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At present, huge amounts of data are being produced every second, a situation that will gradually overwhelm current storage technology. DNA is a storage medium that features high storage density and long-term stability and is now considered to be a feasible storage solution. Errors are easily made during the sequencing and synthesis of DNA, however. In order to reduce the error rate, novel uncorrelated address constrain are reported, and a Damping Multi-Verse Optimizer (DMVO)algorithm is proposed to construct a set of DNA coding, which is used as the non-payload. The DMVO algorithm exchanges objects through black/white holes in order to achieve a stable state and adds damping factors as disturbances. Compared with previous work, the coding set obtained by the DMVO algorithm is larger in size and of higher quality. The results of this study reveal that the size of the DNA storage coding set obtained by the DMVO algorithm increased by 4-16 percent, and the variance of the melting temperature decreased by 3-18 percent.

With the development of information technology, huge amounts of data are produced at the same time. How to store data efficiently and at low cost has become an urgent problem. DNA is a high-density and persistent medium, making DNA storage a viable solution. In a DNA data storage system, the first consideration is how to encode the data effectively into code words. However, DNA strands are prone to non-specific hybridization during the hybridization reaction process and are prone to errors during synthesis and sequencing. In order to reduce the error rate, a thermodynamic minimum free energy (MFE) constraint is proposed and applied to the construction of coding sets for DNA storage. The Brownian multi-verse optimizer (BMVO) algorithm, based on the Multi-verse optimizer (MVO) algorithm, incorporates the idea of Brownian motion and Nelder-Mead method, and it is used to design a better DNA storage coding set. In addition, compared with previous works, the coding set has been increasing by 4%-50% in size and has better thermodynamic properties. With the improvement of the quality of the DNA coding set, the accuracy of reading and writing and the robustness of the DNA storage system are also enhanced.

DNA strand displacement is introduced in this study and used to construct an analog DNA strand displacement chaotic system based on six reaction modules in nanoscale size. The DNA strand displacement circuit is employed in encryption as a chaotic generator to produce chaotic sequences. In the encryption algorithm, we convert chaotic sequences to binary ones by comparing the concentration of signal DNA strand. Simulation results show that the encryption scheme is sensitive to the keys, and key space is large enough to resist the brute-force attacks, furthermore algorithm has a high capacity to resist statistic attack. Based on robustness analysis, our proposed encryption scheme is robust to the DNA strand displacement reaction rate control, noise and concentration detection to a certain extent.

DNA computing, the combination of computer science and molecular biology, is a burgeoning research field that holds promise for many applications. The accuracy of DNA computing is determined by reliable DNA sequences, the quality of which affects the accuracy of hybridization reactions. Evaluating the sequences obtained from the previous combination constraints in NUPACK for simulation experiments, we find that the concentration of the sequences after entering solution was significantly lower than that before entering solution, which should affect the accuracy of DNA hybridization reactions. Therefore, in this study, we propose a new constraint, a triplet-bases unpaired constraint, which can be combined with other constraints to form a new combination constraint. In addition, we combine the Harmony Search algorithm with the Whale Optimization Algorithm (WOA) to present a new algorithm, termed HSWOA, which we used to design DNA sequences that meet the new combination constraint. Finally, compared with previous findings, our result shows that our algorithm not only improves the efficiency of hybridization reactions but also yields a better fitness value.

Massively parallel sequencing, a popular and efficient sequencing method, produces a tremendous number of sequences from multiple individual samples. Labeling sequences with barcodes (tags) prevents them from being unrecoverable or confused during sequencing, replication, and oligonucleotide synthesis. In view of DNA barcode set design, we propose a tabu variable neighborhood search (TVNS) to design DNA barcode sets. We propose using sequences with the maximum sum of edit distances as the basis for building the neighborhood structures of TVNS. We adopt an exhaustive search to complete local searches built from these structures. The algorithm switches between neighborhoods built from different seed data. Compared with previous designs, our proposed algorithm is effective for designing DNA barcode sets and improving the lower bound of DNA barcode sets.

Deoxyribonucleic acid (DNA) strand displacement can be used to build complex functional circuits due to its highly modular and programmable properties. While DNA strand displacement is most often used to solve logic problems, it can also be used to compute the roots of equations. In this paper, we present the design of novel architectures for catalysis, degradation, and annihilation in ideal formal reaction modules, and we translate these reaction modules to DNA networks. These ideal formal or DNA reaction modules are suitable for building analog circuits for solving tasks. The computing analog DNA circuits are assessed by solving a linear equation, a one-variable quadratic equation, and a set of two simultaneous linear equations. The results were evaluated by simulation.

