Assoc. Prof. Daniele Takahashi
Bahia Federal University, Brazil
Dr. Daniele Takahashi is an Agronomist Engineer, holding a PhD in Agronomy from the University of São Paulo and a Master's degree from the same institution. Her academic journey began with a degree in Agronomy from the Federal University of São Carlos. With a distinguished career, Dr. Daniele Takahashi has served as the Head of Department at Bahia Federal University from 2020 to 2022. Currently, she is an esteemed Associate Professor at the Federal University of Bahia, where her research delves into the intricate mechanisms of biomolecules. Dr. Daniele Takahashi contributions extend beyond the academic realm. She actively leads projects focused on the development of innovative technologies for teaching biochemistry. Additionally, her commitment to education is underscored by her role as the coordinator of the Pedagogical Support Group at the Health Sciences Institute.
Speech Title: "In Silico Molecular Docking of Agathisflavone Isolated from Brazilian Flora Plants: A Possible Inhibitor for Kinesin Eg5"
Abstract: Enzymes such as kinesin Eg5, a motor protein necessary for cell division, are important therapeutic targets for treating tumors and cancer. Several molecules have been isolated from nature, with Agathisflavone isolated from Brazilian flora plants playing an important role in neuroprotection. This work investigates if Agathisflavone has the potential to inhibit by application of molecular docking methodology. To reach this aim we used Autodock Tools, Autodock Vina, UCSF Chimera, and Discovery Studio 2021 software. Kinesin 3D structure was obtained from the PDB database. Docking analysis was performed using a grid box size 40x40x40 cover allosteric pocket and ADP active site. The small molecules tested: apigenin, kolaflavanone, monastrol morelloflavone, and STLC showed binding at the allosteric pocket with affinity energy varying from -8.0 to -10.1 kcal/mol. Agathisflavone showed two possible binding either to the allosteric pocket and ADP active site. Kolaflavanone, monastrol morelloflavone, and STLC are known inhibitors of kinesin Eg5. Agathisflavone pose 1 binding at ADP active site showed an affinity energy of -9.2 kcal/mol. Agathisflavone pose 2 binding at the allosteric site showed an affinity energy of -8.9 kcal/mol. These results suggest that agathisflavone can act as an inhibitor of the biological activity of kinesin Eg5.
Dr. Min Zhao
University of the Sunshine Coast, Australia
Dr. Min Zhao’s research focuses on the investigation of regulatory machines in aquatic genomes and disease genomes, primarily cancer. In addition to 4 review manuscripts, Dr. Zhao have published 98 research papers in peer-reviewed international journals including 62 first and corresponding author papers in total. In total, my papers have been cited over 4700+ times (H-index 32). After obtaining his PhD degree from the Center for Bioinformatics, Peking University, he worked as a postdoctoral researcher for 5 years at the Chinese Academy of Sciences and Vanderbilt University, U.S.A. In 2014, Dr. Zhao joined the University of the Sunshine Coast, and received tenure as senior lecturer in 2019. Dr. Zhao is currently an editorial board member of the international SCI journal Genomics and BMC medical genomics. As the chief investigator, Dr. Min Zhao has received research grants from the Australian Research Council (ARC) and the Reef2050.
Speech Title: "TICCI: A Novel Bioinformatics Tool for Trajectory Inference with Cell-Cell Interaction at Single Cell Level"
Abstract: Understanding the cellular dynamics of cell differentiation and development is a pivotal focus in single cell transcriptome analysis. The existing cell differentiation trajectory inference algorithms face challenges such as high dimensionality, noise, and dependence on external biological information. This talk introduces TICCI (Trajectory Inference with Cell-Cell Interaction), a novel method addressing these challenges by integrating intercellular communication information. Recognizing the crucial role of intercellular communication during development, TICCI proposes CCI (Cell-Cell Interaction) at a single-cell resolution. The hypothesis posits that cells exhibiting higher similarity in gene expression patterns are more likely to exchange information through biomolecular mediators. The TICCI algorithm starts by constructing a cell-neighbourhood matrix, utilizing edge weights composed of cell-cell association probability and CCI information. Louvain partitioning identifies trajectory branches, attenuating noise, while single-cell entropy assesses differentiation status. The Chu-Liu algorithm constructs a directed least-square model to identify cell branches, and an improved diffusion fitted time algorithm computes cell fitted time in non-connected topologies. Validation using TICCI on real scRNA-seq datasets confirms the accuracy of constructed cell trajectories, aligning with genealogical branching and known gene markers. Verification using extrinsic information labels demonstrates the utility of CCI information in enhancing trajectory inference accuracy. Comparative analyses establish TICCI's proficiency in proposing accurate temporal orderings.
