Expression Pattern Matching Research Articles

Mechanism-free repurposing of drugs for C9orf72-related ALS/FTD using large-scale genomic data.

Repeat expansions in the C9orf72 gene are the most common genetic cause of (ALS) and frontotemporal dementia (FTD). Like other genetic forms of neurodegeneration, pinpointing the precise mechanism(s) by which this mutation leads to neuronal death remains elusive, and this lack of knowledge hampers the development of therapy for C9orf72-related disease. We used an agnostic approach based on genomic data (n= 41,273 ALS and healthy samples, and n= 1,516 C9orf72 carriers) to overcome these bottlenecks. Our drug-repurposing screen, based on gene- and expression-pattern matching and information about the genetic variants influencing onset age among C9orf72 carriers, identified acamprosate, a γ-aminobutyric acid analog, as a potentially repurposable treatment for patients carrying C9orf72 repeat expansions. We validated its neuroprotective effect in cell models and showed comparable efficacy to riluzole, the current standard of care. Our work highlights the potential value of genomics in repurposing drugs insituations where the underlying pathomechanisms are inherently complex. VIDEO ABSTRACT.

Verification of Program Transformations with Inductive Refinement Types

High-level transformation languages like Rascal include expressive features for manipulating large abstract syntax trees: first-class traversals, expressive pattern matching, backtracking, and generalized iterators. We present the design and implementation of an abstract interpretation tool, Rabit, for verifying inductive type and shape properties for transformations written in such languages. We describe how to perform abstract interpretation based on operational semantics, specifically focusing on the challenges arising when analyzing the expressive traversals and pattern matching. Finally, we evaluate Rabit on a series of transformations (normalization, desugaring, refactoring, code generators, type inference, etc.) showing that we can effectively verify stated properties.

Verification of high-level transformations with inductive refinement types

High-level transformation languages like Rascal include expressive features for manipulating large abstract syntax trees: first-class traversals, expressive pattern matching, backtracking and generalized iterators. We present the design and implementation of an abstract interpretation tool, Rabit, for verifying inductive type and shape properties for transformations written in such languages. We describe how to perform abstract interpretation based on operational semantics, specifically focusing on the challenges arising when analyzing the expressive traversals and pattern matching. Finally, we evaluate Rabit on a series of transformations (normalization, desugaring, refactoring, code generators, type inference, etc.) showing that we can effectively verify stated properties.

Research on Complex Event Detection over Streams Supporting Interval Temporal Logic and Regular Expression Pattern Matching

To improve the detective ability of complex event processing (CEP), we combine interval temporal logic (ITL) and regular expression in pattern description of event streams constraint. We propose different timed automata to support varied ITL. The pattern described by regular expression improves the simplicity of the pattern description. Meanwhile, we have implemented above technique in a CEP system to test performance. The experimental evaluation shows that the pattern can be efficiently and accurately matched.

A novel JSON based regular expression language for pattern matching in the internet of things

The Internet of Things work by constantly sensing the physical properties in the vicinity of the user such as ambient light, sounds, motion and temperature. These sensors produce huge volumes of data that has to be efficiently sifted for relevant events required triggering certain actions. In addition, filtering has to be performed to ensure that privacy-sensitive confidential data is not leaked. Efficient and expressive pattern matching is thus a key enabling technology for the full realization of ambient and humanized computing. The bulk of research in this area has focused on the use of specialized hardware and reducing of the memory footprint. Unfortunately, there has been limited work if any on optimizing the core elements of pattern matching- the regular expression language and the compilation process that is responsible for converting patterns into internal data structures. The importance of writing good REs so that on compilation they do not lead to unrealizable data structures is relatively less understood. In the proposed research, we empirically compare different RE processing engines and practically demonstrate that the compilation phase is highly memory intensive and time-consuming as compared to the matching phase -and hence is worth exploring for new techniques and optimizations. As a second important contribution, we propose a novel technique for defining regular expressions by utilizing JavaScript Object Notation. Our evaluation with carefully created patterns shows that the performance of the proposed technique is at par with competing approaches. It is also less ambiguous, extensible, more expressive and much appropriate for defining large and complex patterns.

