Emerging Trends in Deep Learning for Data Mining in Bioinformatics Analysis

Dear Colleagues,

Bioinformatics has undergone a profound transformation in recent years, largely driven by the rapid accumulation of biological data across various scales and dimensions. This data deluge, comprising genomic sequences, protein structures, clinical records, and high-throughput experimental results, presents both unprecedented opportunities and formidable challenges. At the heart of this lies the need for advanced computational methods capable of extracting meaningful insights, predicting biological phenomena, and aiding biomedical research.

Motivated by these challenges, deep learning has emerged as a compelling approach.

In fact, deep learning, rooted in artificial neural networks inspired by the human brain, has risen to prominence due to its remarkable capacity to automatically learn hierarchical representations from raw data. In the context of bioinformatics, deep learning excels at deciphering intricate patterns, predicting biological outcomes, and extracting essential insights from multifaceted datasets. Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Graph Neural Networks (GNNs), and Transformerbased models have been adapted and extended to tackle diverse bioinformatics tasks, including genomic sequence analysis, protein structure prediction, biological networks, disease classification, and image analysis.

The topics of interests of this Special Issue are, but are not limited to:

• Genomic data analysis.
• Protein structures analysis.
• Biological networks applications (e.g., protein–protein interaction networks).
• Protein structure prediction and function prediction.
• Disease prediction and diagnosis.
• Drug discovery and pharmacology.
• Clinical data (patient records and medical imaging) analysis.
• Functional genomics and transcriptomics.
• Biological image analysis (microscopy and radiology).
• Convolutional Neural Networks (CNNs) for sequence and image data.
• Recurrent Neural Networks (RNNs) for sequential data.
• Graph Neural Networks (GNNs) for biological networks.
• Transformer-based models for various bioinformatics tasks.
• Transfer learning and pre-trained models.
• Interpretability and explainability of deep learning models

Dr. Federico Divina
Dr. Pedro Manuel Martínez García
Dr. Miguel García-Torres
Guest Editors

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• deep learning
• data mining
• genomic data
• proteomic data
• biological networks