The University of Tokyo · 생화학·유전·분자생물학
Alok Sharma 교수의 연구실은 생물정보학, 오믹스 데이터 분석 및 뇌-컴퓨터 인터페이스 기술을 중심으로, 유전자 및 생물 신호 데이터에서 숨겨진 패턴을 식별하고 정밀의료 및 실시간 신경 신호 분류에 응용하는 데 초점을 맞추고 있습니다. 특히, 딥러닝 기반의 이미지 변환 기법과 CNN, LSTM 등 신경망 아키텍처를 활용해 복잡한 생물학적 데이터의 구조적 특징을 효과적으로 추출합니다. 또한, 식물 성장 촉진 및 병원균 억제 기능을 가진 박테리아에서 유래한 생물활성 물질의 규명도 함께 진행하고 있습니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
It is critical, but difficult, to catch the small variation in genomic or other kinds of data that differentiates phenotypes or categories. A plethora of data is available, but the information from its genes or elements is spread over arbitrarily, making it challenging to extract relevant details for identification. However, an arrangement of similar genes into clusters makes these differences more accessible and allows for robust identification of hidden mechanisms (e.g. pathways) than dealing
The field of omics, driven by advances in high-throughput sequencing, faces a data explosion. This abundance of data offers unprecedented opportunities for predictive modeling in precision medicine, but also presents formidable challenges in data analysis and interpretation. Traditional machine learning (ML) techniques have been partly successful in generating predictive models for omics analysis but exhibit limitations in handling potential relationships within the data for more accurate predic
Brain-computer interface (BCI) systems having the ability to classify brain waves with greater accuracy are highly desirable. To this end, a number of techniques have been proposed aiming to be able to classify brain waves with high accuracy. However, the ability to classify brain waves and its implementation in real-time is still limited. In this study, we introduce a novel scheme for classifying motor imagery (MI) tasks using electroencephalography (EEG) signal that can be implemented in real-
Supplementary data are available at Bioinformatics online.
Supplementary data are available at Bioinformatics online.
A detailed screening of bacterial isolates from the Central Himalayan region for plant growth promotion and antimycelial activity against Pythium and Phytophthora strains afforded seven isolates, of which three were particularly effective against the incidence of damping-off in field trials on chilli and tomato. In this investigation an initial spectroscopic survey of the methanolic extracts of the seven bacterial isolates showed complex mixtures except for Pseudomonas sp. GRP3, one of the most
Artificial intelligence methods offer exciting new capabilities for the discovery of biological mechanisms from raw data because they are able to detect vastly more complex patterns of association that cannot be captured by classical statistical tests. Among these methods, deep neural networks are currently among the most advanced approaches and, in particular, convolutional neural networks (CNNs) have been shown to perform excellently for a variety of difficult tasks. Despite that applications
These results demonstrate that tyrosine phosphorylation of p130Cas is sufficient for its localization to focal adhesions and for activation of downstream signaling events associated with cell migration. FIT provides a valuable tool to evaluate the contribution of individual components of the response to signals with multiple outputs, such as activation of NTKs.