Following the completion of the human genome project, a large amount of high-throughput bio-data was generated. To analyze these data, massively parallel sequencing, namely next-generation sequencing, was rapidly developed. DNA barcodes are used to identify the ownership between sequences and samples when they are attached at the beginning or end of sequencing reads. Constructing DNA barcode sets provides the candidate DNA barcodes for this application. To increase the accuracy of DNA barcode sets, a particle swarm optimization (PSO) algorithm has been modified and used to construct the DNA barcode sets in this paper. Compared with the extant results, some lower bounds of DNA barcode sets are improved. The results show that the proposed algorithm is effective in constructing DNA barcode sets.

The redundant residue number system is introduced into DNA computing in order to overcome the negative effect caused by the instability of the biochemical reactions and the error hybridizations. Based on the Adleman-Lipton model and a special 3-moduli set, the DNA encoding scheme for redundant residue numbers is presented and the DNA algorithm of one-digit error detection is proposed. The parallel DNA arithmetic operation can be realized in redundant residue number system with error detection, and which can improve the reliability of DNA computing and simplify the DNA encoding scheme.

The well-known massively parallel sequencing method is efficient and it can obtain sequence data from multiple individual samples. In order to ensure that sequencing, replication, and oligonucleotide synthesis errors do not result in tags (or barcodes) that are unrecoverable or confused, the tag sequences should be abundant and sufficiently different. Recently, many design methods have been proposed for correcting errors in data using error-correcting codes. The existing tag sets contain small tag sequences, so we used a modified genetic algorithm to improve the lower bound of the tag sets in this study. Compared with previous research, our algorithm is effective for designing sets of DNA tags. Moreover, the GC content determined by existing methods includes an imprecise range. Thus, we improved the GC content determination method to obtain tag sets that control the GC content in a more precise range. Finally, previous studies have only considered perfect self-complementarity. Thus, we considered the crossover between different tags and introduced an improved constraint into the design of tag sets.

A new improved hybrid optimization algorithm - PGATS algorithm, which is based on toy off-lattice model, is presented for dealing with three-dimensional protein structure prediction problems. The algorithm combines the particle swarm optimization (PSO), genetic algorithm (GA), and tabu search (TS) algorithms. Otherwise, we also take some different improved strategies. The factor of stochastic disturbance is joined in the particle swarm optimization to improve the search ability; the operations of crossover and mutation that are in the genetic algorithm are changed to a kind of random liner method; at last tabu search algorithm is improved by appending a mutation operator. Through the combination of a variety of strategies and algorithms, the protein structure prediction (PSP) in a 3D off-lattice model is achieved. The PSP problem is an NP-hard problem, but the problem can be attributed to a global optimization problem of multi-extremum and multi-parameters. This is the theoretical principle of the hybrid optimization algorithm that is proposed in this paper. The algorithm combines local search and global search, which overcomes the shortcoming of a single algorithm, giving full play to the advantage of each algorithm. In the current universal standard sequences, Fibonacci sequences and real protein sequences are certified. Experiments show that the proposed new method outperforms single algorithms on the accuracy of calculating the protein sequence energy value, which is proved to be an effective way to predict the structure of proteins.

The problem of protein structure prediction in the hydrophobic-polar (HP) lattice model is the prediction of protein tertiary structure. This problem is usually referred to as the protein folding problem. This paper presents a method for the application of an enhanced hybrid search algorithm to the problem of protein folding prediction, using the three dimensional (3D) HP lattice model. The enhanced hybrid search algorithm is a combination of the particle swarm optimizer (PSO) and tabu search (TS) algorithms. Since the PSO algorithm entraps local minimum in later evolution extremely easily, we combined PSO with the TS algorithm, which has properties of global optimization. Since the technologies of crossover and mutation are applied many times to PSO and TS algorithms, so enhanced hybrid search algorithm is called the MCMPSO-TS (multiple crossover and mutation PSO-TS) algorithm. Experimental results show that the MCMPSO-TS algorithm can find the best solutions so far for the listed benchmarks, which will help comparison with any future paper approach. Moreover, real protein sequences and Fibonacci sequences are verified in the 3D HP lattice model for the first time. Compared with the previous evolutionary algorithms, the new hybrid search algorithm is novel, and can be used effectively to predict 3D protein folding structure. With continuous development and changes in amino acids sequences, the new algorithm will also make a contribution to the study of new protein sequences.

