The "International Conference on Translational Information Sciences" is an annual medical informatics event that gathers academicians from Chinese and foreign institutions, professors from renowned universities, and top scholars. With the core mission of serving the "Healthy China 2030 Strategy," it builds an academic exchange platform for the world's top talents and scientists from various countries. The conference aims to discover and recommend scientific and technological innovations, stimulate passion and mission in industry research, promote innovation in China's medical science and health industry, and transform international cutting-edge scientific theories and discoveries into technology and productive forces for the well-being of all humanity.

    The 9th International Conference on Translational Information Sciences (ICTI 2022) was held online on December 2nd and 3rd, 2022. The conference brought together domestic and international experts and scholars in the fields of translational medicine and bioinformatics to discuss the latest research progress and development directions in translational medical informatics, facilitating communication among experts, scholars, corporate product service experts, and decision-makers from relevant platforms.

**Professor Bairong Shen**

    Professor Shen Bairong delivered the opening speech and presented a keynote speech titled "Translational Informatics for Data-Driven and Knowledge-Guided Smart Medicine." He introduced the importance of translational informatics, its definition, and examples, and provided the cultural background and history of the International Conference on Translational Bioinformatics/Informatics (ICTBI/ICTI).

    Professor Shen proposed that translational informatics, as a new paradigm, will promote the future of drugs, vaccines, digital therapies, proactive health, and smart cities.

**Professor Mauno Vihinen**

    Professor Mauno Vihinen discussed how precise machine learning can assist in predicting the disease association of genetic variations. He proposed a framework from scene to application, involving variable explanation, medical application requirements, development of AI/machine learning methods, and prediction of the harmful degree and effect mechanism of variations. He provided key information and suggestions, such as the importance of computed prediction factors in variable explanation and the cautious use of black boxes. His report clarified the direction for personalized genetic variation analysis in the era of precision medicine.

**Dr. Nika Abdollahi**

    Dr. Nika Abdollahi introduced the IMGT system (International ImMunoGenetics information system) and how to use it for: (1) analysis of IG (immunoglobulin) and TR (T cell receptor) genes on the genome loci of jawed vertebrates; (2) two-dimensional and three-dimensional structure analysis of adaptive immune proteins; (3) exploration of IG and TR expression profiles. Her research and report provided a scientific foundation and information support platform for immunoinformatics and future virus prevention and control.

**Professor Jean-Baptiste Cazier**

    Professor Jean-Baptiste Cazier gave a speech on "Explainable Artificial Intelligence (XAI) and Diversity." Professor Cazier concluded that, as a predictive model beyond traditional methods, XAI can identify underlying mechanisms and generate new hypotheses; in the process of using XAI, it is necessary to determine the method of quantifying signals and the verification process; emphasized the role of iterative random forests in data exploration. He used examples to show how bioinformatics has provided a good example for identifying the spectrum of COVID-19 infections and precise virus control.

**Professor Shuaicheng Li**

    Professor Li's speech topic was "Finding Hierarchy in Cell Data." Cells have a nested structure, but most single-cell analyses ignore the nested structure when detecting and displaying functional diversity. The cell hierarchy helps study the diversity of cell functions in subpopulations, clubs (i.e., sub-subpopulations and cell layers).

    Professor Li proposed hierarchical methods, including structural entropy and cell kNN graphs. He also introduced the Structure Entropy hierArchy deTection (SEAT) framework for organizing and splitting club hierarchical structures to find the optimal cell subpopulations from the hierarchy. His report fully demonstrated that big data becomes valuable with algorithms being a key element, reflecting the importance of bioinformatics in translation.

**Professor Mohammad Amjad Kamal**

    Professor Mohammad Amjad Kamal shared his intellectual journey from enzymology to informatics. He introduced the concepts of enzymoinformatics and translational informatics and showcased his achievements during this period. His goal is to discover new and effective drugs for the treatment of most rapidly growing diseases, including metabolic syndrome (diabetes), cancer, Alzheimer's, and Parkinson's, based on his current and previous key research. His personal experience also presented an interesting story of a scholar's happy growth.

**Professor Wang Shuang**

    Professor Wang Shuang discussed how privacy-preserving computation empowers biomedical data sharing and collaboration. Overall, privacy protection is a very important issue for biomedical data sharing and analysis across institutions. There is currently a great need for research and development of privacy-preserving technologies, as well as research on ethical, legal, and social impact issues to bridge the gap between policy and technology. Privacy-preserving models provide practical solutions to confidentiality issues in data sharing processes.

**Professor Yves Lussier**

    Professor Yves Lussier's speech topic was "Breakthroughs in Precision Medicine: Enhancing Statistical Power of Small Clinical Trials through Multiple Single-Subject Experimental Designs (S3)." He concluded that single-subject studies require smaller clinical groups for comparison and classification, enabling smaller clinical trials based on S3 biomarkers; new analyses require a paradigm shift in biomarker design; and there is a need for polygenic biomarkers at the mechanism level rather than single-molecule biomarkers.

**Professor Suhail Rasool**

    Professor Suhail Rasool explored the development of neurodegenerative disease treatment methods. Professor Rasool presented his experimental results, showing that compared with the 3xTg mouse control group (carrying three mutations related to familial Alzheimer's disease: APP Swedish, MAPT P301L, and PSEN1 M146V), Tau immunotherapy significantly reduced tau protein aggregation in tissue pathology, which is related to reduced microglial proliferation and microhemorrhage. This highlights the necessity of preventive treatment for individuals with genetic mutations for Alzheimer's disease (AD) or related tauopathies. Moreover, since the degree of tau protein lesions is much more related to cognitive deficits than the burden of beta-amyloid (Aß), tau protein immunotherapy may have better clinical benefits in late-stage AD than direct targeting of Aß treatment.

    In conclusion, this forum built a bridge of communication for users, advisors, and builders of the translational medical informatics platform, promoting the sharing and exchange of the latest research progress in international and domestic translational informatics.

    The forum is divided into online and offline parts, and the second part is to be expected.