Pattern Mining for Bioinformatics

More workshops at IEEE International Conference on Data Mining (ICDM) 2026

• AI-ready Data for Science — Focuses on preparing data for AI-driven scientific discovery, which is usually the limiting factor rather...
• AI-for-Science Benchmarking — Covers benchmarking AI methods for scientific applications, on the premise that claims in AI for...
• Adaptable Reliable Responsible Learning — Focuses on adaptability, reliability and responsibility in machine learning, treating the three as a single...
• Foundation Models for Biology — Explores foundation models for biology and bioinnovation, an area that has moved quickly since large...
• Data and Database Systems for AI — Covers data and database systems that underpin modern AI, the infrastructure layer that rarely gets...
• Deep Learning and Clustering — Focuses on deep-learning approaches to clustering and representation learning, where the two problems are increasingly...
• Data Mining for Ambient Intelligence — Applies data mining to ambient intelligence and secure communications: environments densely instrumented with sensors that...
• Data Mining in Biomedical Informatics — Applies data mining to biomedical informatics and healthcare data, including electronic health records, clinical notes,...
• Data Mining for Service — Applies data mining to service systems and service science, covering the operational side of businesses...
• Evolutionary Data Mining — Covers evolutionary computation approaches to data mining and machine learning: genetic algorithms, genetic programming, swarm...
• Evolving Graph Data Mining — Focuses on mining evolving graphs and continual relational learning, where the structure itself changes over...
• GNN-LLM Synergy — Combines graph neural networks with large language models for reasoning over structure, bringing together two...
• Human Dynamics and Mobility Analytics — Focuses on mining human dynamics and mobility in the age of AI agents, covering how...
• High Dimensional Data Mining — Focuses on mining and learning from high-dimensional data, where distance measures lose meaning and most...
• Incremental and Continual Learning — Covers incremental, continual and lifelong learning: models that keep learning after deployment instead of being...
• Knowledge Discovery in ISAC — Applies knowledge discovery to integrated sensing and communication systems, in which the same radio hardware...
• LLMs for Multimodal Data Fusion — Applies large language models to fusing multimodal data, using language as the connective layer between...
• LLMs and Tensor Analysis — Explores the interplay of large language models and tensor analysis, in both directions. Topics include...
• Large Models for Time Series — Covers large and foundation models applied to time series data mining, an area that arrived...
• Medical Time Series Foundation Models — Covers foundation models for medical time series analytics, including ECG, EEG, vital signs and continuous...
• Mental Health on Social Media — Covers detecting mental-health signals in social media data, a research area with unusually heavy ethical...
• Multimodal Mining for Agriculture — Applies multimodal data mining to sustainable agriculture, combining satellite and drone imagery, in-field sensors, weather...
• Machine Learning for Cybersecurity — Applies machine learning to cybersecurity problems, where the data is adversarial by construction rather than...
• Optimization for Data Mining — Applies optimization techniques to emerging data mining problems, focusing on the mathematical machinery underneath rather...
• Open World Anomaly Detection — Covers anomaly detection in open, dynamic and evolving settings, where the closed-world assumption behind most...
• Clustering with Pretrained Models — Explores clustering using pretrained and foundation models, which has quietly changed how the task is...
• Sentiment Analysis — Covers sentiment analysis and opinion mining from text, a long-running area substantially reshaped by large...
• Spatial and Spatiotemporal Data Mining — Covers data mining over spatial and spatiotemporal data, where observations are correlated across both space...
• Secure Federated Generative AI — Covers security and trust in federated generative AI, combining two areas whose risks compound. Federated...
• Trustworthy ML for Decision-Making — Covers fairness, privacy, robustness and explainability in decision-making machine learning, in settings where a model's...
• Trustworthy Federated Learning for Industry — Focuses on trustworthy federated learning for smart industrial systems, where data is distributed across factories...
• Trustworthy Recommender Systems — Focuses on trust, privacy and unlearning in recommender systems, covering the obligations that arrive once...
• Urban Intelligence and Mobility — Covers urban intelligence and multimodal mobility data mining, treating the city as a sensed system....
• AI for Personalization — Covers AI and data mining methods for personalization and recommendation, spanning both the modelling and...
• World Models and Autonomous Intelligence — Explores world models and autonomous intelligence for data mining: learned internal models of an environment...