Published in Frontiers in Genetics
Computational drug-target affinity prediction has the potential to accelerate drug discovery. Currently, pre-training models have achieved significant success in various fields due to their ability to train the model using vast amounts of unlabeled data. However, given the scarcity of drug-target interaction data, pre-training models can only be tr...
Published in Measurement Science and Technology
Protein–protein interaction (PPI) plays an important role in the biological process. While, there are limitations of long spend time and high labor cost in traditional lab based PPIs detection approaches. Although many computation-based methods have been proposed for prediction of PPIs, achieving high predictive performance and overcoming low gener...
Published in Frontiers in Medicine
Introduction The prevalence of Renal cell carcinoma (RCC) is increasing among adults. Histopathologic samples obtained after surgical resection or from biopsies of a renal mass require subtype classification for diagnosis, prognosis, and to determine surveillance. Deep learning in artificial intelligence (AI) and pathomics are rapidly advancing, le...
Published in Machine Learning: Science and Technology
Currently, mainstream methods for multi-fidelity data fusion have achieved great success in many fields, but they generally suffer from poor scalability. Therefore, this paper proposes a C32 combination modeling method for complex multi-fidelity data fusion, devoted to solving the modeling problems with three types of multi-fidelity data fusion, an...
Published in Measurement Science and Technology
This paper introduces a learning-based calibration method tailored for microelectromechanical system (MEMS) gyroscopes. The proposed method integrates two linear networks, linked by a parametric rectified linear unit (PReLU), and boasts a compacted architecture with only 25 parameters. This simplicity allows for efficient training on a graphics pro...
This paper deals with the challenge of estimating the turbidity of water samples by processing images captured through the use of a smartphone application designed for both experts and ordinary citizens. The contribution of this paper is threefold.
Firstly, a generic end-to-end monitoring platform is presented. It enables an easy acquisition of the ...
Published in Smart Materials and Structures
The article presents a performance-based comparative analysis of popular deep neural network (DNN) models such as 1-dimensional convolutional neural network (1D-CNN) and long short-term memory (LSTM) for position estimation of shape memory alloy (SMA)-based wire actuator. These DNN models utilize the self-sensing property (SSP) for position estimat...
Published in Frontiers in Medicine
Epilepsy is one of the most frequent neurological illnesses caused by epileptic seizures and the second most prevalent neurological ailment after stroke, affecting millions of people worldwide. People with epileptic disease are considered a category of people with disabilities. It significantly impairs a person’s capacity to perform daily tasks, es...
Published in ITM Web of Conferences
The Internet and communications have rapidly expanded, leading to a significant rise in data generation and heterogeneity. Intrusion detection systems play a crucial role in ensuring the security and integrity of computer systems. These systems have been developed by researchers, academicians, and practitioners to effectively detect and mitigate ne...
Published in Journal of Neural Engineering
Objective. The scarcity of electroencephalogram (EEG) data, coupled with individual and scenario variations, leads to considerable challenges in real-world EEG-based driver fatigue detection. We propose a domain adaptation method that utilizes EEG data collected from a laboratory to supplement real-world EEG data and constructs a cross-scenario and...