Assoc. Prof. Shiyang Ma
Shanghai Jiao Tong University, China
Dr. Shiyang Ma is an Associate Professor at Shanghai Jiao Tong University, School of Medicine and School of Mathematical Sciences. She was a postdoctoral research scientist in the Department of Biostatistics at Columbia University, working with Professor Iuliana Ionita-Laza. She obtained her Ph.D. in Statistics in 2019 from University of Rochester under the supervision of Professors Michael P. McDermott and David Oakes. Her research area is in statistical genetics and clinical trials, and her current work focuses on developing statistical methods for the analysis of high-dimensional genetic data. Dr. Ma has published work in PNAS, Genome Biology, Nature Communications, American Journal of Human Genetics, Statistics in Medicine, and elsewhere.
Speech Title: "Fine-Mapping Gene-Based Associations via Knockoff Analysis of Biobank-Scale Data"
Abstract: Gene-based tests are important tools for elucidating the genetic basis of complex traits. Despite substantial recent efforts in this direction, the existing tests are still limited owing to low power and detection of false positive signals due to the confounding effects of linkage disequilibrium and co-regulation. We propose BIGKnock (BIobank-scale Gene-based association test via Knockoffs), a computationally efficient gene-based testing approach for biobank-scale data, that leverages long-range chromatin interaction data, and performs conditional genome-wide testing via knockoffs. BIGKnock can prioritize causal genes over proxy associations at a locus. We apply BIGKnock to the UK Biobank data with 405,296 participants for multiple binary and quantitative traits, and show that relative to conventional gene-based tests, BIGKnock produces smaller sets of significant genes that contain the causal gene(s) with high probability.
Prof. Xinghua Lu
University of Pittsburgh, USA
Dr. Xinghua Lu completed his medical (MBBS/MD) and MS in Clinical Sciences degrees from Shandong Medical University. He practiced as a physician for a decade. He earned a Ph.D. in Pharmacology from the University of Connecticut. Dr. Lu has broad research experience in clinical and basic biomedical sciences. His research interest concentrates on developing artificial intelligence (AI) methodologies and their application in translational medicine, particularly precision oncology. The ongoing research in his group spans a broad spectrum of translational informatics: using causal inference methods to understand cancer disease mechanisms, using probabilistic graphic models and deep learning models to study cancer signaling pathways, using NLP technology to mine and annotate biomedical texts (publications and medical records), using AI techniques to facilitate clinical decision making.
Speech Title: "From Big Data to Bedside (DB2B): The Role of Artificial Intelligence in Precision Oncology"
Abstract: Cancers are genomic diseases caused by somatic genome alterations (SGAs). The cause and disease mechanisms of tumors are highly individualized and heterogeneous, leading to a broad spectrum of molecular, cellular, and clinical phenotypes. Therefore, a cancer patient's treatment should be individualized based on the disease mechanism of the tumor, i.e., precision oncology. With available big data on cancers, artificial intelligence (AI) will play an indispensable role in transforming genomic, molecular, cellular, and clinical data into knowledge and improving clinical practice. The main utility of AI in precision oncology is twofold: to gain insight into the disease mechanism of individual cancer (causality) and to facilitate clinicians to make optimal clinical decisions. In this talk, I will discuss our research experience in developing Bayesian causal inference and deep learning methods to investigate the disease mechanisms of individual tumors, infer the cellular states of cancer cells, and utilize such mechanism-oriented information to provide support for individualized clinical decisions in real-world practice.
Prof. Zhifu Sun
Mayo Clinic, USA
Dr. Zhifu Sun is a Professor and Consultant in the Division of Computational Biology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota. His research interests are to apply bioinformatics and data sciences to medical research and practice, particularly in cancer etiology, molecular marker identification, and personalized medicine. His major focus has been epigenomics (DNA methylation, histone modification, chromatin interaction, miRNA and long non-coding RNAs) and multi-platform genomic feature integration. He has over 140 peer-reviewed publications, with many in high impact journals. He is an editorial board member for Epigenomics and an associate editor for BMC Cancer and Frontiers in Oncology.