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Updated: Oct 23, 2025

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay EMSA and DNA-affinity Precipitation Assay DAPA

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A primer on machine learning techniques for genomic applications.

Alfonso Monaco¹, Ester Pantaleo², Nicola Amoroso^1,3

¹Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, Via A. Orabona 4, 70125 Bari, Italy.

Computational and Structural Biotechnology Journal

|August 25, 2021

Summary

This summary is machine-generated.

Artificial Intelligence (AI) and machine learning are crucial for analyzing big data from high-throughput sequencing. This review details AI methods for genomics, highlighting their power in handling complex, multimodal genomic datasets.

Keywords:

Deep learning Genomics Machine learning

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Last Updated: Oct 23, 2025

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Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

High-throughput sequencing generates vast amounts of genomic data.
Analyzing this big data requires advanced computational approaches.
Artificial Intelligence (AI) offers powerful tools for omics data analysis.

Purpose of the Study:

To review common machine learning (ML) and deep learning (DL) methodologies in genomics.
To explain the capabilities, strengths, and limitations of AI in genomics.
To demonstrate the relevance of AI for handling large-scale multimodal genomic data.

Main Methods:

Review of prevalent machine learning algorithms.
Discussion of deep learning techniques.
Application examples in genomics tasks.

Main Results:

ML and DL are effective for analyzing complex genomic data at single nucleotide resolution.
AI methods demonstrate significant power in handling big data challenges.
The described AI approaches are applicable to diverse multimodal genomic datasets.

Conclusions:

AI, particularly ML and DL, is essential for the big data era in genomics.
These computational methods provide efficient solutions for analyzing heterogeneous omics data.
AI empowers researchers to extract insights from large, multimodal genomic information.