Advances in next-generation sequencing and high-throughput techniques have enabled the generation of vast amounts of diverse omics data. These big data provide an unprecedented opportunity in biology, but impose great challenges in data integration, data mining, and knowledge discovery due to the complexity, heterogeneity, dynamics, uncertainty, and high-dimensionality inherited in the omics data. Network has been widely used to represent relations between entities in biological system, such as protein-protein interaction, gene regulation, and brain connectivity (i.e. network construction) as well as to infer novel relations given a reconstructed network (aka link prediction). Particularly, heterogeneous multi-layered network (HMLN) has proven successful in integrating diverse biological data for the representation of the hierarchy of biological system. The HMLN provides unparalleled opportunities but imposes new computational challenges on establishing causal genotype-phenotype associations and understanding environmental impact on organisms. In this review, we focus on the recent advances in developing novel computational methods for the inference of novel biological relations from the HMLN. We first discuss the properties of biological HMLN. Then we survey four categories of state-of-the-art methods (matrix factorization, random walk, knowledge graph, and deep learning). Thirdly, we demonstrate their applications to omics data integration and analysis. Finally, we outline strategies for future directions in the development of new HMLN models.

Graph Neural Networks (GNNs) are powerful for the representation learning of graph-structured data. Most of the GNNs use a message-passing scheme, where the embedding of a node is iteratively updated by aggregating the information from its neighbors. To achieve a better expressive capability of node influences, attention mechanism has grown to be popular to assign trainable weights to the nodes in aggregation. Though the attention-based GNNs have achieved remarkable results in various tasks, a clear understanding of their discriminative capacities is missing. In this work, we present a theoretical analysis of the representational properties of the GNN that adopts the attention mechanism as an aggregator. Our analysis determines all cases when those attention-based GNNs can always fail to distinguish certain distinct structures. Those cases appear due to the ignorance of cardinality information in attention-based aggregation. To improve the performance of attention-based GNNs, we propose cardinality preserved attention (CPA) models that can be applied to any kind of attention mechanisms. Our experiments on node and graph classification confirm our theoretical analysis and show the competitive performance of our CPA models. The code is available online: https://github.com/zetayue/CPA.

Bacterial cellulose (BC) is a polymeric nanostructured fibrillar network produced by certain microorganisms, principally Gluconacetobacter xylinus. BC has a great potential of application in many fields. Lignocellulosic biomass has been investigated as a cost-effective feedstock for BC production through pretreatment and hydrolysis. It is well known that detoxification of lignocellulosic hydrolysates may be required to achieve efficient production of BC. Recent results suggest that phenolic compounds contribute to the inhibition of G. xylinus. However, very little is known about the effect on G. xylinus of specific lignocellulose-derived inhibitors. In this study, the inhibitory effects of four phenolic model compounds (coniferyl aldehyde, ferulic acid, vanillin and 4-hydroxybenzoic acid) on the growth of G. xylinus, the pH of the culture medium, and the production of BC were investigated in detail. The stability of the phenolics in the bacterial cultures was investigated and the main bioconversion products were identified and quantified.

Processing the traditional Chinese medicine by fermentation has drawn widely concern. In this study, Kombucha SCOBY was utilized to ferment rose and jujube kernel to enhancing its sedative and hypnotic effect. Results showed that the content of 6‐feruloylspinosin and spinosin, as typical functional substances that exert sedative and hypnotic effects, increased by 7% in the fermentation broth compared with the infusion broth. The content of gallic acid, one of functional antioxidant phenolic substances in rose, increased 15.9% higher compared with infusion broth. In addition, jujuboside B, which has profound sedative effects but was absent in the initial medium, was detected in the final mixture fermentation broth with a concentration of 0.011 g/L. The analysis of flavors showed that the fermentation medium with jujube kernel and rose mixture had appropriate acidity and bitterness. This study provides a new approach to process Chinese medicine with better functional effects and acceptable flavor. Practical applications This study provides a theoretical basis for future investigations into the potential utilization of kombucha SCOBY to process other functional plants as substrates to enhance their medical effects.

Systematically discovering protein-ligand interactions across the entire human and pathogen genomes is critical in chemical genomics, protein function prediction, drug discovery, and many other areas. However, more than 90% of gene families remain “dark”—i.e., their small-molecule ligands are undiscovered due to experimental limitations or human/historical biases. Existing computational approaches typically fail when the dark protein differs from those with known ligands. To address this challenge, we have developed a deep learning framework, called PortalCG, which consists of four novel components: (i) a 3-dimensional ligand binding site enhanced sequence pre-training strategy to encode the evolutionary links between ligand-binding sites across gene families; (ii) an end-to-end pretraining-fine-tuning strategy to reduce the impact of inaccuracy of predicted structures on function predictions by recognizing the sequence-structure-function paradigm; (iii) a new out-of-cluster meta-learning algorithm that extracts and accumulates information learned from predicting ligands of distinct gene families (meta-data) and applies the meta-data to a dark gene family; and (iv) a stress model selection step, using different gene families in the test data from those in the training and development data sets to facilitate model deployment in a real-world scenario. In extensive and rigorous benchmark experiments, PortalCG considerably outperformed state-of-the-art techniques of machine learning and protein-ligand docking when applied to dark gene families, and demonstrated its generalization power for target identifications and compound screenings under out-of-distribution (OOD) scenarios. Furthermore, in an external validation for the multi-target compound screening, the performance of PortalCG surpassed the rational design from medicinal chemists. Our results also suggest that a differentiable sequence-structure-function deep learning framework, where protein structural information serves as an intermediate layer, could be superior to conventional methodology where predicted protein structures were used for the compound screening. We applied PortalCG to two case studies to exemplify its potential in drug discovery: designing selective dual-antagonists of dopamine receptors for the treatment of opioid use disorder (OUD), and illuminating the understudied human genome for target diseases that do not yet have effective and safe therapeutics. Our results suggested that PortalCG is a viable solution to the OOD problem in exploring understudied regions of protein functional space.

The demand for natural lactone gamma-decalactone (GDL) has increased in the fields of food and cosmetic products. However, low productivity during bioprocessing limits its industrial production. In this study, a novel composite porous cell carrier, bacterial cellulose-alginate (BC-ALG), was used for long-term biotransformation and production of GDL. The effects of this carrier on biotransformation and related mechanisms were investigated. BC-ALG carriers showed improved mechanical strength over ALG carriers, with their internal embedded cell pattern changed to an interconnected porous structure. In five repeated-batch biotransformation experiments, the maximum concentration of GDL obtained in culture with BC-ALG carriers was 8.37 g/L, approximately 3.7 times higher than that from the medium with an ALG carrier alone. The result indicated that multiple hydrogen bonding interactions at the interface between BC and ALG contributed to the compatibility and stability of BC-ALG carriers. On the basis of the above results, the BC-ALG composite carrier can be considered ideal for immobilisation of cells for the production of GDL on an industrial scale, and has the potential to be utilised in other biological processes.