Zobrazeno 1 - 10
of 39
pro vyhledávání: '"Matsubara, Yasuko"'
Autor:
Yang, Ziwei, Chen, Zheng, Liu, Xin, Kotoge, Rikuto, Chen, Peng, Matsubara, Yasuko, Sakurai, Yasushi, Sun, Jimeng
Retrieving gene functional networks from knowledge databases presents a challenge due to the mismatch between disease networks and subtype-specific variations. Current solutions, including statistical and deep learning methods, often fail to effectiv
Externí odkaz:
http://arxiv.org/abs/2410.13178
Autor:
Kotoge, Rikuto, Chen, Zheng, Kimura, Tasuku, Matsubara, Yasuko, Yanagisawa, Takufumi, Kishima, Haruhiko, Sakurai, Yasushi
While end-to-end multi-channel electroencephalography (EEG) learning approaches have shown significant promise, their applicability is often constrained in neurological diagnostics, such as intracranial EEG resources. When provided with a single-chan
Externí odkaz:
http://arxiv.org/abs/2410.11200
Recent normalization-based methods have shown great success in tackling the distribution shift issue, facilitating non-stationary time series forecasting. Since these methods operate in the time domain, they may fail to fully capture the dynamic patt
Externí odkaz:
http://arxiv.org/abs/2410.01860
Multivariate time series data suffer from the problem of missing values, which hinders the application of many analytical methods. To achieve the accurate imputation of these missing values, exploiting inter-correlation by employing the relationships
Externí odkaz:
http://arxiv.org/abs/2409.09930
Machine learning has shown great potential in the field of cancer multi-omics studies, offering incredible opportunities for advancing precision medicine. However, the challenges associated with dataset curation and task formulation pose significant
Externí odkaz:
http://arxiv.org/abs/2409.02143
Autor:
Piao, Xihao, Gao, Pei, Chen, Zheng, Zhu, Lingwei, Matsubara, Yasuko, Sakurai, Yasushi, Sun, Jimeng
The medical community believes binary medical event outcomes in EHR data contain sufficient information for making a sensible recommendation. However, there are two challenges to effectively utilizing such data: (1) modeling the relationship between
Externí odkaz:
http://arxiv.org/abs/2408.09410
The Transformer model has shown leading performance in time series forecasting. Nevertheless, in some complex scenarios, it tends to learn low-frequency features in the data and overlook high-frequency features, showing a frequency bias. This bias pr
Externí odkaz:
http://arxiv.org/abs/2406.09009
The advent of generative AI tools has had a profound impact on societies globally, transcending geographical boundaries. Understanding these tools' global reception and utilization is crucial for service providers and policymakers in shaping future p
Externí odkaz:
http://arxiv.org/abs/2405.20037
Subsequence clustering of time series is an essential task in data mining, and interpreting the resulting clusters is also crucial since we generally do not have prior knowledge of the data. Thus, given a large collection of tensor time series consis
Externí odkaz:
http://arxiv.org/abs/2402.11773
Precision medicine fundamentally aims to establish causality between dysregulated biochemical mechanisms and cancer subtypes. Omics-based cancer subtyping has emerged as a revolutionary approach, as different level of omics records the biochemical pr
Externí odkaz:
http://arxiv.org/abs/2308.09725