Prof. Limsoon Wong (ACM Fellow)

National University of Singapore

Limsoon Wong is Kwan Im Thong Hood Cho Temple Professor in Computing and Professor (by courtesy) in Medicine at the National University of Singapore. He is currently most interested in data analysis problems in biology and medicine that involve high-resolution omics experiments, especially those that are plagued by heavy batch effects and/or dominated by confounding noises. He is an ACM Fellow, inducted in 2013 for his contributions to database theory and computational biology. His technical contributions to these distinct fields have been recognized by several awards, e.g., the 2003 FEER Asian Innovation Gold Award for his work on treatment optimization of childhood leukemias, and the ICDT 2014 Test of Time Award for his work on naturally embedded query languages. Limsoon co-founded Molecular Connections (Molcon) in Bangalore to offer efficient high-quality information curation services; Molcon has grown organically about 400x over two decades and has been consistently recognized in the recent years as a “100 best companies in India for women to work”.

Speech Title: "Illuminating the Twilight Zone of Protein Function Prediction"

Abstract: Generally, if two proteins are quite similar in their amino acid sequence, they would have a common ancestor and would have inherited their function from that ancestor. Thus, if one knows the function of one of these two proteins, one can infer the function of the other protein. However, at sequence similarity below 30% (aka the twilight zone), this way of inferring protein function is accompanied by an explosion of false positives. Several deep learning methods have been proposed as solutions. Do these approaches work? In the first part of this talk, we discuss some nuances in classifier performance evaluation that are often overlooked and show that such classifiers, if deployed, would result in disappointments. In the second part of this talk, we introduce a novel approach, EnsembleFam, which can perform reliable protein family assignment even in the twilight zone. EnsembleFam is unusual among protein function prediction approaches in that it uses the concept of "guilt by association of the similarity of dissimilarities" to exploit information that is normally discarded or ignored (by other methods).

 

Prof. Bing Zhang

Shanghai Jiao Tong University, China

Bing Zhang, PhD, MD; Professor Shanghai Jiao Tong University, National Science Fund for Distinguished Young Scholar, One Thousand Young Talent Awardee. The executive member of China’s association of development biology, the member of Shanghai Cell Biology Council, the member of the Translational and Basic Cardiovascular Science Council, Asian Heart Society, the assessment experts of China’s Ministry of Science and Technology, NSFC and RGC Hong Kong. His research interest is understanding the fundamental mechanisms of cardiovascular regeneration and critical illness, innovating the gene editing technique and nuclear acid & gene therapy drug development. He has published more than 40 research articles in the high-profile journals such as Cell research, Circulation, Circulation Research, JCI, Genome Research, Genome Biology and Blood etc.

Speech Title: "Gene Editing: a new hope for Familial Cardiomyopathy"

Abstract: Familial cardiomyopathy is the most common inherent cardiac disorders affecting heart muscle. Due to the lack of specific medicines, current therapies on familial cardiomyopathy are running in a dilemma, and most familial cardiomyopathy patients end with catastrophic clinic complications: heart failure, arrhythmia and sudden cardiac death et.al. To tackle this therapeutic plight, Dr. Bing Zhang’s lab have developed variable gene editing tools with high efficiency and high-precision and tested them in different subtypes of familial cardiomyopathies bearing with distinct genetic variants. Their results demonstrate gene edition precisely correct the genetic variants in the cardiomyocytes and be able to effectively improve cardiac function and prevent the cardiomyopathy. Their works further demonstrate long-term application of gene editor that are carefully optimized is safe in vivo. In this seminar, Dr. Zhang will talk about their founding and progressions in this exciting area.

 

Invited Speaker

Prof. Pui-Chi Gigi Lo

City University of Hong Kong

Prof. Pui-Chi Gigi Lo is a distinguished medicinal chemist, currently serving as an Associate Professor in the Department of Biomedical Sciences at City University of Hong Kong (CityU). She began her academic journey at The Chinese University of Hong Kong (CUHK), where she earned both her BSc degree in chemistry and PhD. Her postdoctoral fellowship at the Ontario Cancer Institute sparked her interest in smart theranostic agents for cancer diagnosis and treatment. After working as a Research Assistant Professor at CUHK, she joined CityU in 2015. Her research focuses on the development of smart photosensitizers for targeted photodynamic therapy and stimuli-responsive polymeric micelles as multifunctional nanocarriers. She also explores the design of nanodrugs for cancer therapy and targeted protein degradation by photosensitization triggering ferroptosis for enhanced antitumor immunity. Prof. Lo's contributions to her field have been recognized with numerous awards, including Stanford’s top 2% most highly cited scientists 2022 and 2023, Hong Kong Institute for Advanced Study (HKIAS) Young Rising Star Lectureship, and the Society of Porphyrins and Phthalocyanines Young Investigator Award. She has published over 100 papers in international peer-reviewed journals, boasting an H-index of 41. Her work continues to push the boundaries of cancer research and treatment.

