Waseda University · 면역·미생물학
히토시 이우치 교수의 연구실은 생물학적 시퀀스 데이터, 특히 염기서열과 단백질 발현 시계열 데이터의 분석에 초점을 맞추고 있습니다. 고속 시퀀싱 기술로 급격히 증가하는 오미크스 데이터를 효과적으로 해석하기 위해 자연어처리(NLP) 기반의 표현 학습 기법과 주기성 패턴 탐지 알고리즘을 응용합니다. 특히 시간 경과에 따른 단백질 발현의 진동 패턴을 탐지하는 MICOP 알고리즘 개발과, 단일세포 RNA 시퀀싱 데이터에서 세포의 동적 변화를 추론하는 데 초점을 맞춘 분석 기법을 개발하고 있습니다. 이는 감염병의 숙주-바이러스 상호작용 이해 및 유전자 발현의 시간적 동역학 규명에 기여합니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
Although remarkable advances have been reported in high-throughput sequencing, the ability to aptly analyze a substantial amount of rapidly generated biological (DNA/RNA/protein) sequencing data remains a critical hurdle. To tackle this issue, the application of natural language processing (NLP) to biological sequence analysis has received increased attention. In this method, biological sequences are regarded as sentences while the single nucleic acids/amino acids or k-mers in these sequences re
The coronavirus disease-2019 (COVID-19) pandemic has elucidated major limitations in the capacity of medical and research institutions to appropriately manage emerging infectious diseases. We can improve our understanding of infectious diseases by unveiling virus-host interactions through host range prediction and protein-protein interaction prediction. Although many algorithms have been developed to predict virus-host interactions, numerous issues remain to be solved, and the entire network rem
In this paper, we presented MICOP, which is an MIC-based algorithm, for predicting periodic patterns in large-scale time-resolved protein expression profiles. The performance test using artificially generated simulation data revealed that the performance of MICOP for decaying data was superior to that of the existing widely used methods. It can reveal novel findings from time-series data and may contribute to biologically significant results. This study suggests that MICOP is an ideal approach f
ABSTRACT Remarkable advances in high-throughput sequencing have resulted in rapid data accumulation, and analyzing biological (DNA/RNA/protein) sequences to discover new insights in biology has become more critical and challenging. To tackle this issue, the application of natural language processing (NLP) to biological sequence analysis has received increased attention, because biological sequences are regarded as sentences and k-mers in these sequences as words. Embedding is an essential step i
Time-course experiments using parallel sequencers have the potential to uncover gradual changes in cells over time that cannot be observed in a two-point comparison. An essential step in time-series data analysis is the identification of temporal differentially expressed genes (TEGs) under two conditions (e.g. control versus case). Model-based approaches, which are typical TEG detection methods, often set one parameter (e.g. degree or degree of freedom) for one dataset. This approach risks model
Abstract Motivation In recent years, single-cell RNA sequencing (scRNA-seq) has provided high-resolution snapshots of biological processes and has contributed to the understanding of cell dynamics. Trajectory inference has the potential to provide a quantitative representation of cell dynamics, and several trajectory inference algorithms have been developed. However, the downstream analysis of trajectory inference, such as the analysis of differentially expressed genes (DEG), remains challenging
