Balance guided incomplete multi-view spectral clustering.

There is a large volume of incomplete multi-view data in the real-world. How to partition these incomplete multi-view data is an urgent realistic problem since almost all of the conventional multi-view clustering methods are inapplicable to cases with missing views. In this paper, a novel graph learning-based incomplete multi-view clustering (IMVC) method is proposed to address this issue. Different from existing works, our method aims at learning a common consensus graph from all incomplete views and obtaining a clustering indicator matrix in a unified framework. To achieve a stable clustering result, a relaxed spectral clustering model is introduced to obtain a probability consensus representation with all positive elements that reflect the data clustering result. Considering the different contributions of views to the clustering task, a weighted multi-view learning mechanism is introduced to automatically balance the effects of different views in model optimization. In this way, the intrinsic information of the incomplete multi-view data can be fully exploited. The experiments on several incomplete multi-view datasets show that our method outperforms the compared state-of-the-art clustering methods, which demonstrates the effectiveness of our method for IMVC.

Identification of Potential Inhibitors of PDE5 based on Structure-based Virtual Screening Approaches.

BACKGROUND: Phosphodiesterase type 5 (PDE5), exclusively specific for cyclic guanidine monophosphate (cGMP), a potential target for the therapy of various diseases, and PDE5 inhibitors could be used as a treatment for erectile dysfunction (ED) or chronic pulmonary hypertension. OBJECTIVE: In the present study, we carried out an integrated computer-aided virtual screening technique against the natural products in the ZINC database to discover potential inhibitors of PDE5. METHODS: Pharmacophore, molecular docking and ADMET (Absorption, distribution, metabolism, excretion and toxicity) properties filtration were used to select the PDE5 inhibitors with the best binding affinities and drug-like properties. The binding modes of PDE5 inhibitors were investigated, and these complexes' stabilities were explored by molecular dynamic simulations and MM/GBSA free energy calculations. RESULTS: Two natural compounds (Z171 and Z283) were identified and may be used as a critical starting point for the development of novel PDE5 inhibitors. The MM/GBSA free energy decomposition analysis quantitatively analyzed the importance of hydrophobic interaction in PDE5- ligands binding. CONCLUSION: In this study, we identified two novel natural compounds from the ZINC database to effectively inhibit PDE5 through virtual screening. The novel scaffolds of these compounds can be used as the starting templates in the drug design of PDE5 inhibitors with good pharmacokinetic profiles. These results may promote the de novo design of new compounds against PDE5.

Breast Mass Detection in Mammography Based on Image Template Matching and CNN.

In recent years, computer vision technology has been widely used in the field of medical image processing. However, there is still a big gap between the existing breast mass detection methods and the real-world application due to the limited detection accuracy. It is known that humans locate the regions of interest quickly and further identify whether these regions are the targets we found. In breast cancer diagnosis, we locate all the potential regions of breast mass by glancing at the mammographic image from top to bottom and from left to right, then further identify whether these regions are a breast mass. Inspired by the process of human detection of breast mass, we proposed a novel breast mass detection method to detect breast mass on a mammographic image by stimulating the process of human detection. The proposed method preprocesses the mammographic image via the mathematical morphology method and locates the suspected regions of breast mass by the image template matching method. Then, it obtains the regions of breast mass by classifying these suspected regions into breast mass and background categories using a convolutional neural network (CNN). The bounding box of breast mass obtained by the mathematical morphology method and image template matching method are roughly due to the mathematical morphology method, which transforms all of the brighter regions into approximate circular areas. For regression of a breast mass bounding box, the optimal solution should be searched in the feasible region and the Particle Swarm Optimization (PSO) is suitable for solving the problem of searching the optimal solution within a certain range. Therefore, we refine the bounding box of breast mass by the PSO algorithm. The proposed breast mass detection method and the compared detection methods were evaluated on the open database Digital Database for Screening Mammography (DDSM). The experimental results demonstrate that the proposed method is superior to all of the compared detection methods in detection performance.

Advances in L-Type Calcium Channel Structures, Functions and Molecular Modeling.

L-type Calcium Channels (LTCCs), also termed as Cav1, belong to voltage-gated calcium channels (VGCCs/Cavs), which play a critical role in a wide spectrum of physiological processes, including neurotransmission, cell cycle, muscular contraction, cardiac action potential and gene expression. Aberrant regulation of calcium channels is involved in neurological, cardiovascular, muscular and psychiatric disorders. Accordingly, LTCCs have been regarded as important drug targets, and a number of LTCC drugs are in clinical use. In this review, the recent development of structures and biological functions of LTCCs are introduced. Moreover, the representative modulators and ligand binding sites of LTCCs are discussed. Finally, molecular modeling and Computer-aided Drug Design (CADD) methods for understanding structure-function relations of LTCCs are summarized.