1,721,007 research outputs found
A Loop Grammar to Understand the roles of miRNAs in the Tumor Cell
Full text linkA miRNA is a small non-coding RNA molecule that regulates gene expression. Current studies showed that miRNAs may function both as oncogenes and as tumor suppressors, but not revealed the precise conditions that cause miRNAs to alter gene expression of the cancer cells. In this study, we introduce a context-free grammar, Loop Grammar, that formalizes the primary and secondary structure as a composition of loops, corresponding to concatenation or nesting of hairpins. We also formalize the concatenation and nesting on fatgraphs, oriented surfaces with boundary, and we define a Surface Loop Grammar, whose algebraic expressions uniquely identify such surfaces associated to given RNA structures. The Loop Grammar has been used to model tumor and healthy miRNAs of the mir-515 family, and we observed that the mutations of elements of primary structure involved in loops formation changed the secondary structure of tumor miRNAs. The Surface Loop Grammar is useful to classify RNA structures in terms of loops and relations among them.
References:
1) Peng, Y., Croce, C. M. The role of MicroRNAs in human cancer. Signal transduction and targeted
therapy, 2016, 1, 15004.
2) Penner, R.C., Knudsen, M., Wiuf, C., Andersen, J.E., Fatgraph models of proteins. Communications on Pure and Applied Mathematics, 2010, 63(10), 1249–1297
3) Quadrini, M., Culmone, R., Merelli, E.: Topological Classification of RNA Structures via Intersection Graph. In: International Conference on Theory and Practice of Natural Computing, Springer, 2017, 203–215
4) Quadrini, M., Merelli, E.: Loop-loop interaction metrics on RNA secondary structures with pseudoknotsth International Conference on Bioinformatics Models, Methods and Algorithms, Proceedings; Part of 11th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2018 3, 2018
Searching RNA Substructures with Arbitrary Pseudoknots
No full textRNA functions depend on its three-dimensional structure formed largely from hydrogen bonds between pairs of nucleotides. RNAs with analogous functions exhibit highly similar structures without showing significant sequence similarity necessarily. Understanding the relationships between the structure and the functions has been considered one of the challenges in biology. In this study, we face the problem of identifying a given structural pattern into an RNA secondary structure with arbitrary pseudoknots. We abstract the shape in terms of secondary structure, formalized by the arc diagram, and we introduce a set of operators necessary and sufficient to describe any arc diagram in terms of relations among loops. For each molecule, we uniquely associate the relation matrix, and we face the aforementioned problem in terms of searching a submatrix. The algorithms work in polynomial time
Exploiting the Role of Features for Antigens-Antibodies Interaction Site Prediction
No full textAntibodies are a class of proteins that recognize and neutralize pathogens by binding to their antigens. They are the most significant category of biopharmaceuticals for both diagnostic and therapeutic applications. Understanding how antibodies interact with their antigens plays a fundamental role in drug and vaccine design and helps to comprise the complex antigen binding mechanisms. Computational methods for predicting interaction sites of antibody-antigen are of great value due to the overall cost of experimental methods. Machine learning methods and deep learning techniques obtained promising results. In this work, we predict antibody interaction interface sites by applying HSS-PPI, a hybrid method defined to predict the interface sites of general proteins. The approach abstracts the proteins in terms of hierarchical representation and uses a graph convolutional network to classify the amino acids between interface and non-interface. Moreover, we also equipped the amino acids with different sets of physicochemical features together with structural ones to describe the residues. Analyzing the results, we observe that the structural features play a fundamental role in the amino acid descriptions. We compare the obtained performances, evaluated using standard metrics, with the ones obtained with SVM with 3D Zernike descriptors, Parapred, Paratome, and Antibody i-Patch
A SPATIAL LOGIC FOR SIMPLICIAL MODELS
Full text linkCollective Adaptive Systems often consist of many heterogeneous components typically organised in groups. These entities interact with each other by adapting their behaviour to pursue individual or collective goals. In these systems, the distribution of these entities determines a space that can be either physical or logical. The former is defined in terms of a physical relation among components. The latter depends on logical relations, such as being part of the same group. In this context, specification and verification of spatial properties play a fundamental role in supporting the design of systems and predicting their behaviour. For this reason, different tools and techniques have been proposed to specify and verify the properties of space, mainly described as graphs. Therefore, the approaches generally use model spatial relations to describe a form of proximity among pairs of entities. Unfortunately, these graph-based models do not permit considering relations among more than two entities that may arise when one is interested in describing aspects of space by involving interactions among groups of entities. In this work, we propose a spatial logic interpreted on simplicial complexes. These are topological objects, able to represent surfaces and volumes efficiently that generalise graphs with higher-order edges. We discuss how the satisfaction of logical formulas can be verified by a correct and complete model checking algorithm, which is linear to the dimension of the simplicial complex and logical formula. The expressiveness of the proposed logic is studied in terms of the spatial variants of classical bisimulation and branching bisimulation relations defined over simplicial complexes
Structural relation matching: an algorithm to identify structural patterns into RNAs and their interactions
