기초과학연구원 의생명수학그룹
In this talk, we discuss the paper "The Large Language Models on Biomedical Data Analysis: A Survey" by Wei Lan et.al, IEEE J. Biomedical and Health Informatics, 2025, at the Journal Club. Abstract With the rapid development of Large Language Model (LLM) technology, it has become an indispensable force in biomedical data analysis research. However, …
Abstract The standard theory of infectious diseases, tracing back to the work of Kermack and McKendrick nearly a century ago, has been a triumph of mathematical biology, a rare marriage of theory and application. Yet the limitations of its most simple representations, which has always been known, have been laid bare in dealing with COVID-19, …
In this talk, we discuss the paper "A biological model of nonlinear dimensionality reduction" by K. Yoshida and T. Toyoizumi, Science Advances, 2025, at the Journal Club. Abstract Obtaining appropriate low-dimensional representations from high-dimensional sensory inputs in an unsupervised manner is essential for straightforward downstream processing. Although nonlinear dimensionality reduction methods such as t-distributed stochastic neighbor …
Abstract Our present healthcare system focuses on treating people when they are ill rather than keeping them healthy. We have been using big data and remote monitoring approaches to monitor people while they are healthy to keep them that way and detect disease at its earliest moment presymptomatically. We use advanced multiomics technologies (genomics, immunomics, …
In this talk, we discuss the paper "Phantom oscillations in principal component analysis" by M. Shinn et.al, PNAS, 2023 at the Journal Club. Abstract Principal component analysis (PCA) is a dimensionality reduction method that is known for being simple and easy to interpret. Principal components are often interpreted as low-dimensional patterns in high-dimensional space. However, …
Abstract Individual human cancer cells often show different responses to the same treatment. In this talk I will share the quantitative experimental approaches my lab has developed for studying the fate and behavior of human cells at the single-cell level. I will focus on the tumor suppressor protein p53, a transcription factor controlling genomic integrity …
In this talk, we discuss the paper "scREADER: Prompting Large Language Models to Interpret scRNA-seq Data" by Cong Li et.al., arxiv, 2024, at the Journal Club. Abstract Large language models (LLMs) have demonstrated remarkable advancements, primarily due to their capabilities in modeling the hidden relationships within text sequences. This innovation presents a unique opportunity in …
Abstract The advent of single-cell transcriptomics has brought a greatly improved understanding of the heterogeneity of gene expression across cell types, with important applications in developmental biology and cancer research. Single-cell RNA sequencing datasets, which are based on tags called universal molecular identifiers, typically include a large number of zeroes. For such datasets, genes with …
Abstract Many natural systems exhibit oscillations that show sizeable fluctuations in frequency and amplitude. This variability can arise from a wide variety of physical mechanisms. Phase descriptions that work for deterministic oscillators have a limited applicability for stochastic oscillators. In my talk I review attempts to generalize the phase concept to stochastic oscillations, specifically, the …
Abstract Spontaneous rhythmic oscillations are widely observed in real-world systems. Synchronized rhythmic oscillations often provide important functions for biological or engineered systems. One of the useful theoretical methods for analyzing rhythmic oscillations is the phase reduction theory for weakly perturbed limit-cycle oscillators, which systematically gives a low-dimensional description of the oscillatory dynamics using only the …
