Deep Learning Methods for Predicting Disease Status Using Genomic Data.

Predicting disease status for a complex human disease using genomic data is an important, yet challenging, step in personalized medicine. Among many challenges, the so-called curse of dimensionality problem results in unsatisfied performances of many state-of-art machine learning algorithms. A major recent advance in machine learning is the rapid development of deep learning algorithms that can efficiently extract meaningful features from high-dimensional and complex datasets through a stacked and hierarchical learning process. Deep learning has shown breakthrough performance in several areas including image recognition, natural language processing, and speech recognition. However, the performance of deep learning in predicting disease status using genomic datasets is still not well studied. In this article, we performed a review on the four relevant articles that we found through our thorough literature search. All four articles first used auto-encoders to project high-dimensional genomic data to a low dimensional space and then applied the state-of-the-art machine learning algorithms to predict disease status based on the low-dimensional representations. These deep learning approaches outperformed existing prediction methods, such as prediction based on transcript-wise screening and prediction based on principal component analysis. The limitations of the current deep learning approach and possible improvements were also discussed.

Research on a novel chitosan microsphere/scaffold system by double crosslinkers.

RhBMP-2 has shown great promise for the reconstruction of teeth segmental bone defects due to its osteoinductive properties. But the application of rhBMP-2 is limited by its weak drug controled release. It is usually loaded in a Chitosan Microspheres (CMs) delivery system with excess single cross-linker and then removed before practice. In this study, cross-linkers were replaced with RhBMP-2 which contains vanillin and vitriolic acid, and thus CMs were developed. The materials were studied by SEM, FTIR and drug release experiments. It showed an ideal releasing profile and excellent osteoconductive and osteoinductive performance in the delivery system. Therefore, designing biomaterials with a controllable delivery system composite and releasing profile of rhBMP-2 are critical for applications of bone regeneration and tissue engineering.