Sequence Analysis: DNA Sequence Alignment Using Transformer Models

Academic Background DNA sequence alignment is a core task in genomics, aiming to map short DNA fragments (reads) to the most probable locations on a reference genome. Traditional methods typically involve two steps: first, indexing the genome, followed by efficient searching to locate potential positions for the reads. However, with the exponential...

Deep-Learning-Enhanced Metal-Organic Framework E-Skin for Health Monitoring

Deep Learning-Enhanced Metal-Organic Framework E-Skin for Health Monitoring Academic Background Electronic skin (e-skin) is a technology capable of sensing physiological and environmental stimuli, mimicking human skin functions. In recent years, the potential applications of e-skin in fields such as robotics, sports science, and healthcare monitori...

Deep Learning of Imaging Phenotype and Genotype for Predicting Overall Survival Time of Glioblastoma Patients

Deep Learning of Imaging Phenotype and Genotype for Predicting Overall Survival Time of Glioblastoma Patients

Globally, the most common and deadly malignant brain tumor is glioblastoma (Glioblastoma, GBM). In recent years, research has continuously attempted to predict the overall survival time (OS) of GBM patients using machine learning techniques based on preoperative single-modality or multi-modality imaging phenotypes. Although these machine learning m...

A Transformer-Based Approach Combining Deep Learning Network and Spatial-Temporal Information for Raw EEG Classification

Research Background and Objectives In recent years, Brain-Computer Interface (BCI) systems have been widely applied in the fields of neuroengineering and neuroscience. Electroencephalogram (EEG), as a data tool reflecting the activities of different neuronal groups in the central nervous system, has become a core research topic in these fields. How...