HpGAN: Sequence Search with Generative Adversarial Networks

Sequences play an important role in many engineering applications and systems. Searching sequences with desired properties has long been an interesting but also challenging research topic. This article proposes a novel method, called HpGAN, to search desired sequences algorithmically using generative adversarial networks (GAN). HpGAN is based on the idea of zero-sum game to train a generative model, which can generate sequences with characteristics similar to the training sequences. In HpGAN, we design the Hopfield network as an encoder to avoid the limitations of GAN in generating discrete data. Compared with traditional sequence construction by algebraic tools, HpGAN is particularly suitable for intractable problems with complex objectives which prevent mathematical analysis. We demonstrate the search capabilities of HpGAN in two applications: 1) HpGAN successfully found many different mutually orthogonal complementary code sets (MOCCS) and optimal odd-length Z-complementary pairs (OB-ZCPs) which are not part of the training set. In the literature, both MOCSSs and OB-ZCPs have found wide applications in wireless communications. 2) HpGAN found new sequences which achieve four-times increase of signal-to-interference ratio--benchmarked against the well-known Legendre sequence--of a mismatched filter (MMF) estimator in pulse compression radar systems. These sequences outperform those found by AlphaSeq.Comment: 12 pages, 16 figure

Construction and confirmatory factor analysis of the core cognitive ability index system of ship C2 system operators.

BackgroundCognitive ability refers to the ability to receive, process, store, and extract information. It is the most important psychological condition for people to successfully complete activities. Previous studies have shown that the design of the human-computer interface of the command and control system cannot exceed the cognitive ability of the operator of the command and control system, and it must match the cognitive ability of the operator in order to reduce the mental load intensity, and improve the accuracy, timeliness and work efficiency. However, previous researchers in the field of cognitive science have not put forward a core index system that can represent the cognitive ability of ship command and control system operators and the importance of each index, and there are few achievements that can be used for reference.ObjectiveTo explore the core index system of cognitive ability that affecting the cognitive process of command and control system operators, and to verify the index system.MethodsBased on the classic O*NET questionnaire, two indexes of O*NET were revised, three indexes of response ability were added, and then a questionnaire on the importance evaluation of cognitive abilities index was formed. The questionnaire includes 24 indexes in six aspects: verbal abilities, idea generation and reasoning abilities, quantitative abilities, visual perception abilities, mnemonic and attentive abilities, and response abilities. The cognitive ability importance evaluation data of 202 people from different positions in the ship command and control system were collected. These data reflect the overall level of cognitive ability of operators in the whole ship command and control field.ResultsThe data analysis results show that: firstly, the most important cognitive abilities affecting command and control system operators were visual perception abilities, mnemonic and attentive abilities, and response abilities. Secondly, the results of confirmatory factor analysis show that CMIN/DF, GFI, CFI, TLI, RMSEA, RMR and other indicators used in the model test all meet the requirements. The model has a good fitting degree, and the overall index extraction method is feasible. Thirdly, the independence T test results show that for beginners and experienced experts, there is a significant difference in the important evaluation of mnemonic and attentive abilities, while there is no significant difference in the important evaluation of response and visual perception abilities. Fourthly, the results of Bi-group confirmatory factor analysis experiment show that the structural model has good stability and factor invariance.ConclusionsThrough the research of this paper, the index system which can express the core cognitive ability of the commander of command and control system is successfully constructed, and the index system has been fully verified by mathematics. The 3 abilities and 10 indexes in the index system are closely related to the work tasks of operators, which also reflects the correctness of our construction results to a certain extent. According to the results of data analysis, there are differences between assistant commanders and professional commanders in the evaluation of the importance of some indexes, which reflects the importance of working age and experience to the promotion of position skills. The results of this research are of great significance for the subsequent acquisition of cognitive ability data and assessment of post cognitive ability of command and control system operators

Drug Target Protein-Protein Interaction Networks: A Systematic Perspective

The identification and validation of drug targets are crucial in biomedical research and many studies have been conducted on analyzing drug target features for getting a better understanding on principles of their mechanisms. But most of them are based on either strong biological hypotheses or the chemical and physical properties of those targets separately. In this paper, we investigated three main ways to understand the functional biomolecules based on the topological features of drug targets. There are no significant differences between targets and common proteins in the protein-protein interactions network, indicating the drug targets are neither hub proteins which are dominant nor the bridge proteins. According to some special topological structures of the drug targets, there are significant differences between known targets and other proteins. Furthermore, the drug targets mainly belong to three typical communities based on their modularity. These topological features are helpful to understand how the drug targets work in the PPI network. Particularly, it is an alternative way to predict potential targets or extract nontargets to test a new drug target efficiently and economically. By this way, a drug target’s homologue set containing 102 potential target proteins is predicted in the paper

