We have developed a genetics-based approach that integrates functional data from GWAS, ontologies, and biological networks to predict potential drug targets with evidence for disease association. We obtained a list of target genes associated with ALS and prioritized potential target genes by integrating structural information and cell-type expression data. The work that I will present is an excellent example of using genetics and multimodal data to predict drug targets associated with ALS. Our results suggest that data augmentation and integration with cell-type gene expression networks can improve performance for predicting drug targets and emphasizes the importance of multi-omics data & network approaches for target prediction. 

International Consortium for Technology in Biomedicine (ICTBioMed) was created in 2013 by Open Health Systems Laboratory (OHSL), Poznan Supercomputing and Networking Center (PSNC), and the Centre for Development of Advanced Computing (CDAC) with the purpose of bringing together biomedical researchers on a computational platform for collaborative research. In this session, ICTBioMed teams will present some of their ongoing open science projects which would welcome international research partners.

Effective transdisciplinary research using technologies such as machine learning and digital twins relies on multiple data modalities and a common data space, including global coverage. The ICTBIOMED consortium recognizes that a more holistic and consistent approach in developing interaction between all stakeholders is necessary to enable wide scale testing of new ideas and to provide an environment for sustainable growth of value-adding cancer research solutions.

OHSL’s global team science approach utilizes an open science shared knowledge framework along with developments in the forefront of computational technology and biomedical research to address key challenges in biomedicine. Ayurveda Developmental Therapeutic Program is one such international collaborative effort between scientists in multi-disciplinary areas of expertise. Its aim is to examine Ayurveda, the principle of whole person health as an effective scientific system of medicine, and its application alone or in conjunction with conventional medicine, primarily in the areas of cancer management and therapy. The collaboration spans Ayurveda, western or conventional  medical science, multi-omics and high throughput computational technology including whole systems biology, digital twins, quantum computing and plant based drug design. This talk will cover some of this journey in the hope of opening avenues for further collaborations.

Use of herbal medicines for therapeutic purposes has a long recorded history. Despite therapeutic potential, these traditional medicines are not accepted globally because of the unknown composition and mechanism of action. Therefore, a rigorous analysis by modern science is required in order to provide a proof-of-concept for their therapeutic efficacy. In line with this, I look into bioactive compounds from natural sources as therapeutic agents.

The advent of personal biomedical digital twins is bringing together biopharma, the patient, the physician, and the community to improve the precision medicine of the individual. With many scientific advances and new technologies, the time is right for a global collaborative network to improve outcomes for each patient, using predictive models, real-world health information, and medical innovations. This presentation will share a path forward to reach this goal.

Genomic-based testing based on variant analysis is glowingly integrated in healthcare practice. The increasing demands for analyzing more samples quickly, integrating more knowledge bases, and periodic re-analysis of samples pose challenges that if not handled carefully would lead to missing mutations, wrong outdated classification, and interpretation errors. Learn how artificial intelligence techniques in the era of ChatGPT, coupled with big data, can address these problems and improve overall performance.

Bioinformatic methodologies play a vital role in unraveling the intricate mechanisms and pathogenesis underlying complex diseases. Recognizing that diseases are dynamic processes rather than static entities, it becomes imperative to employ methods that can effectively analyze and extract temporal features from molecular and clinical data to facilitate drug discovery in the realm of precision medicine. This presentation introduces a novel set of tools based on nonlinear dynamics and applied bifurcation theory that can be seamlessly integrated into the drug discovery pipelines.