With the development of next generation sequencing techniques, it is fast and cheap to determine protein sequences but relatively slow and expensive to extract useful information from protein sequences because of limitations of traditional biological experimental techniques. Protein function prediction has been a long standing challenge to fill the gap between the huge amount of protein sequences and the known function. In this paper, we propose a novel method to convert the protein function problem into a language translation problem by the new proposed protein sequence language “ProLan” to the protein function language “GOLan”, and build a neural machine translation model based on recurrent neural networks to translate “ProLan” language to “GOLan” language. We blindly tested our method by attending the latest third Critical Assessment of Function Annotation (CAFA 3) in 2016, and also evaluate the performance of our methods on selected proteins whose function was released after CAFA competition. The good performance on the training and testing datasets demonstrates that our new proposed method is a promising direction for protein function prediction. In summary, we first time propose a method which converts the protein function prediction problem to a language translation problem and applies a neural machine translation model for protein function prediction.

Drug discovery, which is the process of discovering new candidate medications, is very important for pharmaceutical industries. At its current stage, discovering new drugs is still an expensive and time-consuming process, requiring Phases I, II and III for clinical trials. Recently, machine learning techniques in Artificial Intelligence (AI), especially the deep learning techniques which allow a computational model to generate multiple layers, have been widely applied and achieved state-of-the-art performance in various fields, such as speech recognition, image classification, bioinformatics, etc. One potential application of these AI techniques is in the field of drug discovery. The main focus of this survey is to understand the current status of machine learning techniques in the drug discovery field within both academic and industrial settings, and discuss its potential future applications. Several interesting patterns for machine learning techniques in drug discovery fields are discussed in this survey.

The rapidly increasing complexities of hardware designs are forcing design methodologies and tools to move to the Electronic System Level (ESL), a higher abstraction level with better productivity than the state-of-the-art Register Transfer Level (RTL). Behavioral synthesis, which automatically synthesizes ESL behavioral specifications to RTL implementations, plays a central role in this transition.However, since behavioral synthesis is a complex and error-prone translation process, the lack of designers' confidence in its correctness becomes a major barrier to its wide adoption. Therefore, techniques for establishing equivalence between an ESL specification and its synthesized RTL implementation are critical to bring behavioral synthesis into practice.The major research challenge to equivalence checking for behavioral synthesis is the significant semantic gap between ESL and RTL. The semantics of ESL involve untimed, sequential execution; however, the semantics of RTL involve timed, concurrent execution. We propose a sequential equivalence checking (SEC) framework for certifying a behavioral synthesis flow, which exploits information on successive

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