Obtaining a protein's 3D structure is crucial to the understanding of its functions and interactions with other proteins. It is critical to accelerate the protein crystallization process with improved accuracy for understanding cancer and designing drugs. Systematic high-throughput approaches in protein crystallization have been widely applied, generating a large number of protein crystallization-trial images. Therefore, an efficient and effective automatic analysis for these images is a top priority. In this paper, we present a novel system, CrystalNet, for automatically labeling outcomes of protein crystallization-trial images. CrystalNet is a deep convolutional neural network that automatically extracts features from X-ray protein crystallization images for classification. We show that (1) CrystalNet can provide real-time labels for crystallization images effectively, requiring approximately 2 seconds to provide labels for all 1536 images of crystallization microassay on each plate; (2) compared with the state-of-the-art classification systems in crystallization image analysis, our technique demonstrates an improvement of 8% in accuracy, and achieve 90.8% accuracy in classification. As a part of the high-throughput pipeline which generates millions of images a year, CrystalNet can lead to a substantial reduction of labor-intensive screening.

Web Intelligence (WI) is a new and active research field in current AI and IT. Personalized recommendation in an intelligent B2C portal is an important research topic in WI. In this paper, we first investigate the architecture of a B2C portal from the viewpoint of conceptual levels of WI. Aiming at data mining of knowledge-level in a B2C portal, we present a new improved learning algorithm of Bayesian Networks, which consists of two major contributions, namely, making the best of lower order Conditional Independence (CI) tests and accelerating search process by means of sort order for parent nodes. By a number of experiments on ALARM datasets, we find that the proposed algorithm is both more efficient and effective than others. We have applied this algorithm to a commodity recommendation system in a B2C portal. Our experimental results demonstrate that the recommendation method based on a Customer Shopping Model (CSM) produced by the new algorithm outperforms some traditional ones in rates of coverage and precision.