BACKGROUND: MicroRNAs (miRNAs) are a class of non-coding RNAs that play a pivotal role as gene expression regulators. These miRNAs are typically approximately 20 to 25 nucleotides long. The maturation of miRNAs requires Dicer cleavage at specific sites within the precursor miRNAs (pre-miRNAs). Recent advances in machine learning-based approaches for cleavage site prediction, such as PHDcleav and LBSizeCleav, have been reported. ReCGBM, a gradient boosting-based model, demonstrates superior performance compared with existing methods. Nonetheless, ReCGBM operates solely as a binary classifier despite the presence of two cleavage sites in a typical pre-miRNA. Previous approaches have focused on utilizing only a fraction of the structural information in pre-miRNAs, often overlooking comprehensive secondary structure information. There is a compelling need for the development of a novel model to address these limitations. RESULTS: In this study, we developed a deep learning model for predicting the presence of a Dicer cleavage site within a pre-miRNA segment. This model was enhanced by an autoencoder that learned the secondary structure embeddings of pre-miRNA. Benchmarking experiments demonstrated that the performance of our model was comparable to that of ReCGBM in the binary classification tasks. In addition, our model excelled in multi-class classification tasks, making it a more versatile and practical solution than ReCGBM. CONCLUSIONS: Our proposed model exhibited superior performance compared with the current state-of-the-art model, underscoring the effectiveness of a deep learning approach in predicting Dicer cleavage sites. Furthermore, our model could be trained using only sequence and secondary structure information. Its capacity to accommodate multi-class classification tasks has enhanced the practical utility of our model.

BACKGROUND: Bioinformatics capability to analyze spatio-temporal dynamics of gene expression is essential in understanding animal development. Animal cells are spatially organized as functional tissues where cellular gene expression data contain information that governs morphogenesis during the developmental process. Although several computational tissue reconstruction methods using transcriptomics data have been proposed, those methods have been ineffective in arranging cells in their correct positions in tissues or organs unless spatial information is explicitly provided. RESULTS: This study demonstrates stochastic self-organizing map clustering with Markov chain Monte Carlo calculations for optimizing informative genes effectively reconstruct any spatio-temporal topology of cells from their transcriptome profiles with only a coarse topological guideline. The method, eSPRESSO (enhanced SPatial REconstruction by Stochastic Self-Organizing Map), provides a powerful in silico spatio-temporal tissue reconstruction capability, as confirmed by using human embryonic heart and mouse embryo, brain, embryonic heart, and liver lobule with generally high reproducibility (average max. accuracy = 92.0%), while revealing topologically informative genes, or spatial discriminator genes. Furthermore, eSPRESSO was used for temporal analysis of human pancreatic organoids to infer rational developmental trajectories with several candidate 'temporal' discriminator genes responsible for various cell type differentiations. CONCLUSIONS: eSPRESSO provides a novel strategy for analyzing mechanisms underlying the spatio-temporal formation of cellular organizations.

In this paper, we propose tree edit distance with variables, which is an extension of the tree edit distance to handle trees with variables and has a potential application to measuring the similarity between mathematical formulas. We analyze the computational complexity of several variants of this model. In particular, we show that the problem is NP-complete for ordered trees. We also show for unordered trees that the problem of deciding whether or not the distance is 0 is graph isomorphism complete but can be solved in polynomial time if the maximum outdegree of input trees is bounded by a constant. We also present parameterized and exponential-time algorithms for ordered and unordered cases, respectively.

Boolean networks (BNs) have been developed to describe various biological processes, which requires analysis of attractors, the long-term stable states. While many methods have been proposed to detection and enumeration of attractors, there are no methods which have been demonstrated to be theoretically better than the naive method and be practically used for large biological BNs. Here, we present a novel method to calculate attractors based on a priori information, which works much and verifiably faster than the naive method. We apply the method to two BNs which differ in size, modeling formalism, and biological scope. Despite these differences, the method presented here provides a powerful tool for the analysis of both networks. First, our analysis of a BN studying the effect of the microenvironment during angiogenesis shows that the previously defined microenvironments inducing the specialized phalanx behavior in endothelial cells (ECs) additionally induce stalk behavior. We obtain this result from an extended network version which was previously not analyzed. Second, we were able to heuristically detect attractors in a cell cycle control network formalized as a bipartite Boolean model (bBM) with 3158 nodes. These attractors are directly interpretable in terms of genotype-to-phenotype relationships, allowing network validation equivalent to an in silico mutagenesis screen. Our approach contributes to the development of scalable analysis methods required for whole-cell modeling efforts.

Although ROS (Robotic Operating System) has attracted attention to enhance the productivity of robot software development, it is necessary to adopt the device with high function and large power consumption enough to install Linux. This paper designs a lightweight runtime environment of ROS nodes onto mid-range embedded devices. Our environment, that is named to mROS, consists of a real-time OS and TCP/IP protocol stack to provide a tiny ROS communication library. mROS provides the connectivity to host and other ROS nodes with the native ROS network protocol. One of advantages for mROS is that native ROS nodes can be ported from Linux-based systems to RTOS-based systems since APIs with the same name of native ROS can be used in the embedded program. Experimental results validate that the performance requirement of mROS can be achieved for the construction of distributed robot systems.

