Identifying optimal patient selection criteria, endpoints, and dose remain important challenges in drug development, especially when starting from small cohorts of patients. Explainable artificial intelligence (xAI) using formal concept analysis (FCA) as implemented in the KEM platform enables effective analysis of heterogeneous datasets, including small cohorts. KEM systematically explores all variable associations, identifying criteria for patient stratification, surrogate efficacy endpoints, and optimal dose. Examples will be discussed during the presentation.

This project aims to develop an automated pipeline bridging cBioPortal's curated cancer genomics data with Open Targets (OT), facilitating target-disease associations. The pipeline, employing Scala and Python, transforms cBioPortal evidence into OT-compatible scores, effectively integrating evidence into OT with a user-friendly interface. This integration enhances cancer research by enabling a streamlined ranking of drug targets based on advanced genomics, fostering a more interconnected ecosystem for researchers and clinicians.

The underlying cause for the increased prevalence of early onset cancer in recent years remains elusive, and many cases do not show obvious drivers upon standard short read testing. Long reads uncover thousands of variants that are missed by short reads. We describe the long read germline sequencing of two early-onset colorectal cancer trios, and a family consisting of two brothers with testicular cancer, and their unaffected parents. We survey multiple aspects of genomic variation including SVs, SNVs, and methylation changes, and to prioritize variants for further study to aid in identification of events that may contribute to cancer predisposition.

With new imaging technology, cancer biology is undergoing a transformation into a digital era. AI has enabled the analysis of such images in unprecedented scale. However, human domain knowledge and intervention are still essential, as data acquisition and experimental analysis pipelines are rapidly evolving. I will present scalable visualizations and visual analytics interfaces that integrate machine learning to support biologists and pathologists in their workflows. The approaches help experts to filter and classify cells, find cellular interaction, and annotate cancerous region. Future work will address the transition to multimodal, volumetric, and time-varying analysis.

Drug target assessment typically starts with information gathering from major database entries associated with a particular protein. While these generally give a consensus picture, there can be significant differences between them. This work looked at the BACE1 Alzheimer’s target across multiple resources including Guide to Pharmacology, Open Targets, DrugBank, PubChem, BindingDB, DrugCentral, Pharos, PDBe, canSAR.ai, and the Human Protein Atlas. Surprising discordances were discerned, particularly in aligned bioactive chemistry numbers, disease indications tissue expression, and interaction partners. The problems of data circularity, consensus reliability, and date divergence for the same targets will be outlined.  

Target identification is a critical stage in drug discovery. The utilization of big data strategies can substantially improve the decision-making process during target discovery. This talk will highlight two significant aspects of target finding. Firstly, computational techniques can be applied to existing biological and high-throughput omics data to uncover novel drug targets. Secondly, the application of systems biology approach using bioinformatics methods can aid in understanding the mechanism of action of a specific target or a group of targets.

The concept of One Health describes how human health is inextricably linked with environmental health, many of these complex interdependencies are still not well-understood. I will describe how the advent of real-time genomic analyses can benefit One Health and how it can enable timely, in-depth ecosystem health assessments. Nanopore sequencing is the only disruptive technology that currently allows for real-time genomic analyses and is already being used worldwide to improve the accessibility and versatility of genomic sequencing. Real-time genomic studies on zoonotic disease, food security, environmental microbiome, emerging pathogens, and their antimicrobial resistances, and on environmental health itself - from genomic resource creation for wildlife conservation to the monitoring of biodiversity, invasive species, and wildlife trafficking will be showcased. Stressing why equitable access to real-time genomics in the context of One Health is paramount and will discuss related practical, legal, and ethical limitations.