This special session aims to bring together researchers and practitioners from the fields of bioinformatics, high-performance computing, and data-intensive systems to exchange innovative ideas, challenges, and results related to computational efficiency in biological data analysis.

We invite the submission of original research papers addressing theoretical advances, algorithmic innovations, software tools, or practical applications that contribute to the efficient and scalable processing of biological data.

Topics of Interest include, but are not limited to:

• - Design and implementation of scalable bioinformatics algorithms on multicore, cluster, and cloud infrastructures.
• - Hybrid parallelism and workload optimization for large-scale biological computations.
• - Green and sustainable computing practices in bioinformatics and computational biology.
• - Memory- and energy-efficient methods for genome, transcriptome, and proteome analysis.
• - Multithreaded computing strategies for shared-memory systems.
• - Performance modeling, tuning, and prediction for bioinformatics applications in heterogeneous systems.
• - Integration of Big Data technologies and AI for accelerating biological data processing.
• - High-throughput and distributed pipelines for omics data analysis.
• - Exploitation of GPUs, FPGAs, and emerging accelerators (TPUs, NPUs, etc.) in computational biology.
• - Cloud, edge, and grid computing paradigms for bioinformatics services.
• - Scalable software architectures and reproducible workflow management in life sciences.
• - Fault tolerance, reliability, and benchmarking of bioinformatics tools under parallel and distributed environments.
• - Advanced visualization, simulation, and modeling techniques supported by parallel computing.

Organizers:
Dr. Juan José Escobar , Department of Software Engineering, University of Granada, Granada, Spain

Motivation: The surge in multi-omics and clinical data demands scalable analysis beyond traditional systems. Cloud computing enables elastic, AI-driven bioinformatics, yet challenges in cost, security, and reproducibility persist. This Special Session aims to address these challenges and provide a focused platform on cloud-native bioinformatics, including scalable architectures, HPC-cloud integration, distributed pipelines, AI-driven cloud platforms, and secure biological data management.

Objectives: To unite experts to explore scalable, secure cloud-based bioinformatics; highlight AI-driven omics applications; promote reproducible, cost-efficient workflows; and advance next-generation cloud platforms for global biological data analysis.

Topics include, but are not limited to:

• Cloud Architectures & Scalable Systems:
• - Cloud-native architectures for large biological datasets
• - HPC–cloud hybrid systems for genomics and proteomics
• - Scalable computing frameworks: Kubernetes, Docker, Serverless, Spark
• - Multi-cloud, hybrid cloud, and edge-cloud models for omics data
• Data Processing Pipelines:
• - Cloud-based processing of genomics, transcriptomics, proteomics, metabolomics
• - Scalable pipelines for NGS, variant calling, and genome assembly
• - High-throughput microbiome and metagenomics analysis
• - Workflow orchestration: Nextflow, Snakemake, CWL, WDL
• AI/ML and Advanced Analytics in the Cloud:
• - Large-scale training of biological AI/ML/DL models
• - Cloud deployment of BioGPT, protein foundation models, AlphaFold workflows
• - Federated learning & privacy-preserving ML for biomedical applications
• - Cloud-AI in drug discovery, biomarker detection, and clinical decision support
• Security, Compliance, and Governance:
• - Secure handling of clinical and biological datasets in the cloud
• - Blockchain for secure biomedical data sharing
• - HIPAA, GDPR, and biomedical data regulatory compliance
• - Access control, encryption, and data governance frameworks
• Systems Biology & Big Data Integration:
• - Cloud-based integration of multi-omics data
• - Graph analytics for systems biology and pathway modeling
• - Visualization, simulation, and modeling of biological processes at cloud scale

Organizers:
Dr. S. B. Goyal, Department of Computer Science & Engineering Chitkara University Institute of Engineering & Technology Chitkara University, Punjab, INDIA
Dr. Vikram Singh, Department of Computer Science & Engineering Chaudhary Devi Lal University, Sirsa, Haryana, INDIA

The rapidly expanding use of microbiome and metagenomic sequencing in health, ecology, and biotechnology demands advanced machine learning methods capable of handling high dimensionality, sparsity, compositional constraints, technical variability, and complex ecological structure. This special session aims to bring together researchers developing innovative analytical, statistical, and ML approaches—from compositional data analysis, cross-cohort harmonization, and synthetic data generation to phylogeny-aware models, causal inference, and multiomic integration. The goal is to accelerate robust, interpretable, and scalable microbiome science, moving beyond mere biomarker discovery toward functional understanding and translational applications.

Topics include, but are not limited to:

• - Disease screening and prediction via the gut microbiome
• - Multiomic integration and systems-level microbiome analysis
• - Metagenome functional content prediction from marker gene
• - Compositional Data Analysis (CoDA) for microbiome workflows
• - Advances in differential abundance testing
• - Feature selection for microbiome biomarker discovery
• - Advances in amplicon data harmonization and cross-cohort microbiome meta-analysis
• - Phenotype classification using microbiome data
• - Explainable AI, interfaces and metrics tailored to microbiome-specific data properties
• - Gut–brain axis modeling
• - Reproductive microbiome analysis and low-biomass contamination correction
• - Phylogenetic information in machine learning and taxonomy-aware ML methods
• - Synthetic microbiome data generation for benchmarking and model training
• - Microbiome correlation networks
• - Microbiome time series analysis and dynamic modeling
• - Metagenome-Assembled Genomes (MAGs) and whole metagenome sequencing pipelines
• - Advances in taxonomic classification workflows
• - Phylogenetic tree construction innovations

Organizer:
Ignacio Garach,

Department of Computer Engineering, Automation and Robotics, University of Granada, Spain.

Prof. Luis J. Herrera,

Department of Computer Engineering, Automation and Robotics, University of Granada, Spain.