Emerging machine learning (ML) technologies have the potential to significantly improve the research and treatment of rare diseases, which constitute a vast set of diseases that affect a small proportion of the total population. Artificial Intelligence (AI) algorithms can help to quickly identify patterns and associations that would be difficult or impossible for human analysts to detect. Predictive modeling techniques, such as deep learning, have been used to forecast the progression of rare diseases, enabling the development of more targeted treatments. Moreover, AI has also shown promise in the field of drug development for rare diseases with the identification of subpopulations of patients who may be most likely to respond to a particular drug. This review aims to highlight the achievements of AI algorithms in the study of rare diseases in the past decade and advise researchers on which methods have proven to be most effective. The review will focus on specific rare diseases, as defined by a prevalence rate that does not exceed 1–9/100,000 on Orphanet, and will examine which AI methods have been most successful in their study. We believe this review can guide clinicians and researchers in the successful application of ML in rare diseases.

In this review, we focus on bioinformatic oncology as an integrative discipline that incorporates knowledge from the mathematical, physical, and computational fields to further the biomedical understanding of cancer. Before providing a deeper insight into the bioinformatics approach and utilities involved in oncology, we must understand what is a system biology framework and the genetic connection, because of the high heterogenicity of the backgrounds of people approaching precision medicine. In fact, it is essential to providing general theoretical information on genomics, epigenomics, and transcriptomics to understand the phases of multi-omics approach. We consider how to create a multi-omics model. In the last section, we describe the new frontiers and future perspectives of this field.

Integrating Artificial Intelligence (AI) into Precision Medicine (PM) is redefining healthcare, enabling personalized treatments tailored to individual patients based on their genetic code, environment, and lifestyle. AI’s ability to analyze vast and complex datasets, including genomics and medical records, facilitates the identification of hidden patterns and correlations, which are critical for developing personalized treatment plans. Unsupervised Learning (UL) is particularly valuable in PM as it can analyze unstructured and unlabeled data to uncover novel disease subtypes, biomarkers, and patient stratifications. By revealing patterns that are not explicitly labeled, unsupervised algorithms enable the discovery of new insights into disease mechanisms and patient variability, advancing our understanding of individual responses to treatment. However, the integration of AI into PM presents some challenges, including concerns about data privacy and the rigorous validation of AI models in clinical practice. Despite these challenges, AI holds immense potential to revolutionize PM, offering a more personalized, efficient, and effective approach to healthcare. Collaboration among AI developers and clinicians is essential to fully realize this potential and ensure ethical and reliable implementation in medical practice. This review will explore the latest emerging UL technologies in the biomedical field with a particular focus on PM applications and their impact on human health and well-being.

Lignin, the most abundant renewable aromatic biopolymer on Earth, is rapidly emerging as a powerful enabler of next-generation sustainable technologies. This review shifts the focus to the latest industrial breakthroughs that exploit lignin’s multifunctional properties across energy, agriculture, healthcare, and environmental sectors. Lignin-derived carbon materials are offering scalable, low-cost alternatives to critical raw materials in batteries and supercapacitors. In agriculture, lignin-based biostimulants and controlled-release fertilizers support resilient, low-impact food systems. Cosmetic and pharmaceutical industries are leveraging lignin’s antioxidant, UV-protective, and antimicrobial properties to create bio-based, clean-label products. In water purification, lignin-based adsorbents are enabling efficient and biodegradable solutions for persistent pollutants. These technological leaps are not merely incremental, they represent a paradigm shift toward a materials economy powered by renewable carbon. Backed by global sustainability roadmaps like the European Green Deal and China’s 14th Five-Year Plan, lignin is moving from industrial residue to strategic asset, driven by unprecedented investment and cross-sector collaboration. Breakthroughs in lignin upgrading, smart formulation, and application-driven design are dismantling long-standing barriers to scale, performance, and standardization. As showcased in this review, lignin is no longer just a promising biopolymer, it is a catalytic force accelerating the global transition toward circularity, climate resilience, and green industrial transformation. The future of sustainable innovation is lignin-enabled.

Computational methods have transformed target and drug discovery, significantly accelerating the identification of biological targets and lead compounds. Despite its limitations, in silico molecular docking represents a foundational tool. Molecular Dynamics (MD) simulations, employing accurate force fields, provide near-realistic insights into a compound’s behavior within a biological target. However, docking and MD predictions may be unreliable without precise knowledge of the target binding site. Through MD simulations, we investigated 100 co-crystal structures of biological targets complexed with active compounds, identifying key structural and energy dynamic features that govern target–ligand interactions. Our analysis provides a detailed quantitative description of these parameters, offering critical validation for improving the predictive reliability of docking and MD simulations. This work provides a robust framework for refining early-stage drug discovery and target identification.

Background: Alkaptonuria (AKU) is an ultra-rare autosomal recessive disease caused by a mutation in the homogentisate 1,2-dioxygenase (HGD) gene. One of the main obstacles in studying AKU, and other ultra-rare diseases, is the lack of a standardized methodology to assess disease severity or response to treatment. Quality of Life scores (QoL) are a reliable way to monitor patients’ clinical condition and health status. QoL scores allow to monitor the evolution of diseases and assess the suitability of treatments by taking into account patients’ symptoms, general health status and care satisfaction. However, more comprehensive tools to study a complex and multi-systemic disease like AKU are needed. In this study, a Machine Learning (ML) approach was implemented with the aim to perform a prediction of QoL scores based on clinical data deposited in the ApreciseKUre, an AKU- dedicated database.Method: Data derived from 129 AKU patients have been firstly examined through a preliminary statistical analysis (Pearson correlation coefficient) to measure the linear correlation between 11 QoL scores. The variable importance in QoL scores prediction of 110 ApreciseKUre biomarkers has been then calculated using XGBoost, with K-nearest neighbours algorithm (k-NN) approach. Due to the limited number of data available, this model has been validated using surrogate data analysis.Results: We identified a direct correlation of 6 (age, Serum Amyloid A, Chitotriosidase, Advanced Oxidation Protein Products, S-thiolated proteins and Body Mass Index) out of 110 biomarkers with the QoL health status, in particular with the KOOS (Knee injury and Osteoarthritis Outcome Score) symptoms (Relative Absolute Error (RAE) 0.25). The error distribution of surrogate-model (RAE 0.38) was unequivocally higher than the true-model one (RAE of 0.25), confirming the consistency of our dataset. Our data showed that inflammation, oxidative stress, amyloidosis and lifestyle of patients correlates with the QoL scores for physical status, while no correlation between the biomarkers and patients’ mental health was present (RAE 1.1).Conclusions: This proof of principle study for rare diseases confirms the importance of database, allowing data management and analysis, which can be used to predict more effective treatments.