Rancang Bangun Aplikasi Chatbot Sebagai Media Pencarian Informasi Anime Menggunakan Regular Expression Pattern Matching

Technology development has affected many areas of life, especially the entertainment field. One of the fastest growing entertainment industry is anime. Anime has evolved as a trend and a hobby, especially for the population in the regions of Asia. The number of anime fans grow every year and trying to dig up as much information about their favorite anime. Therefore, a chatbot application was developed in this study as anime information retrieval media using regular expression pattern matching method. This application is intended to facilitate the anime fans in searching for information about the anime they like. By using this application, user can gain a convenience and interactive anime data retrieval that can’t be found when searching for information via search engines. Chatbot application has successfully met the standards of information retrieval engine with a very good results, the value of 72% precision and 100% recall showing the harmonic mean of 83.7%. As the application of hedonic, chatbot already influencing Behavioral Intention to Use by 83% and Immersion by 82%.
 Index Terms—anime, chatbot, information retrieval, Natural Language Processing (NLP), Regular Expression Pattern Matching

Reconstruction of gene co-expression network from microarray data using local expression patterns.

BackgroundBiological networks connect genes, gene products to one another. A network of co-regulated genes may form gene clusters that can encode proteins and take part in common biological processes. A gene co-expression network describes inter-relationships among genes. Existing techniques generally depend on proximity measures based on global similarity to draw the relationship between genes. It has been observed that expression profiles are sharing local similarity rather than global similarity. We propose an expression pattern based method called GeCON to extract Gene CO-expression Network from microarray data. Pair-wise supports are computed for each pair of genes based on changing tendencies and regulation patterns of the gene expression. Gene pairs showing negative or positive co-regulation under a given number of conditions are used to construct such gene co-expression network. We construct co-expression network with signed edges to reflect up- and down-regulation between pairs of genes. Most existing techniques do not emphasize computational efficiency. We exploit a fast correlogram matrix based technique for capturing the support of each gene pair to construct the network.ResultsWe apply GeCON to both real and synthetic gene expression data. We compare our results using the DREAM (Dialogue for Reverse Engineering Assessments and Methods) Challenge data with three well known algorithms, viz., ARACNE, CLR and MRNET. Our method outperforms other algorithms based on in silico regulatory network reconstruction. Experimental results show that GeCON can extract functionally enriched network modules from real expression data.ConclusionsIn view of the results over several in-silico and real expression datasets, the proposed GeCON shows satisfactory performance in predicting co-expression network in a computationally inexpensive way. We further establish that a simple expression pattern matching is helpful in finding biologically relevant gene network. In future, we aim to introduce an enhanced GeCON to identify Protein-Protein interaction network complexes by incorporating variable density concept.

Evidence that an olive derivative can beneficially affect intrinsic aging, photoaging, and inflammatory skin conditions based on gene expression profiling and pattern matching

Evidence that an olive derivative can beneficially affect intrinsic aging, photoaging, and inflammatory skin conditions based on gene expression profiling and pattern matching

Computer-Aided Word Mastermind Solving Using Regular Expression Pattern Matching in a Directed Acyclic Word Graph

Computer-Aided Word Mastermind Solving Using Regular Expression Pattern Matching in a Directed Acyclic Word Graph

CDA: Combinatorial Drug Discovery Using Transcriptional Response Modules

BackgroundAnticancer therapies that target single signal transduction pathways often fail to prevent proliferation of cancer cells because of overlapping functions and cross-talk between different signaling pathways. Recent research has identified that balanced multi-component therapies might be more efficacious than highly specific single component therapies in certain cases. Ideally, synergistic combinations can provide 1) increased efficacy of the therapeutic effect 2) reduced toxicity as a result of decreased dosage providing equivalent or increased efficacy 3) the avoidance or delayed onset of drug resistance. Therefore, the interest in combinatorial drug discovery based on systems-oriented approaches has been increasing steadily in recent years.MethodologyHere we describe the development of Combinatorial Drug Assembler (CDA), a genomics and bioinformatics system, whereby using gene expression profiling, multiple signaling pathways are targeted for combinatorial drug discovery. CDA performs expression pattern matching of signaling pathway components to compare genes expressed in an input cell line (or patient sample data), with expression patterns in cell lines treated with different small molecules. Then it detects best pattern matching combinatorial drug pairs across the input gene set-related signaling pathways to detect where gene expression patterns overlap and those predicted drug pairs could likely be applied as combination therapy. We carried out in vitro validations on non-small cell lung cancer cells and triple-negative breast cancer (TNBC) cells. We found two combinatorial drug pairs that showed synergistic effect on lung cancer cells. Furthermore, we also observed that halofantrine and vinblastine were synergistic on TNBC cells.ConclusionsCDA provides a new way for rational drug combination. Together with phExplorer, CDA also provides functional insights into combinatorial drugs. CDA is freely available at http://cda.i-pharm.org.