We present a novel image encryption algorithm based on DNA subsequence operation. Different from the traditional DNA encryption methods, our algorithm does not use complex biological operation but just uses the idea of DNA subsequence operations (such as elongation operation, truncation operation, deletion operation, etc.) combining with the logistic chaotic map to scramble the location and the value of pixel points from the image. The experimental results and security analysis show that the proposed algorithm is easy to be implemented, can get good encryption effect, has a wide secret key's space, strong sensitivity to secret key, and has the abilities of resisting exhaustive attack and statistic attack.

The problem of data and information encoding on DNA bears an increasing interest for both biological and nonbiological applications of biomolecular computing. Recent experimental and theoretical advances have produced and tested to obtain large code sets of oligonucleotides to support virtually any kind of application. In this paper, we have developed an algorithm to design DNA short-word sets based on minimum free energy (MFE) criteria. The MFE constraint is the minimum value among free energies of all the possible structures and the effective approach to control the generation of unexpected secondary structure of DNA sequences may cause error. The algorithm is constructive and directly produces the actual DNA words of the sets, unlike other academic and statistical numbers. According to the previous values, our experimental results can succeed in generating better DNA word sets based on MFE constraint. More importantly, using our results could decrease the emergence of false hybridization reaction, and improve the reliability and the scale of DNA computing.

Background: The historical data of rare disease is very scarce in reality, so how to perform drug repositioning for the rare disease is a great challenge. Most existing methods of drug repositioning for the rare disease usually neglect father-son information, so it is extremely difficult to predict drugs for the rare disease.Method: In this paper, we focus on father-son information mining for the rare disease. We propose GRU-Cooperation-Attention-Network (GCAN) to predict drugs for the rare disease. We construct two heterogeneous networks for information enhancement, one network contains the father-nodes of the rare disease and the other network contains the son-nodes information. To bridge two heterogeneous networks, we set a mapping to connect them. What's more, we use the biased random walk mechanism to collect the information smoothly from two heterogeneous networks, and employ a cooperation attention mechanism to enhance repositioning ability of the network.Result: Comparing with traditional methods, GCAN makes full use of father-son information. The experimental results on real drug data from hospitals show that GCAN outperforms state-of-the-art machine learning methods for drug repositioning.Conclusion: The performance of GCAN for drug repositioning is mainly limited by the insufficient scale and poor quality of the data. In future research work, we will focus on how to utilize more data such as drug molecule information and protein molecule information for the drug repositioning of the rare disease. [ABSTRACT FROM AUTHOR]

Abstract: A novel couple images encryption algorithm based on DNA subsequence operation and chaotic system is presented. Different from the traditional DNA encryption methods, our algorithm is not use complex biological operation, but just uses the idea of DNA subsequence operation (such as elongation operation, truncation operation, and deletion operation). And then, do the DNA addition operation under the Chen's Hyper-chaotic map in this image cipher. The simulation experimental results and security analysis show that our algorithm not only has good encryption effect, but also has the ability of resisting exhaustive attack and statistical attack. [Copyright &y& Elsevier]

Biotechnological and biomolecular advances have introduced novel uses for DNA such as DNA computing, storage, and encryption. For these applications, DNA sequence design requires maximal desired (and minimal undesired) hybridizations, which are the product of a single new DNA strand from 2 single DNA strands. Here, we propose a novel constraint to design DNA sequences based on thermodynamic properties. Existing constraints for DNA design are based on the Hamming distance, a constraint that does not address the thermodynamic properties of the DNA sequence. Using a unique, improved genetic algorithm, we designed DNA sequence sets which satisfy different distance constraints and employ a free energy gap based on a minimum free energy (MFE) to gauge DNA sequences based on set thermodynamic properties. When compared to the best constraints of the Hamming distance, our method yielded better thermodynamic qualities. We then used our improved genetic algorithm to obtain lower-bound DNA sequence sets. Here, we discuss the effects of novel constraint parameters on the free energy gap. [ABSTRACT FROM AUTHOR]