Full text linkRNA molecules play crucial roles in various biological processes. Their three-dimensional configurations determine the functions and, in turn, influences the interaction with other molecules. RNAs and their interaction structures, the so-called RNA–RNA interactions, can be abstracted in terms of secondary structures, i.e., a list of the nucleotide bases paired by hydrogen bonding within its nucleotide sequence. Each secondary structure, in turn, can be abstracted into cores and shadows. Both are determined by collapsing nucleotides and arcs properly. We formalize all of these abstractions as arc diagrams, whose arcs determine loops. A secondary structure, represented by an arc diagram, is pseudoknot-free if its arc diagram does not present any crossing among arcs otherwise, it is said pseudoknotted. In this study, we face the problem of identifying a given structural pattern into secondary structures or the associated cores or shadow of both RNAs and RNA–RNA interactions, characterized by arbitrary pseudoknots. These abstractions are mapped into a matrix, whose elements represent the relations among loops. Therefore, we face the problem of taking advantage of matrices and submatrices. The algorithms, implemented in Python, work in polynomial time. We test our approach on a set of 16S ribosomal RNAs with inhibitors of Thermus thermophilus, and we quantify the structural effect of the inhibitors
Hierarchical Representation and Graph Convolutional Networks for the Prediction of Protein–Protein Interaction Sites
No full textProteins carry out a broad range of functions in living organisms usually by interacting with other molecules. Protein–protein interaction (PPI) is an important base for understanding disease mechanisms and for deciphering rational drug design. The identification of protein interactions using experimental methods is expensive and time-consuming. Therefore, efficient computational methods to predict PPIs are of great value to biologists. This work focuses on predicting protein interfaces and investigates the effect of different molecular representations in the prediction of such sites. We introduce a molecular representation according to its hierarchical structure. Therefore, proteins are abstracted in terms of spatial and sequential neighboring among amino acid pairs, while we use a deep learning framework, Graph Convolutional Networks, for data training. We tested the framework on two classes of proteins, Antibody–Antigen and Antigen–Bound Antibody, extracted from the Protein–Protein Docking Benchmark 5.0. The obtained results in terms of the area under the ROC curve (AU-ROC) on these classes are remarkable
CIBB 2019: 16th International Conference on Computational Intelligence methods for Bioinformatics and Biostatics. Special session on ALGEBRAIC AND COMPUTATIONAL METHODS FOR THE STUDY OF RNA BEHAVIOUR
No full texthttps://dinamico2.unibg.it/cazzaniga/cibb2019/specialsessions.htm
Label core for understanding RNA structure
Full text linkThe RNA structure, the main predictor of biological function, is the result of the folding process. While the nucleotides in the RNA sequence rapidly coupled forming weak bonds, the spatial arrangement is a slow process. Although many computational approaches have been proposed to study the folding process of RNA, most of them do not consider the hierarchical aspect existing among the bonds. In this work, we propose to collapse nucleotides and bonds underpinning the primary and secondary structure of RNA in a unique label core congruent with the spatial configuration. A label core is represented as a term of generalized context-free grammar properly defined to support RNA structural reduction and analysis
Comparison of Machine Learning approaches for Stress Detection from Wearable Sensors Data
No full textStress is a prevalent and growing phenomenon in the modern world potentially leading to significant repercussions on both physical and mental health. The analysis of physiological signals, collected from wearable sensors, has emerged as a promising approach to predicting and managing stress. Methods based on machine learning techniques have been defined in the literature and achieved promising results by using handcrafted features extracted from the signal. However, there is no consensus on the list of features, while deep learning approaches that overcomes the problem require significant computational power and a large amount of data. In this paper, we present a comprehensive view of the most common representative machine learning algorithms applied to the stress detection domain by giving a reference point for both academia and industry professionals in this application field. This study considers fragments of signals without extracting any features and uses a public dataset, WESAD, that contains high-resolution physiological, including blood volume pulse, electrocardiogram and electromyogram. The data collected from 15 subjects during a lab study are heterogeneous and characterized by different frequencies and noises due to some devices. After preprocessing, we assess the performance of ten machine learning algorithms belonging to four models (tree, ensemble, linear and neighbours) on the WESAD by facing the problem as binary (stress/no-stress) and multiclass (baseline, stress, and amusement) classifications. Our results, evaluated in terms of classical metrics, show that Random Forest outperforms the others in binary and multi-class approaches
An integral equation method for the numerical solution of the Burgers equation
No full textWe consider an initial–boundary value problem for the two-dimensional Burgers equation on the plane. This problem is reformulated by an equivalent integral equation on the Fourier transform space. For the solution of this integral equation, two numerical methods are proposed. One of these two methods is based on the properties of the Gaussian function, whereas the other one is based on the FFT algorithm. Finally, the Galerkin method with Gaussian basis functions is applied to the original initial–boundary value problem, in order to compare the performances of the proposed methods with a standard numerical procedure. Some numerical examples are given to evaluate the efficiency of the proposed methods. The performances obtained from these numerical experiments promise that these methods can be applied effectively to more complex problems, such as the Navier–Stokes equation and the turbulence flows
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