GPU-based NFA implementation for memory efficient high speed regular expression matching

Regular expression pattern matching is the foundation and core engine of many network functions, such as network intrusion detection, worm detection, traffic analysis, web applications and so on. DFA-based solutions suffer exponentially exploding state space and cannot be remedied without sacrificing matching speed. Given this scalability problem of DFA-based methods, there has been increasing interest in NFA-based methods for memory efficient regular expression matching. To achieve high matching speed using NFA, it requires potentially massive parallel processing, and hence represents an ideal programming task on Graphic Processor Unit (GPU). Based on in-depth understanding of NFA properties as well as GPU architecture, we propose effective methods for fitting NFAs into GPU architecture through proper data structure and parallel programming design, so that GPU's parallel processing power can be better utilized to achieve high speed regular expression matching. Experiment results demonstrate that, compared with the existing GPU-based NFA implementation method [9], our proposed methods can boost matching speed by 29~46 times, consistently yielding above 10Gbps matching speed on NVIDIA GTX-460 GPU. Meanwhile, our design only needs a small amount of memory space, growing exponentially more slowly than DFA size. These results make our design an effective solution for memory efficient high speed regular expression matching, and clearly demonstrate the power and potential of GPU as a platform for memory efficient high speed regular expression matching.

MEASURING INCONSISTENCY IN A NETWORK INTRUSION DETECTION RULE SET BASED ON SNORT

In this preliminary study, we investigate how inconsistency in a network intrusion detection rule set can be measured. To achieve this, we first examine the structure of these rules which are based on Snort and incorporate regular expression (Regex) pattern matching. We then identify primitive elements in these rules in order to translate the rules into their (equivalent) logical forms and to establish connections between them. Additional rules from background knowledge are also introduced to make the correlations among rules more explicit. We measure the degree of inconsistency in formulae of such a rule set (using the Scoring function, Shapley inconsistency values and Blame measure for prioritized knowledge) and compare the informativeness of these measures. Finally, we propose a new measure of inconsistency for prioritized knowledge which incorporates the normalized number of atoms in a language involved in inconsistency to provide a deeper inspection of inconsistent formulae. We conclude that such measures are useful for the network intrusion domain assuming that introducing expert knowledge for correlation of rules is feasible.

INFAnt

This paper presents iNFAnt, a parallel engine for regular expression pattern matching. In contrast with traditional approaches, iNFAnt adopts non-deterministic automata, allowing the compilation of very large and complex rule sets that are otherwise hard to treat. iNFAnt is explicitly designed and developed to run on graphical processing units that provide large amounts of concurrent threads; this parallelism is exploited to handle the non-determinism of the model and to process multiple packets at once, thus achieving high performance levels.

A framework for traversing dense annotation lattices

Pattern matching, or querying, over annotations is a general purpose paradigm for inspecting, navigating, mining, and transforming annotation repositories—the common representation basis for modern pipelined text processing architectures. The open-ended nature of these architectures and expressiveness of feature structure-based annotation schemes account for the natural tendency of such annotation repositories to become very dense, as multiple levels of analysis get encoded as layered annotations. This particular characteristic presents challenges for the design of a pattern matching framework capable of interpreting ‘flat’ patterns over arbitrarily dense annotation lattices. We present an approach where a finite state device applies (compiled) pattern grammars over what is, in effect, a linearized ‘projection’ of a particular route through the lattice. The route is derived by a mix of static grammar analysis and runtime interpretation of navigational directives within an extended grammar formalism; it selects just the annotations sequence appropriate for the patterns at hand. For expressive and efficient pattern matching in dense annotations stores, our implemented approach achieves a mix of lattice traversal and finite state scanning by exposing a language which, to its user, provides constructs for specifying sequential, structural, and configurational constraints among annotations.

Regular expression filters for XML

XML data are described by types involving regular expressions. This raises the question of what language feature is convenient for manipulating such data. Previously, we have given an answer to this question by proposing regular expression pattern matching. However, since this construct is derived from ML pattern matching, it does not have an iteration functionality in itself, which makes it cumbersome to process data typed by Kleene stars. In this paper, we propose a novel programming feature regular expression filters. This construct extends the previous proposal by permitting pattern clauses to be closed under arbitrary regular expression operators. This yields many convenient programming idioms such as non-uniform processing of sequences and almost-copying of trees. We further develop a type inference mechanism that obtains (1) types for pattern variables that are locally precise with respect to the type of input values and (2) a type for the result of the whole filter expression that is also locally precise with respect to the types of the body expressions. We discuss how our construct is useful in the practice of XML processing and, in particular, how our type inference is crucial for avoiding changes of programs when types of data to be processed evolve frequently.

