This paper proposes a novel approach named AGM to efficiently mine the association rules among the frequently appearing substructures in a given graph data set. A graph transaction is represented by an adjacency matrix, and the frequent patterns appearing in the matrices are mined through the extended algorithm of the basket analysis. Its performance has been evaluated for the artificial simulation data and the carcinogenesis data of Oxford University and NTP. Its high efficiency has been confirmed for the size of a real-world problem.. . .

The need for mining structured data has increased in the past few years. One of the best studied data structures in computer science and discrete mathematics are graphs. It can therefore be no surprise that graph based data mining has become quite popular in the last few years.This article introduces the theoretical basis of graph based data mining and surveys the state of the art of graph-based data mining. Brief descriptions of some representative approaches are provided as well.

High-throughput, high-accuracy detection of emerging viruses allows for the control of disease outbreaks. Currently, reverse transcription-polymerase chain reaction (RT-PCR) is currently the most-widely used technology to diagnose the presence of SARS-CoV-2. However, RT-PCR requires the extraction of viral RNA from clinical specimens to obtain high sensitivity. Here, we report a method for detecting novel coronaviruses with high sensitivity by using nanopores together with artificial intelligence, a relatively simple procedure that does not require RNA extraction. Our final platform, which we call the artificially intelligent nanopore, consists of machine learning software on a server, a portable high-speed and high-precision current measuring instrument, and scalable, cost-effective semiconducting nanopore modules. We show that artificially intelligent nanopores are successful in accurately identifying four types of coronaviruses similar in size, HCoV-229E, SARS-CoV, MERS-CoV, and SARS-CoV-2. Detection of SARS-CoV-2 in saliva specimen is achieved with a sensitivity of 90% and specificity of 96% with a 5-minute measurement.

Immunosensing is a bioanalytical technique capable of selective detections of pathogens by utilizing highly specific and strong intermolecular interactions between recognition probes and antigens. Here, we exploited the molecular mechanism in artificial nanopores for selective single-virus identifications. We designed hemagglutinin antibody mimicking oligopeptides with a weak affinity to influenza A virus. By functionalizing the pore wall surface with the synthetic peptides, we rendered specificity to virion−nanopore interactions. The ligand binding thereof was found to perturb translocation dynamics of specific viruses in the nanochannel, which facilitated digital typing of influenza by the resistive pulse bluntness. As amino acid sequence degrees of freedom can potentially offer variety of recognition ability to the molecular probes, this peptide nanopore approach can be used as a versatile immunosensor with single-particle sensitivity that promises wide applications in bioanalysis including bacterial and viral screening to infectious disease diagnosis.

When conducting a single-molecule measurement, data are analyzed using the histogram of a measured physical quantity in which a single dataset contains information about a specific single molecule. Oftentimes, the histogram consisting of only specific single-molecule information excludes the input from other information sources. In other words, despite measuring the single molecule during analysis, we miss many of the properties of that single molecule. Herein, we have successfully identified a single molecule with a high degree of precision via a one-electric current pulse method using machine learning to read the single-molecule information. With the use of positive unlabeled classification, which is one of the techniques used in machine learning, we have developed a method for discerning a single molecule from a background of electric noises by analyzing the electric noises produced at the nanoscale level. In this method, we have demonstrated that the 2-, 3-, and 4-type nucleotides could be identified with a high degree of accuracy at a single-molecule resolution. This method can be widely applied for the accurate identification of a nucleotide using one measurement signal within a noisy matrix.

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We address the problem of inferring the causal direction between two variables by comparing the least-squares errors of the predictions in both possible directions. Under the assumption of an independence between the function relating cause and effect, the conditional noise distribution, and the distribution of the cause, we show that the errors are smaller in causal direction if both variables are equally scaled and the causal relation is close to deterministic. Based on this, we provide an easily applicable algorithm that only requires a regression in both possible causal directions and a comparison of the errors. The performance of the algorithm is compared with various related causal inference methods in different artificial and real-world data sets. Schölkopf B. 2009. Nonlinear causal discovery with additive noise models. In: Advances in neural information processing systems 21.Red Hook: Curran Associates, Inc., 689-696. Hyvärinen A, Smith S. 2013. Pairwise likelihood ratios for estimation of non-Gaussian structural equation models. Journal of Machine Learning Research 14(Jan):111-152. Janzing D, Mooij J, Zhang K, Lemeire J, Zscheischler J, Daniušis P, Steudel B, Schölkopf B. 2012. Information-geometric approach to inferring causal directions. . Janzing D, Sun X, Schölkopf B. 2009. Distinguishing cause and effect via second order exponential models. eprint http://arxiv.org/abs/0910.5561. Kano Y, Shimizu S. 2003. Causal inference using nonnormality. In: Proceedings of the international symposium on science of modeling, the 30th anniversary of the information criterion. Tokyo, 261-270. Lemeire J, Janzing D. 2012. Replacing causal faithfulness with algorithmic independence of conditionals. Minds and Machines 23(2):227-249 DOI 10.1007/s11023-012-9283-1. Ma S, Statnikov A. 2017. Methods for computational causal discovery in biomedicine. Behaviormetrika 44(1):165-191 DOI 10.1007/s41237-016-0013-5. Marx A, Vreeken J. 2017. Telling cause from effect using MDL-based local and global regression. In: 2017 IEEE international conference on data mining (ICDM). Piscataway: IEEE, 307-316 DOI 10.1109/ICDM.2017.40. Mooij J, Peters J, Janzing D, Zscheischler J, Schölkopf B. 2016. Distinguishing cause from effect using observational data: methods and benchmarks. Journal of Machine Learning Research 17(32):1-102. Pearl J. 2009. Causality: models, reasoning and inference. 2nd edition. New York: Cambridge University Press. Peters J, Janzing D, Schölkopf B. 2011. Causal inference on discrete data using additive noise models.

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