.3.2.

Convolutional Neural Networks

4.2.3.3.3.

Recurrent Neural Networks

4.3.

Biological Databases and Resources

4.3.1.

Sequence Databases

4.3.1.1.

4.3.1.2.

4.3.1.3.

4.3.2.

Protein Databases

4.3.2.1.

4.3.2.2.

Protein Data Bank

4.3.2.3.

4.3.3.

Pathway and Network Databases

4.3.3.1.

4.3.3.2.

4.3.3.3.

4.3.3.4.

4.3.4.

Gene Ontology and Annotation

4.3.4.1.

GO Terms and Structure

4.3.4.2.

Functional Enrichment Analysis

4.3.4.3.

Pathway Enrichment

4.4.

Data Integration Strategies

4.4.1.

Multi-Omics Integration

4.4.1.1.

Concatenation Methods

4.4.1.2.

Transformation Methods

4.4.1.3.

Model-Based Integration

4.4.2.

Network-Based Integration

4.4.2.1.

Multi-Layer Networks

4.4.2.2.

Network Alignment

4.4.2.3.

4.4.3.

Statistical Integration Methods

4.4.3.1.

4.4.3.2.

Bayesian Integration

4.4.3.3.

Matrix Factorization

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5. Network Biology