Zhenyu Yang

AlphaFold2 (AF2) is an artificial intelligence (AI) system developed by DeepMind that can predict three-dimensional (3D) structures of proteins from amino acid sequences with atomic-level accuracy. Protein structure prediction is one of the most challenging problems in computational biology and chemistry, and has puzzled scientists for 50 years. The advent of AF2 presents an unprecedented progress in protein structure prediction and has attracted much attention. Subsequent release of structures of more than 200 million proteins predicted by AF2 further aroused great enthusiasm in the science community, especially in the fields of biology and medicine. AF2 is thought to have a significant impact on structural biology and research areas that need protein structure information, such as drug discovery, protein design, prediction of protein function, et al. Though the time is not long since AF2 was developed, there are already quite a few application studies of AF2 in the fields of biology and medicine, with many of them having preliminarily proved the potential of AF2. To better understand AF2 and promote its applications, we will in this article summarize the principle and system architecture of AF2 as well as the recipe of its success, and particularly focus on reviewing its applications in the fields of biology and medicine. Limitations of current AF2 prediction will also be discussed.

Deep learning has achieved significant success on intelligent medical treatments, such as automatic diagnosis and analysis of medical data. To train an automatic diagnosis system with high accuracy and strong robustness in healthcare, sufficient training data are required when using deep learning-based methods. However, given that the data collected by sensors that are embedded in medical or mobile devices are inadequate, it is challenging to train an effective and efficient classification model with state-of-the-art performance. Inspired by generative adversarial networks (GANs), we propose TS-GAN, a Time-series GAN architecture based on long short-term memory (LSTM) networks for sensor-based health data augmentation, thereby improving the performance of deep learning-based classification models. TS-GAN aims to learn a generative model that creates time-series data with the same space and time dependence as the real data. Specifically, we design an LSTM-based generator for creating realistic data and an LSTM-based discriminator for determining how similar the generated data are to real data. In particular, we design a sequential-squeeze-and-excitation module in the LSTM-based discriminator to better understand space dependence of real data, and apply the gradient penalty originated from Wasserstein GANs in the training process to stabilize the optimization. We conduct comparative experiments to evaluate the performance of TS-GAN with TimeGAN, C-RNN-GAN and Conditional Wasserstein GANs through discriminator loss, maximum mean discrepancy, visualization methods and classification accuracy on health datasets of ECG_200, NonInvasiveFatalECG_Thorax1, and mHealth, respectively. The experimental results show that TS-GAN exceeds other state-of-the-art time-series GANs in almost all the evaluation metrics, and the classifier trained on synthetic datasets generated by TS-GAN achieves the highest classification accuracy of 97.50% on ECG_200, 94.12% on NonInvasiveFatalECG_Thorax1, and 98.12% on mHealth, respectively.

With the rapid development of the Internet of Things (IoTs) and mobile communications, mobile devices have become indispensable in our daily lives. Given the substantial amount of private information stored on these devices, the security of mobile devices has emerged as a significant concern for users. Different from conventional methods such as PINs, fingerprints, and face IDs, which authenticate users only during the initial login stage, continuous authentication ensures consistent verification while mobile devices are in use. Current continuous authentication methods require extensive data from a series of users for effective training. Nevertheless, it is challenging to collect sufficient amount of data within a limited time. In this paper, we propose CALL, an unsupervised sensor-based Continuous Authentication system with a Low-rank transformer using Learning-to-rank algorithms. The lightweight CALL is capable of providing both spatial and temporal features for end-to-end authentication. Specifically, CALL utilizes time series data from a legitimate user, collected by the accelerometer, gyroscope, and magnetometer sensors on smartphones, to train a pure one-dimensional autoencoder for spatial features and a shuffle low-rank Transformer (SLRT) for temporal features in the training phase. In the authentication phase, the trained pure one-dimensional autoencoder captures spatial features by reconstructing input data to obtain the reconstruction error, and SLRT captures temporal features by predicting a ranking vector that reveals the order of the shuffled feature sequence. The predicted ranking vector is then used to recover the shuffled sequence and the similarity between the frequency spectrum sequences of the recovered sequence and the original time series data is calculated. The reconstruction error and similarity are compared against pre-defined thresholds, and CALL authenticates a user as legitimate only if both values fall below their respective thresholds. Finally, we evaluate the performance of CALL on UCI_HAR, WISDM_HARB, and our dataset, and the extensive experiments illustrate that CALL reaches the best performance with 96.43%, 95.24% and 96.92% accuracy, and 4.28%, 4.76% and 3.86% EERs on the three datasets, outperforming state-of-the-art continuous authentication methods.