BII Scientific Conference 2022

Presentation Details :

Speaker: Sebastian Maurer-Stroh
Abstract: Sebastian Maurer-Stroh, the Executive Director at Bioinformatics Institute opened the BII Scientific Conference 2022 by giving a brief overview of BII. He shared the overall goals in 5 years, the BII winning strategy - Adding Value to Data and the new structure of the institute.

Speaker: Michael Levitt
Title: Lessons From 790 Days Obsessed With Covid-19
Abstract: Michael Levitt shared on how he was involved with the COVID-19 outbreak from the start. He talked about the different growth functions in his research, with respect to varied aspects of the real Covid-19 outbreaks worldwide. Besides predicting trajectories of single Covid-19 Outbreaks in his study, he also shared about the original best line predictions method and Omicron outbreaks in real time.

Abstract: This talk shows how expertise in sequence analysis bridging quickly to 3D structure and protein function can have impact in applications areas from viruses to shampoo, novel food, flavors, drugs and human diseases.

To learn more about the research they do, please visit: Protein Sequence and Analysis Group Page

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Abstract: Our lab investigates structure and function of RNA. RNA plays many roles in biological systems spanning genetic, enzymatic and regulatory functions. Many of these roles depend on spatial structure or specific RNA-RNA interactions. We follow an integrative approach to analyze data obtained by sequencing, NMR, X-ray crystallography, chemical probing and crosslinking experiments as well as molecular simulations. Relating structure, dynamics and function allows us to identify RNA elements that are crucial for the life cycle of viruses. In this overview, we show recent results of our research into RNA interactions and structure in Dengue, Zika, SARS-CoV-2, Chikungunya and Influenza viruses.

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Abstract: Modern omics technologies generate large datasets. Biostatistics and data analytics have been instrumental for deciphering key features of those data. However, are there statistical laws hidden within the datasets? Here I will show some examples based on gene expressions datasets, followed by the usefulness of those laws for data inference.

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Abstract: In this video, I share how my group uses computational tools to complement experimental efforts in chemical biology. A major theme of our research is the application of fragment-based concepts to computer-aided ligand/drug design.

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Abstract: This talk gives a brief overview of our progress in multiscale modelling of the envelopes of bacteria and viruses (including dengue and SARS-CoV-2) and their interactions with host immunity. I also describe recent developments in novel antibiotic and antibody-based therapeutics.

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Abstract: The research focus of Fan Lab is to develop computational techniques to study protein-ligand interactions. The developed methods are used in basic research, to help understand and regulate biological processes, in particular, structure-function mechanisms of GPCRs, transporters, and kinases. Meanwhile, these methods were also employed in applied research, to help engineer industrial enzymes, evaluate toxicity of chemicals including food ingredients, and discover new drug leads.

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Abstract: Working at the interface between the physics and biology, we develop physical models and computational approaches for modelling structures of biomolecules and for predicting their functional characteristics. We verify our models and predictions in collaboration with experimentalists. We highlight today two major areas of research pursued in the group: (i) protein allostery; (ii) biophysics of chromatin and epigenetic regulation. Other research topic in the group include, but not limited to, evolution of protein function, evolutionary-inspired design of protein function, molecular mechanisms of adaptation. 

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Abstract: Translating molecular simulations to the clinic - This video outlines how careful applications of advanced biomolecular simulations combined with experiments are opening new windows into clinical applications for diseases ranging from cancer to bacterial infections.

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Abstract: I will briefly introduce the biophysical modeling capabilities of the group in the context of using behavioral tracking to assess the progression of neurodegenerative diseases.  Many neurodegenerative diseases such as Alzheimer’s disease and Parkinson’s disease are characterized by changes in an individual’s behaviors.  We propose to analyze and track such behaviorial changes by using machine learning to estimate the pose of an individual from videos and then to classify behavior from the pose information.  We demonstrate proof of concept application to videos of interacting mice and showed that we can achieve accuracy comparable to manual annotation of behaviors.

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Abstract: The Complex Cellular Phenotype Analysis Group develops and uses novel phenotypic and molecular profiling methods to elucidate the mode of actions (MoAs) of xenobiotics, and build computational models that can predict in vivo effects based on these MoAs. Our current research focuses on building bioimage databases and visualization tools, assessing chemical/drug safety and efficacy, and digital medicine for cancer.

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Abstract: Our lab focuses on the development of tools, solutions and models to enable the AI-based Digital pathology diagnosis. This video shares with you some of our recent works/projects.

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Abstract: Our vision is to “Advance Biomedical research by developing quantitative biological analysis using modern techniques of data analytics”, to “Support Local Research”; and towards our projects in Strategic National Research programs like GUSTO; GeriLAB, IMMACULATE, PETRI, Mental health studies etc. Our focus areas are Quantitative Biology (clinical), Radiomics /Computer-aided diagnosis systems, Develop AI-enabled SaMD framework for clinical translation, and Population based studies.

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Abstract: In this video, we share a spectrum of AI work that are relevant in clinical research. We also place emphasis on the development of theoretical AI methodologies that are inspired by real world clinical applications.

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Abstract: Xing Yi Woo heads the newly-formed Research Data Integration Group of the Biomedical Datahub in BII. In this presentation, she discusses how multimodal data analysis and integration of cancer datasets can improve translational outcomes, highlighting results from her previous research and new research data integration plans to drive cancer research in BII.

To learn more about the research they do, please visit: Research Data Integration.

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Abstract: The Biomedical Data Architecture & Repository of BII operationalises a SSSO (Standard Systems Support Office) for clinical research data management and collaboration within health clusters. This presentation showcases our latest effort in Synthetic Data generation - a much sought after strategy that safeguards the privacy of healthcare data owners without the usual utility trade-offs. Via copulas, we demonstrate high preservation of linear relationships between marginal distributions of a local clinical dataset and the potential of synthetic data in accelerating collaborative research efforts.

To learn more about the research they do, please visit: BioMedical Data Architecture & Repository.

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Abstract: It is becoming apparent that changes in human exposures and behaviors are shaping the health adversities of future. Also, the availability of big data and multi-omics technologies are providing deeper insights into human variation and disease susceptibility. Our multidisciplinary team specializes in building multi-omics roadmaps and using systems approaches to identify biomarkers and interventions linked with health adversities (eg. Metabolic disorders and mental health) and suboptimal lifestyle that are currently plaguing the global health and economy. During our research journey we have established distinguished collaborations and delivered tangible scientific advances to multiple consortia, cohort studies, intervention trials, national platforms and nutrition industries.

To learn more about the research they do, please visit: Clinical Data Engagement.

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