Speech Title: "Enhancing the Tumor Specificity and Modulating the Oxygen Content for Advanced Photodynamic Therapy"

Abstract: Photodynamic therapy (PDT) utilizes reactive oxygen species (ROS) for elimination of malignant cells and tissues. These highly reactive cytotoxic species can be generated through the excitation of photosensitizers by light, followed by the interactions with the endogenous oxygen. This modality is now a clinically approved procedure for the treatment of a variety of localized and superficial cancers. However, there are still some hurdles to be addressed in order to promote its clinical translation. The effectiveness of PDT is largely contingent on two factors, namely the tumor specificity of the photosensitizers and the oxygen concentration in the tumor microenvironment. If these two conditions are not optimized, the therapeutic outcome of PDT could be compromised. In this presentation, we will highlight our recent endeavours to address these limitations of PDT, including the construction of double-locked photodynamic molecular beacons, utilization of a bioorthogonal strategy to actualize targeted delivery and site-specific activation of photosensitizers, and development of oxygen-replenishing and oxygen-economized photosensitizing systems.

Asst. Prof. Mengsha Tong

Xiamen University

Mengsha Tong is Assistant Professor in Bioinformatics at School of Life Sciences, National Institute for Data Science in Health and Medicine, Xiamen University. She received her PhD in biology from Tsinghua University. Her research interests include precision oncology and medical big data analysis. As the principal investigator, she has led projects funded by the National Natural Science Foundation of China for Young Scientists, the Young Science and Technology Talents Innovation Project of Fujian Province, and the Young Innovation Project of the Xiamen University President's Fund. She has published research articles in international journals such as EBioMedicine, Cell reports Methods, Briefings in Bioinformatics, Oncogenesis, and iScience. She was awarded the Rising Star at the π-HuB Global Summit and earned the first prize in the 18th Young Teachers Teaching Competition at Xiamen University. As the Director of the Office in Fujian Bioinformatics Society, she make efforts to promote the development of bioinformatics.

Speech Title: "Tracing Unknown Tumor Origins with A Biological Pathway-based Transformer Model"

Abstract: Cancer of unknown primary (CUP) represents metastatic cancer where the primary site remains unidentified despite standard diagnostic procedures. To determine the tumor origin in such cases, we developed BPformer, a deep learning method integrating the transformer model with prior knowledge of biological pathways. Trained on transcriptomes from 10,410 primary tumors across 32 cancer types, BPformer achieved remarkable accuracy rates of 94%, 92%, and 89% in primary tumors and primary and metastatic sites of metastatic tumors, respectively, surpassing existing methods. Additionally, BPformer was validated in a retrospective study, demonstrating consistency with tumor sites diagnosed through immunohistochemistry and histopathology. Furthermore, BPformer was able to rank pathways based on their contribution to tumor origin identification, which helped to classify oncogenic signaling pathways into those that are highly conservative among different cancers versus those that are highly variable depending on their origins.

Prof. Jose Nacher

Toho University

He obtained his Ph.D. in Theoretical Physics from Valencia University (Spain) in 2001. From 2003 to 2007, he served as a postdoctoral fellow at the Bioinformatics Center, Institute for Chemical Research, Kyoto University (Japan). During this time, he also held a JSPS-funded research fellowship from 2005 to 2007 at the Bioinformatics Center, Kyoto University. Between 2007 and 2012, he held positions as Lecturer and Associate Professor at the Department of Complex and Intelligent Systems at Hakodate Future University (Japan), while concurrently serving as a visiting Associate Professor at Kyoto University (2011-2012).  In April 2012, he joined Toho University (Japan) as an Associate Professor at the Department of Information Science, Faculty of Science, and since April 2016, he has been serving as a Professor in the same department.

 

 

Previous Speakers

 

Prof. Yuan-Ting Zhang City University of Hong Kong	Prof. Alexander Suvoror Institute of Experimental Medicine, St. Petersburg	Prof. David Zhang The Chinese University of Hong Kong (Shenzhen)
Prof.Tun-Wen Pai National Taipei University of Technology	Prof. Dong-Qing Wei Shanghai Jiaotong University	Prof. TSUI Kwok-Wing Stephen The Chinese University of Hong Kong
Prof. Cathy Wu University of Delaware	Prof. Xuegong Zhang Tsinghua University	Prof. Yi Pan Chinese Academy of Sciences
Prof. Bairong Shen Sichuan University	Prof. Wing-Kin Sung The Chinese University of Hong Kong and Hong Kong Genome Institute	Prof. Chanchal Mitra University of Hyderabad
Assoc. Prof. Jie Zheng ShanghaiTech University	Prof. Peiyu Zhang Henan University	Prof. Zheng Zhou Chinese Academy of Sciences
Prof. Le Zhang Sichuan University	Prof. Fei Guo Central South University	Prof. Bin Liu Beijing Institute of Technology
Mr. Xiaoqiang Li China National GeneBank DataBase	Prof. Guan Ning Lin Shanghai Jiao Tong University	Assoc. Prof. Hon-Cheong So The Chinese University of Hong Kong, Hong Kong