Integrated querying and version control of context-specific biological networks

Abstract Motivation Biomolecular data stored in public databases is increasingly specialized to organisms, context/pathology and tissue type, potentially resulting in significant overhead for analyses. These networks are often specializations of generic interaction sets, presenting opportunities for reducing storage and computational cost. Therefore, it is desirable to develop effective compression and storage techniques, along with efficient algorithms and a flexible query interface capable of operating on compressed data structures. Current graph databases offer varying levels of support for network integration. However, these solutions do not provide efficient methods for the storage and querying of versioned networks. Results We present VerTIoN, a framework consisting of novel data structures and associated query mechanisms for integrated querying of versioned context-specific biological networks. As a use case for our framework, we study network proximity queries in which the user can select and compose a combination of tissue-specific and generic networks. Using our compressed version tree data structure, in conjunction with state-of-the-art numerical techniques, we demonstrate real-time querying of large network databases. Conclusion Our results show that it is possible to support flexible queries defined on heterogeneous networks composed at query time while drastically reducing response time for multiple simultaneous queries. The flexibility offered by VerTIoN in composing integrated network versions opens significant new avenues for the utilization of ever increasing volume of context-specific network data in a broad range of biomedical applications. Availability and Implementation VerTIoN is implemented as a C++ library and is available at http://compbio.case.edu/omics/software/vertion and https://github.com/tjcowman/vertion Contact [email protected]

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MERGEABLE DOUBLE-ENDED PRIORITY QUEUES

International Journal of Foundations of Computer Science ◽

10.1142/s0129054199000022 ◽

1999 ◽

Vol 10 (01) ◽

pp. 1-17 ◽

Cited By ~ 6

Author(s):

SEONGHUN CHO ◽

SARTAJ SAHNI

Keyword(s):

Data Structure ◽

Data Structures ◽

Priority Queue ◽

Priority Queues ◽

Experimental Results ◽

Tree Data ◽

Tree Data Structure

We show that the leftist tree data structure may be adapted to obtain data structures that permit the double-ended priority queue operations Insert, DeleteMin, DeleteMax, and Merge to be done in O( log n) time where n is the size of the resulting queue. The operations FindMin and FindMax can be done in O(1) time. Experimental results are also presented.

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On the Study of Automatic NC-Code Programming Based on DXF Files

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.48-49.767 ◽

2011 ◽

Vol 48-49 ◽

pp. 767-772 ◽

Cited By ~ 1

Author(s):

Rui Hang Shi ◽

Lei Luo ◽

Zhen Qiang Yao

Keyword(s):

Data Structure ◽

Data Structures ◽

Object Oriented ◽

Programming System ◽

Automatic Programming ◽

Technological Parameters ◽

Nc Code ◽

Processing Information ◽

Tree Data ◽

Tree Data Structure

In this paper, a method of automatic NC code programming is studied. The data structure used to store graph information is the base of automatic programming system. By utilizing the object-oriented tree data structure proposed in the paper, the disadvantages of the method caused by using simple data structures can be overcome. In processing information, the judgment of topological relationship between two outlines is very important. The Loop Extremum Method is easy, convenient and with no limitations. The system adds plenty of technological parameters, provides multiple machining patterns. In the end, an example is given to test and verify the system`s functions.

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Granule-Based-Classifier (GbC): A Lattice Computing Scheme Applied on Tree Data Structures

Mathematics ◽

10.3390/math9222889 ◽

2021 ◽

Vol 9 (22) ◽

pp. 2889

Author(s):

Vassilis G. Kaburlasos ◽

Chris Lytridis ◽

Eleni Vrochidou ◽

Christos Bazinas ◽

George A. Papakostas ◽

...

Keyword(s):

Pattern Recognition ◽

Data Structures ◽

Real World ◽

Head Orientation ◽

Human Face ◽

Human Face Recognition ◽

Face Representation ◽

Tree Data ◽

Tree Data Structure ◽

Logic Reasoning

Social robots keep proliferating. A critical challenge remains their sensible interaction with humans, especially in real world applications. Hence, computing with real world semantics is instrumental. Recently, the Lattice Computing (LC) paradigm has been proposed with a capacity to compute with semantics represented by partial order in a mathematical lattice data domain. In the aforementioned context, this work proposes a parametric LC classifier, namely a Granule-based-Classifier (GbC), applicable in a mathematical lattice (T,⊑) of tree data structures, each of which represents a human face. A tree data structure here emerges from 68 facial landmarks (points) computed in a data preprocessing step by the OpenFace software. The proposed (tree) representation retains human anonymity during data processing. Extensive computational experiments regarding three different pattern recognition problems, namely (1) head orientation, (2) facial expressions, and (3) human face recognition, demonstrate GbC capacities, including good classification results, and a common human face representation in different pattern recognition problems, as well as data induced granular rules in (T,⊑) that allow for (a) explainable decision-making, (b) tunable generalization enabled also by formal logic/reasoning techniques, and (c) an inherent capacity for modular data fusion extensions. The potential of the proposed techniques is discussed.