Recent years, various services using Cloud computing are involving in people's life. However, when techniques and devices of Internet of Things (IoT) are getting more involved in people's life, current Cloud computing can hardly satisfy the requirement of low latency. To address the above problem, new distributed computing called Fog computing is proposed by Cisco System, Inc. Fog computing has a simple three-tiered hierarchy: Edge device, Fog and Cloud. The Fog tier is put between the Edge device tier and the Cloud tier to play the same or part of roles of the Cloud tier by its proximity to Edge device. The Fog tier consists of some fog nodes. The fog node receives jobs from the Edge device tier. Then, according to the jobs, the fog node decides to either process the jobs or assign it Cloud tier immediately. We call this process “Job assignment”. However, the division of roles between the Fog tier and the cloud tier is not clear and it must be decided depending on the system. In this paper, we express the Fog computing with VCHS (Various Customers, Heterogeneous Servers) queueing model. We then evaluate this model and study on an effective Job assignment strategy.

MOTIVATION: RNA-RNA interactions via base pairing play a vital role in the post-transcriptional regulation of gene expression. Efficient identification of targets for such regulatory RNAs needs not only discriminative power for positive and negative RNA-RNA interacting sequence data but also accurate prediction of interaction sites from positive data. Recently, a few studies have incorporated interaction site accessibility into their prediction methods, indicating the enhancement of predictive performance on limited positive data. RESULTS: Here we show the efficacy of our accessibility-based prediction model RactIPAce on newly compiled datasets. The first experiment in interaction site prediction shows that RactIPAce achieves the best predictive performance on the newly compiled dataset of experimentally verified interactions in the literature as compared with the state-of-the-art methods. In addition, the second experiment in discrimination between positive and negative interacting pairs reveals that the combination of accessibility-based methods including our approach can be effective to discern real interacting RNAs. Taking these into account, our prediction model can be effective to predict interaction sites after screening for real interacting RNAs, which will boost the functional analysis of regulatory RNAs. AVAILABILITY AND IMPLEMENTATION: The program RactIPAce along with data used in this work is available at https://github.com/satoken/ractip/releases/tag/v1.0.1 CONTACT: : [email protected] or [email protected] information: Supplementary data are available at Bioinformatics online.

In this paper, we have extended the concept of unordered tree inclusion to take the costs of insertions and substitutions into account. The resulting algorithm, MinCostIncl, has the same time complexity as the original algorithm of [4] for unordered tree inclusion (O(22Dmn)). Computational experiments on a large synthetic dataset as well as real datasets showed that our proposed algorithm is fast and scalable. Source codes of the implemented algorithms are available upon request.

We study the number of samples required to uniquely determine the structure of a probabilistic Boolean network (PBN), where PBNs are probabilistic extensions of Boolean networks. We show via theoretical analysis and computational analysis that the structure of a PBN can be exactly identified with high probability from a relatively small number of samples for interesting classes of PBNs of bounded indegree. On the other hand, we also show that there exist classes of PBNs for which it is impossible to uniquely determine the structure of a PBN from samples.

This paper considers the problem of identifying all locations of subtrees in a large tree or in a large collection of trees that are similar to a specified pattern tree, where all trees are assumed to be rooted and node-labeled. The tree edit distance is a widely-used measure of tree (dis-)similarity, but is NP-hard to compute for unordered trees. To cope with this issue, we propose a new similarity measure which extends the concept of unordered tree inclusion by taking the costs of insertion and substitution operations on the pattern tree into account, and present an algorithm for computing it. Our algorithm has the same time complexity as the original one for unordered tree inclusion, i.e., it runs in O(|T1∥T2|) time, where T1and T2denote the pattern tree and the text tree, respectively, when the maximum outdegree of T1is bounded by a constant. Our experimental evaluation using synthetic and real datasets confirms that the proposed algorithm is fast and scalable and very useful for bibliographic matching, which is a typical entity resolution problem for tree-structured data. Furthermore, we extend our algorithm to also allow a constant number of deletion operations on T1while still running in O(|T1∥T2|) time.

The tree edit distance is one of the most widely used measures for comparison of tree structured data and has been used for analysis of RNA secondary structures, glycan structures, and vascular trees. However, it is known that the tree edit distance problem is NP-hard for unordered trees while it is polynomial time solvable for ordered trees. We have recently proposed a clique-based method for computing the tree edit distance between unordered trees in which each instance of the tree edit distance problem is transformed into an instance of the maximum vertex weighted clique problem and then an existing clique algorithm is applied. In this paper, we propose an improved clique-based method. Different from our previous method, the improved method is basically a dynamic programming algorithm that repeatedly solves instances of the maximum vertex weighted clique problem as sub-problems. Other heuristic techniques, which do not violate the optimality of the solution, are also introduced. When applied to comparison of large glycan structures, our improved method showed significant speed-up in most cases.