Comprehensive evaluation of deep and graph learning on drug-drug interactions prediction

Recent advances and achievements of artificial intelligence (AI) as well as deep and graph learning models have established their usefulness in biomedical applications, especially in drug-drug interactions (DDIs). DDIs refer to a change in the effect of one drug to the presence of another drug in the human body, which plays an essential role in drug discovery and clinical research. DDIs prediction through traditional clinical trials and experiments is an expensive and time-consuming process. To correctly apply the advanced AI and deep learning, the developer and user meet various challenges such as the availability and encoding of data resources, and the design of computational methods. This review summarizes chemical structure based, network based, NLP based and hybrid methods, providing an updated and accessible guide to the broad researchers and development community with different domain knowledge. We introduce widely-used molecular representation and describe the theoretical frameworks of graph neural network models for representing molecular structures. We present the advantages and disadvantages of deep and graph learning methods by performing comparative experiments. We discuss the potential technical challenges and highlight future directions of deep and graph learning models for accelerating DDIs prediction.Comment: Accepted by Briefings in Bioinformatic

The Regulation of Nitrate Reductases in Response to Abiotic Stress in Arabidopsis

The two homologous genes, NIA1 and NIA2, encode nitrate reductases in Arabidopsis, which govern the reduction of nitrate to nitrite. This step is the rate-limiting step of the nitrate assimilation and utilization. Therefore, the regulation of NIA1 and NIA2 is important for plant development and growth. Although they are similar in sequence and structure, their regulations are different. Genetic analysis uncovers that NIA1, rather than NIA2, plays a predominant role in adopting to ABA stress. Although both long-term stress conditions can cause an improvement in NIA1 levels, a decrease in NIA1 levels under short-term treatments seems to be necessary for plants to switch from the growth status into the adopting status. Interestingly, the downregulation of the NR is distinct under different stress conditions. Under ABA treatment, the NR proteins are degraded via a 26S-proteasome dependent manner, while the transcriptional regulation is the main manner to rapidly reduce the NIA1 levels under nitrogen deficiency and NaCl stress conditions. These results indicate that under stress conditions, the regulation of NIA1 is complex, and it plays a key role in regulating the balance between growth and adaptation