Dynamic visual data mining: biological sequence analysis and annotation using SeqVISTA

In the post-genomic era, the volume of public sequence databases is increasing exponentially and visualisation-centric techniques have become more and more important in biological sequence analysis and annotation. In this paper, we present a methodology called dynamic visual data mining (DVDM), which combines biological object modelling, interactive display, and data analysis tools into one integrative platform. Using Java Development Kit v1.4, an object-oriented software named SeqVISTA has been developed based on DVDM. To illustrate the application of SeqVISTA, the following examples are shown: regular expression pattern matching; comparative analysis of alternative exon splicing patterns; Fourier analyses; exon prediction (MZEF and GENSCAN). Overall, we argue that DVDM is an important technique for biologists to unveil the information hidden behind the large genomic and proteomic databases, and SeqVISTA provides a versatile tool that integrates multiple computational algorithms for meeting biologists' data mining needs.

Generating Optimal Monitors for Extended Regular Expressions

Ordinary software engineers and programmers can easily understand regular patterns, as shown by the immense interest in and the success of scripting languages like Perl, based essentially on regular expression pattern matching. We believe that regular expressions provide an elegant and powerful specification language also for monitoring requirements, because an execution trace of a program is in fact a string of states. Extended regular expressions (EREs) add complementation to regular expressions, which brings additional benefits by allowing one to specify patterns that must not occur during an execution. Complementation gives one the power to express patterns on strings more compactly. In this paper we present a technique to generate optimal monitors from EREs. Our monitors are deterministic finite automata (DFA) and our novel contribution is to generate them using a modern coalgebraic technique called coinduction. Based on experiments with our implementation, which can be publicly tested and used over the web, we believe that our technique is more efficient than the simplistic method based on complementation of automata which can quickly lead to a highly-exponential state explosion.

A Boyer–Moore-style algorithm for regular expression pattern matching

This paper presents a Boyer–Moore-type algorithm for regular expression pattern matching, answering an open problem posed by Aho in 1980 (Pattern Matching in Strings, Academic Press, New York, 1980, p. 342). The new algorithm handles patterns specified by regular expressions—a generalization of the Boyer–Moore and Commentz-Walter algorithms. Like the Boyer–Moore and Commentz-Walter algorithms, the new algorithm makes use of shift functions which can be precomputed and tabulated. The precomputation algorithms are derived, and it is shown that the required shift functions can be precomputed from Commentz-Walter's d 1 and d 2 shift functions. In certain cases, the Boyer–Moore (respectively Commentz-Walter) algorithm has greatly outperformed the Knuth–Morris–Pratt (respectively Aho–Corasick) algorithm (as discussed by Watson in his Ph.D. Thesis, Eindhoven University of Technology, September 1995, and in: N. Ziviani, R. Baeza-Yates, K. Guimaraes (Eds.), Proc. Third South American Workshop on String Processing, International Informatics Series, vol. 4, Carleton University Press, Recife, Brazil, 1996, pp. 280–294). In testing, the algorithm presented in this paper also frequently outperforms the regular expression generalization of the Aho–Corasick algorithm.

XDuce

XDuce is a statically typed programming language for XML processing. Its basic data values are XML documents, and its types (so-called regular expression types ) directly correspond to document schemas. XDuce also provides a flexible form of regular expression pattern matching , integrating conditional branching, tag checking, and subtree extraction, as well as dynamic typechecking. We survey the principles of XDuce's design, develop examples illustrating its key features, describe its foundations in the theory of regular tree automata, and present a complete formal definition of its core, along with a proof of type safety.

Transcriptional control of nutrient partitioning during rice grain filling

Cereal grains accumulate carbohydrates, storage proteins and fatty acids via different pathways during their development. Many genes that participate in nutrient partitioning during grain filling and that affect starch quality have been identified. To understand how the expression of these genes is coordinated during grain development, a genomic approach to surveying the participation and interactions of all the pathways is necessary. Using recently published rice genome information, we designed a rice GeneChip microarray that covers half the rice genome. By monitoring the expression of 21,000 genes in parallel, we identified genes involved in the grain filling process and found that the expression of genes involved in different pathways is coordinately controlled in a synchronized fashion during grain filling. Interestingly, a known promoter element in genes encoding seed storage proteins, AACA, is statistically over-represented among the 269 genes in different pathways with diverse functions that are significantly up-regulated during grain filling. By expression pattern matching, a group of transcription factors that have the potential to interact with this element was identified. We also found that most genes in the starch biosynthetic pathway show multiple distinct spatial and temporal expression patterns, suggesting that different isoforms of a given enzyme are expressed in different tissues and at different developmental stages. Our results reveal key regulatory machinery and provide an opportunity for modifying multiple pathways by manipulating key regulatory elements for improving grain quality and quantity.