The University of Tokyo · Biochemistry, Genetics and Molecular Biology
Professor Alok Sharma's research lab specializes in computational biology and bioinformatics, focusing on leveraging deep learning and machine learning to decode complex biological data. The lab develops innovative computational frameworks—such as DeepInsight and OPTICAL—that transform genomic and neurophysiological data into structured formats amenable to analysis by convolutional and recurrent neural networks. Their work bridges omics data integration, brain-computer interface development, and microbial metabolite discovery, emphasizing real-time, accurate classification and predictive modeling. The lab’s interdisciplinary approach integrates computational neuroscience, systems biology, and bioprospecting for agricultural biotechnology.
Figures are computed from collected data and may differ slightly.
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.
Open papers in the app to read, cite, and organize with AI.