PAIRS: Prediction of Activation/Inhibition Regulation Signaling Pathway

Uncovering the signaling architecture in protein-protein interaction (PPI) can certainly benefit the understanding of disease mechanisms and promise to facilitate the therapeutic interventions. Therefore, it is important to reveal the signaling relationship from one protein to another in terms of activation and inhibition. In this study, we propose a new measurement to characterize the regulation relationship of a PPI pair. By utilizing both Gene Ontology (GO) functional annotation and protein domain information, we developed a tool called Prediction of Activation/Inhibition Regulation Signaling Pathway (PAIRS) that takes protein interaction pairs as input and gives both known and predicted result of the human protein regulation relationship in terms of activation and inhibition. It helps to give prognostic regulation information for further signaling pathway reconstruction

Efficient Data Mining Algorithms for Screening Potential Proteins of Drug Target

The past few decades have witnessed the boom in pharmacology as well as the dilemma of drug development. Playing a crucial role in drug design, the screening of potential human proteins of drug targets from open access database with well-measured physical and chemical properties is a task of challenge but significance. In this paper, the screening of potential drug target proteins (DTPs) from a fine collected dataset containing 5376 unlabeled proteins and 517 known DTPs was researched. Our objective is to screen potential DTPs from the 5376 proteins. Here we proposed two strategies assisting the construction of dataset of reliable nondrug target proteins (NDTPs) and then bagging of decision trees method was employed in the final prediction. Such two-stage algorithms have shown their effectiveness and superior performance on the testing set. Both of the algorithms maintained higher recall ratios of DTPs, respectively, 93.5% and 97.4%. In one turn of experiments, strategy1-based bagging of decision trees algorithm screened about 558 possible DTPs while 1782 potential DTPs were predicted in the second algorithm. Besides, two strategy-based algorithms showed the consensus of the predictions in the results, with approximately 442 potential DTPs in common. These selected DTPs provide reliable choices for further verification based on biomedical experiments

Doped N/Ag Carbon Dot Catalytic Amplification SERS Strategy for Acetamiprid Coupled Aptamer with 3,3′-Dimethylbiphenyl-4,4′-diamine Oxidizing Reaction

The as-prepared co-doped N/Ag carbon dot (CDNAg) has strong catalysis of H2O2 oxidation of 3,3′-dimethylbiphenyl-4,4′-diamine (DBD). It forms an oxidation product (DBDox) with surface-enhanced Raman scattering (SERS) activity at 1605 cm−1 in the silver nanosol substrate, and a CDNAg catalytic amplification with SERS analytical platform can be structured based on aptamer (Apt) with the DBD oxidizing reaction. For example, the aptamer (Apt) of acetamiprid (ACT) can be adsorbed on the surface of CDNAg, resulting in inhibited catalytic activity, the reduced generation of DBDox, and a weakened SERS intensity. When the target molecule ACT was added, it formed a stable Apt-ACT complex and free CDNAg that restored catalytic activity and linearly enhanced the SERS signal. Based on this, we proposed a new quantitative SERS analysis method for the determination of 0.01–1.5 μg ACT with a detection limit of 0.006 μg/L

HpGAN: Sequence Search With Generative Adversarial Networks.

Sequences play an important role in many engineering applications. Searching sequences with desired properties has long been an intriguing but also challenging research topic. This article proposes a novel method, called HpGAN, to search desired sequences algorithmically using generative adversarial networks (GANs). HpGAN is based on the idea of zero-sum game to train a generative model, which can generate sequences with characteristics similar to the training sequences. In HpGAN, we design the Hopfield network as an encoder to avoid the limitations of GAN in generating discrete data. Compared with traditional sequence construction by algebraic tools, HpGAN is particularly suitable for complex problems which are intractable by mathematical analysis. We demonstrate the search capabilities of HpGAN in two applications: 1) HpGAN successfully found many different mutually orthogonal complementary sequence sets (MOCSSs) and optimal odd-length binary Z-complementary pairs (OB-ZCPs) which are not part of the training set. In the literature, both MOCSSs and OB-ZCPs have found wide applications in wireless communications and 2) HpGAN found new sequences which achieve a four-times increase of signal-to-interference ratio--benchmarked against the well-known Legendre sequences--of a mismatched filter (MMF) estimator in pulse compression radar systems. These sequences outperform those found by AlphaSeq