Identifying causative genes from patient phenotypes remains a significant challenge in precision medicine, with important implications for the diagnosis and treatment of genetic disorders. We propose a novel graph-based approach for predicting causative genes from patient phenotypes, with or without an available list of candidate genes, by integrating a rare disease knowledge graph (KG). Our model, combining graph neural networks and transformers, achieves substantial improvements over the current state-of-the-art. On the real-world MyGene2 dataset, it attains a mean reciprocal rank (MRR) of 24.64\% and nDCG@100 of 33.64\%, surpassing the best baseline (SHEPHERD) at 19.02\% MRR and 30.54\% nDCG@100. We perform extensive ablation studies to validate the contribution of each model component. Notably, the approach generalizes to cases where only phenotypic data are available, addressing key challenges in clinical decision support when genomic information is incomplete.

Within precision medicine, and specifically rare diseases, clinicians and researchers rely on genetic and diagnostic testing to help drive accurate diagnosis and treatment. However, genomic data alone are often insufficient to unlock the life-changing diagnoses of rare diseases. Well-curated and accurate phenotype data, which may include quantified observable traits such as short stature, low set ears, and blood biochemistry, along with genetic and diagnostic test results, are vital for shortening the diagnostic journey of these patients and identifying the most effective treatments available. The need for accurate patient phenotyping is not a new concept. In fact, over 20 years ago, Isaac Kohane, Chair of the Department of Biomedical Informatics and the Marion V. Nelson Professor of Biomedical Informatics at Harvard Medical School, predicted that the accurate practice of patient phenotyping would become essential as the volume of genomic information continued to surge.

We introduce the RIKEN Microstructural Imaging Metadatabase, a semantic web-based imaging database in which image metadata are described using the Resource Description Framework (RDF) and detailed biological properties observed in the images can be represented as Linked Open Data. The metadata are used to develop a large-scale imaging viewer that provides a straightforward graphical user interface to visualise a large microstructural tiling image at the gigabyte level. We applied the database to accumulate comprehensive microstructural imaging data produced by automated scanning electron microscopy. As a result, we have successfully managed vast numbers of images and their metadata, including the interpretation of morphological phenotypes occurring in sub-cellular components and biosamples captured in the images. We also discuss advanced utilisation of morphological imaging data that can be promoted by this database.

"Plant breeding is another interesting application we're pursuing, where robotically gathered plant phenotype data can be collected over much larger breeding experiments that current manual measurement techniques allow," said Kantor. "Machine learning tools can then combine the collected phenotype data with genetic and environmental data to help a breeders and geneticists better understand the relationships between genetics, environment, and plant performance." "This in turn accelerates the breeding process, allowing breeders to evaluate many more plants each season so that they can more quickly select for desirable traits such as yield or disease resistance," adds Kantor. Kantor says this kind of accelerated breeding program could have significant benefit in the developing world such as Subsaharan Africa. The FarmView initiative wants to develop inexpensive robotic systems that small- to medium-scale growers can afford to implement.