ABSTRACT
Introduction: Drug-target interaction prediction is one important step in drug research and development. Experimental methods are time consuming and laborious. Methods: In this study, we developed a novel DTI prediction method called EnGDD by combining initial feature acquisition, dimensional reduction, and DTI classification based on Gradient boosting neural network, Deep neural network, and Deep Forest. Results: EnGDD was compared with seven stat-of-the-art DTI prediction methods (BLM-NII, NRLMF, WNNGIP, NEDTP, DTi2Vec, RoFDT, and MolTrans) on the nuclear receptor, GPCR, ion channel, and enzyme datasets under cross validations on drugs, targets, and drug-target pairs, respectively. EnGDD computed the best recall, accuracy, F1-score, AUC, and AUPR under the majority of conditions, demonstrating its powerful DTI identification performance. EnGDD predicted that D00182 and hsa2099, D07871 and hsa1813, DB00599 and hsa2562, D00002 and hsa10935 have a higher interaction probabilities among unknown drug-target pairs and may be potential DTIs on the four datasets, respectively. In particular, D00002 (Nadide) was identified to interact with hsa10935 (Mitochondrial peroxiredoxin3) whose up-regulation might be used to treat neurodegenerative diseases. Finally, EnGDD was used to find possible drug targets for Parkinson's disease and Alzheimer's disease after confirming its DTI identification performance. The results show that D01277, D04641, and D08969 may be applied to the treatment of Parkinson's disease through targeting hsa1813 (dopamine receptor D2) and D02173, D02558, and D03822 may be the clues of treatment for patients with Alzheimer's disease through targeting hsa5743 (prostaglandinendoperoxide synthase 2). The above prediction results need further biomedical validation. Discussion: We anticipate that our proposed EnGDD model can help discover potential therapeutic clues for various diseases including neurodegenerative diseases.
ABSTRACT
Recent developments in three-dimensional (3D) printing technology offer immense potential in fabricating scaffolds and implants for various biomedical applications, especially for bone repair and regeneration. As the availability of autologous bone sources and commercial products is limited and surgical methods do not help in complete regeneration, it is necessary to develop alternative approaches for repairing large segmental bone defects. The 3D printing technology can effectively integrate different types of living cells within a 3D construct made up of conventional micro- or nanoscale biomaterials to create an artificial bone graft capable of regenerating the damaged tissues. This article reviews the developments and applications of 3D printing in bone tissue engineering and highlights the numerous conventional biomaterials and nanomaterials that have been used in the production of 3D-printed scaffolds. A comprehensive overview of the 3D printing methods such as stereolithography (SLA), selective laser sintering (SLS), fused deposition modeling (FDM), and ink-jet 3D printing, and their technical and clinical applications in bone repair and regeneration has been provided. The review is expected to be useful for readers to gain an insight into the state-of-the-art of 3D printing of bone substitutes and their translational perspectives.
