{"id":3433,"date":"2025-10-30T16:40:26","date_gmt":"2025-10-30T15:40:26","guid":{"rendered":"https:\/\/www.upo.es\/investiga\/synergialab\/?p=3433"},"modified":"2025-10-30T16:40:26","modified_gmt":"2025-10-30T15:40:26","slug":"new-paper-published-on-biclustering-in-bioinformatics-with-big-data-and-hpc","status":"publish","type":"post","link":"https:\/\/www.upo.es\/investiga\/synergialab\/2025\/10\/30\/new-paper-published-on-biclustering-in-bioinformatics-with-big-data-and-hpc\/","title":{"rendered":"New paper published on biclustering in bioinformatics with Big Data and HPC"},"content":{"rendered":"

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Researchers from the group have published a comprehensive review of biclustering techniques applied to massive biomedical data, highlighting computational challenges and prospects in Big Data and High Performance Computing (HPC) environments.<\/strong><\/strong><\/div>\n

The article, titled \u2018Biclustering in bioinformatics using big data and High Performance Computing applications: challenges and perspectives\u2019<\/em>, recently accepted for publication in The Journal of Supercomputing<\/em>, presents a critical analysis of the state of the art in biclustering, its applications in bioinformatics, and the opportunities offered by its parallelisation on HPC platforms.<\/p>\n

\ud83d\udcc4 Access the full article here: https:\/\/doi.org\/10.1007\/s11227-025-07563-6<\/a><\/p>\n

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Towards scalable and parallelised bioinformatics<\/h3>\n

Biomedical data analysis has evolved enormously with the emergence of omic technologies such as RNA-Seq, which generate massive volumes of highly dimensional data. In this context, biclustering has proven to be a particularly useful tool by allowing the detection of coherent local patterns between subsets of genes and conditions, unlike traditional clustering.<\/p>\n

This paper provides a systematic and critical review of the most relevant biclustering techniques in bioinformatics, focusing on:<\/p>\n

\ud83d\udd0d Fundamental aspects of biclustering<\/i><\/b><\/p>\n

The theoretical foundations, their applications to real-world problems (such as biomarker identification), and their main advantages over other exploratory data analysis methods are discussed.<\/p>\n

\u2699\ufe0f Current computational challenges<\/i><\/b><\/p>\n

The authors detail the challenges of applying biclustering to large datasets, such as computational complexity, the presence of noise, biological variability, and the need for robust validation of results.<\/p>\n

\ud83d\ude80 Application in HPC and Big Data environments<\/i><\/b><\/p>\n

One of the central themes of the paper is the need to adapt these methods to high-performance computing paradigms, such as GPU-based architectures, distributed systems with Spark or Hadoop, and parallel environments with MPI\/OpenMP.<\/p>\n

\ud83d\udcca Frameworks, tools and taxonomy<\/i><\/b><\/p>\n

An updated taxonomy of existing algorithms and tools is presented, including a comparative analysis of their approaches and limitations, and a guide is proposed for their selection and adaptation depending on the type of data and resources available.<\/p>\n

\ud83d\udd2e Future prospects<\/i><\/b><\/p>\n

The article concludes with a vision for the future that highlights the need for the evaluation and benchmarking standards, a<\/span>daptive and scalable algorithms, i<\/span>ntegration with AI techniques, and i<\/span>nteroperable platforms for reproducible research.<\/span><\/p>\n

This contribution represents a significant milestone for the scientific community working at the frontier between bioinformatics, data science and advanced computing, and lays the foundations for the development of more efficient, reproducible and scalable solutions for the analysis of complex biomedical data.<\/span><\/p>\n

Authors:<\/b><\/span><\/p>\n