Highly accurate classification and discovery of microbial protein-coding gene functions using FunGeneTyper: an extensible deep learning framework.

High-throughput DNA sequencing technologies decode tremendous amounts of microbial protein-coding gene sequences. However, accurately assigning protein functions to novel gene sequences remain a challenge. To this end, we developed FunGeneTyper, an extensible framework with two new deep learning models (i.e., FunTrans and FunRep), structured databases, and supporting resources for achieving highly accurate (Accuracy > 0.99, F1-score > 0.97) and fine-grained classification of antibiotic resistance genes (ARGs) and virulence factor genes. Using an experimentally confirmed dataset of ARGs comprising remote homologous sequences as the test set, our framework achieves by-far-the-best performance in the discovery of new ARGs from human gut (F1-score: 0.6948), wastewater (0.6072), and soil (0.5445) microbiomes, beating the state-of-the-art bioinformatics tools and sequence alignment-based (F1-score: 0.0556-0.5065) and domain-based (F1-score: 0.2630-0.5224) annotation approaches. Furthermore, our framework is implemented as a lightweight, privacy-preserving, and plug-and-play neural network module, facilitating its versatility and accessibility to developers and users worldwide. We anticipate widespread utilization of FunGeneTyper (https://github.com/emblab-westlake/FunGeneTyper) for precise classification of protein-coding gene functions and the discovery of numerous valuable enzymes. This advancement will have a significant impact on various fields, including microbiome research, biotechnology, metagenomics, and bioinformatics.

IPGAN: Generating Informative Item Pairs by Adversarial Sampling.

Negative sampling plays an important role in ranking-based recommender models. However, most existing sampling methods cannot generate informative item pairs with positive and negative instances due to two limitations: 1) they merely treat observed items as positive instances, ignoring the existence of potential positive items (i.e., nonobserved items users may prefer) and the probability of observed but noisy items and 2) they fail to capture the relationship between positive and negative items during negative sampling, which may cause the unexpected selection of potential positive items. In this article, we introduce a dynamic sampling strategy to search informative item pairs. Specifically, we first sample a positive instance from all the items by leveraging the overall features of user's observed items. Then, we strategically select a negative instance by considering its correlation with the sampled positive one. Formally, we propose an item pair generative adversarial network named IPGAN, where our sampling strategy is realized in two generative models for positive and negative instances, respectively. In addition, IPGAN can also ensure that the sampled item pairs are informative relative to the ground truth by a discriminative model. What is more, we propose a batch-training approach to further enhance both user and item modeling by alleviating the special bias (noise) from different users. This approach can also significantly accelerate the process of model training compared with classical GAN method for recommendation. Experimental results on three real data sets show that our approach outperforms other state-of-the-art approaches in terms of recommendation accuracy.

Leveraging Multiactions to Improve Medical Personalized Ranking for Collaborative Filtering.

Nowadays, providing high-quality recommendation services to users is an essential component in web applications, including shopping, making friends, and healthcare. This can be regarded either as a problem of estimating users' preference by exploiting explicit feedbacks (numerical ratings), or as a problem of collaborative ranking with implicit feedback (e.g., purchases, views, and clicks). Previous works for solving this issue include pointwise regression methods and pairwise ranking methods. The emerging healthcare websites and online medical databases impose a new challenge for medical service recommendation. In this paper, we develop a model, MBPR (Medical Bayesian Personalized Ranking over multiple users' actions), based on the simple observation that users tend to assign higher ranks to some kind of healthcare services that are meanwhile preferred in users' other actions. Experimental results on the real-world datasets demonstrate that MBPR achieves more accurate recommendations than several state-of-the-art methods and shows its generality and scalability via experiments on the datasets from one mobile shopping app.