Abstract: Precise pupil center detection is an important factor for gaze tracking in video-oculography (VOG) systems. Existing methods perform well to extract the features when the area of pupil in eye image is clear, whereas, interferences, such as eyelashes, corneal reflection etc., will lead to a low success rate. One main reason is the closure of eyelids. In this paper, a systemic 3D transformation algorithm is proposed to accurately ascertain the pupil center, in spite of the complicating factors mentioned above. Experiments show the good performance of our method. And the pupil center could be extracted accurately, even though only 25% of the pupil is visible. [Copyright &y& Elsevier]

A deeper game-tree search can yield a higher decision quality in a heuristic minimax algorithm. However, exceptions can occur as a result of pathological nodes, which are considered to exist in all game trees and can cause a deeper game-tree search, resulting in worse play. To reduce the impact of pathological nodes on the search quality, we propose an iterative optimal minimax (IOM) algorithm by optimizing the backup rule of the classic minimax algorithm. The main idea is that calculating the state values of the intermediate nodes involves not only the static evaluation function involved but also a search into the future, where the latter is given a higher weight. We experimentally demonstrated that the proposed IOM algorithm improved game-playing performance compared to the existing algorithms. [ABSTRACT FROM AUTHOR]

Identifying the abnormal samples from a data set and determining their type are two key tasks of anomaly detection. However, the existing anomaly detection algorithms are generally faced with the defects of weak generalization ability and insufficient interpretation, the core reason of which is that they cannot mine specific features for different abnormal types. In this paper, a new anomaly detection algorithm aiming at feature selection for different abnormal types is developed. Inspired by self-supervised learning, we take the stationarity of variance changes of abnormal score similarity as a pretext task and combine it with wrapped search method. Then, the features and the corresponding parameters for different abnormal types can be screened to apply to the downstream task of anomaly integration detection. To verify the efficiency of the new algorithm, we conduct two sets of experiments to compare the new algorithm with 11 classical anomaly detection and 3 clustering anomaly detection algorithms on the data sets WDBC, WPBC and Wilt from DAMI database with the evaluation measures P@n , Adj- P@n , AP, Adj-AP and AUC. The experiment results show that, both in the identification and classification on abnormal samples, all performance measures of the new algorithm are explicitly better than that of the contrastive algorithms. Also, we apply the new algorithm to the Chinese auto insurance market, and find that the results can help managers to identify the main patterns of fraudulent claims and to summarize the feature combinations of fraud behaviors. In general, the new algorithm developed in this paper has the following advantages compared with traditional algorithms: 1) It can directly capture abnormal features and realize effective recognition of abnormal types, which effectively bridge the gap between abnormal judgement and feature screening. 2) Automatic screening of abnormal features can be completed under the condition of self-updating learning optimal strategy. 3) Only a few features are extracted from all features to reveal the abnormal characteristics, which significantly improves the interpretability and generalization ability of the algorithm and its results. In a word, through the novel self-supervised design method, feature screening is skillfully integrated into the anomaly detection task, which may provide a new way for anomaly detection research. [ABSTRACT FROM AUTHOR]

Abstract: New image encryption based on DNA encoding combined with chaotic system is proposed. The algorithm uses chaotic system to disturb the pixel locations and pixel values and then DNA encodings according to quaternary code rules are carried out. The pseudo DNA operations are controlled by the quaternary chaotic sequences. At last the image encryption through DNA decoding is achieved. The theoretical analysis and experimental results show that the algorithm improves the encoding efficiency, enhances the security of the ciphertext, has a large key space and a high key sensitivity, it is able to resist the statistical and exhaustive attacks. [Copyright &y& Elsevier]

Abstract: A new image fusion encryption algorithm based on image fusion and DNA sequence operation and hyper-chaotic system is presented. Firstly, two DNA sequences matrices are obtained by encoding the original image and the key image. Secondly, using the chaotic sequences generated by Chen's hyper-chaotic maps to scramble the locations of elements from the DNA sequence matrix which generated form original image. Thirdly, XOR the scrambled DNA matrix and the random DNA matrix by using DNA sequence addition operation. At last, decoding the DNA sequence matrix, we will get the encrypted image. The simulation experimental results and security analysis show that our algorithm not only has good encryption effect, but also has the ability of resisting exhaustive attack and statistical attack. [Copyright &y& Elsevier]