A5114723607- Computational Drug Discovery Methods
- Epilepsy research and treatment
- Cancer-related molecular mechanisms research
- EEG and Brain-Computer Interfaces
- Blind Source Separation Techniques
- Advanced Image Processing Techniques
- Microbial Natural Products and Biosynthesis
- Domain Adaptation and Few-Shot Learning
- Cytokine Signaling Pathways and Interactions
- Forecasting Techniques and Applications
- Cell Image Analysis Techniques
- Gene expression and cancer classification
- SARS-CoV-2 and COVID-19 Research
- Imbalanced Data Classification Techniques
- Image Enhancement Techniques
- Bioinformatics and Genomic Networks
- Image and Signal Denoising Methods
- Multimodal Machine Learning Applications
Nanjing University
2020-2024
Minzu University of China
2024
Multivariate time series data comprises various channels of variables. The multivariate forecasting models need to capture the relationship between accurately predict future values. However, recently, there has been an emergence methods that employ Channel Independent (CI) strategy. These view as separate univariate and disregard correlation channels. Surprisingly, our empirical results have shown trained with CI strategy outperform those Dependent (CD) strategy, usually by a significant...
Abstract Background Querying drug-induced gene expression profiles with machine learning method is an effective way for revealing drug mechanism of actions (MOAs), which strongly supported by the growth large scale and high-throughput databases. However, due to lack code-free user friendly applications, it not easy biologists pharmacologists model MOAs state-of-art deep approach. Results In this work, a newly developed online collaborative tool, Genetic profile-activity relationship (GPAR)...
Differing from traditional semi-supervised learning, class-imbalanced learning presents two distinct challenges: (1) The imbalanced distribution of training samples leads to model bias towards certain classes, and (2) the unlabeled is unknown potentially that labeled samples, which further contributes class in pseudo-labels during training. To address these dual challenges, we introduce a novel approach called Twice Class Bias Correction (TCBC). We begin by utilizing an estimate...
Epilepsy is a group of neurological disorders characterized by epileptic seizures, and it affects tens millions people worldwide. Currently, the most effective diagnostic method employs monitoring brain activity through electroencephalogram (EEG). However, critical to predict seizures in patients prior their onset, allowing for administration preventive medications before seizure occurs. As pivotal application artificial intelligence medical treatment, learning features EEGs epilepsy...
The novel coronavirus disease, named COVID-19, emerged in China December 2019, and has rapidly spread around the world. It is clearly urgent to fight COVID-19 at global scale. development of methods for identifying drug uses based on phenotypic data can improve efficiency development. However, there are still many difficulties applications cell picture data. This work reported one state-of-the-art machine learning method identify image features 1024 drugs generated LINCS program. Because...