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Nearly Balanced Quad List Quad Tree -A Data Structure for VLSI Layout Systems

VLSI Design ◽

10.1155/1996/82789 ◽

1996 ◽

Vol 4 (1) ◽

pp. 17-32

Author(s):

Pei-Yung Hsiao

Keyword(s):

Data Structure ◽

Data Structures ◽

Major Drawback ◽

Vlsi Layout ◽

The Past ◽

Quad Tree ◽

Tree Data ◽

Tree Data Structure ◽

Better Than ◽

Window Query

In the past ten years, many researchers have focused attention on developing better data structures for storing graphical information. Among the proposed data structures, the quad tree data structure provides a good way to organize objects on a 2-D plane. Region searches proceed at logarithmic speeds a desirable characteristic, but no previously proposed VLSI quad tree data structure distributed objects to subdivide the spatial area. This has been a major drawback for operations such as tree searching and window query. In this paper, we present a new division method to reconstruct those quad trees including the multiple storage quad tree (MSQT) and the quad list quad tree (QLQT) into nearly balanced quad tree data structures. Nearly balanced quad trees based on our new spatial division method are constructed by dynamically translating unbalanced multiple storage quad trees or unbalanced quad list quad trees into balanced structures. All benefits of the original quad tree data structures are completely retained. In addition, this method is simple and balanced quad trees memory require less than the original quad trees. Experimental results illustrate that the improvement in region queries of the presented nearly balanced quad trees to both of the QLQT and the MSQT is better than the improvement of the QLQT to the MSQT.

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Logical Structure for User Friendly dynamic Web Page Visualization for Small Screen Terminals Promoting E-Business

Recent Patents on Engineering ◽

10.2174/1872212114999201109204536 ◽

2020 ◽

Vol 14 ◽

Author(s):

Shefali Singhal ◽

Poonam Tanwar

Keyword(s):

Logical Structure ◽

Vital Role ◽

Web Pages ◽

Main Concern ◽

Web Page ◽

Large Screen ◽

Tree Data ◽

Tree Data Structure ◽

User Friendly ◽

Small Screen

Abstract:: Now-a-days when everything is going digitalized, internet and web plays a vital role in everyone’s life. When one has to ask something or has any online task to perform, one has to use internet to access relevant web-pages throughout. These web-pages are mainly designed for large screen terminals. But due to mobility, handy and economic reasons most of the persons are using small screen terminals (SST) like mobile phone, palmtop, pagers, tablet computers and many more. Reading a web page which is actually designed for large screen terminal on a small screen is time consuming and cumbersome task because there are many irrelevant content parts which are to be scrolled or there are advertisements, etc. Here main concern is e-business users. To overcome such issues the source code of a web page is organized in tree data-structure. In this paper we are arranging each and every main heading as a root node and all the content of this heading as a child node of the logical structure. Using this structure, we regenerate a web-page automatically according to SST size. Background:: DOM and VIPS algorithms are the main background techniques which are supporting the current research. Objective:: To restructure a web page in a more user friendly and content presenting format. Method Backtracking:: Method Backtracking: Results:: web page heading queue generation. Conclusion:: Concept of logical structure supports every SST.

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TABU PROGRAMMING: A NEW PROBLEM SOLVER THROUGH ADAPTIVE MEMORY PROGRAMMING OVER TREE DATA STRUCTURES

International Journal of Information Technology & Decision Making ◽

10.1142/s0219622011004373 ◽

2011 ◽

Vol 10 (02) ◽

pp. 373-406 ◽

Cited By ~ 5

Author(s):

ABDEL-RAHMAN HEDAR ◽

EMAD MABROUK ◽

MASAO FUKUSHIMA

Keyword(s):

Search Algorithm ◽

Benchmark Problems ◽

Problem Solver ◽

Learning Tools ◽

Mutation Operators ◽

Tree Data ◽

Tree Data Structure ◽

Comprehensive Framework ◽

Rate Of Success ◽

Crossover And Mutation

Since the first appearance of the Genetic Programming (GP) algorithm, extensive theoretical and application studies on it have been conducted. Nowadays, the GP algorithm is considered one of the most important tools in Artificial Intelligence (AI). Nevertheless, several questions have been raised about the complexity of the GP algorithm and the disruption effect of the crossover and mutation operators. In this paper, the Tabu Programming (TP) algorithm is proposed to employ the search strategy of the classical Tabu Search algorithm with the tree data structure. Moreover, the TP algorithm exploits a set of local search procedures over a tree space in order to mitigate the drawbacks of the crossover and mutation operators. Extensive numerical experiments are performed to study the performance of the proposed algorithm for a set of benchmark problems. The results of those experiments show that the TP algorithm compares favorably to recent versions of the GP algorithm in terms of computational efforts and the rate of success. Finally, we present a comprehensive framework called Meta-Heuristics Programming (MHP) as general machine learning tools.

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Compressed data structures for bi-objective {0,1}-knapsack problems

Computers & Operations Research ◽

10.1016/j.cor.2017.08.008 ◽

2018 ◽

Vol 89 ◽

pp. 82-93

Author(s):

Pedro Correia ◽

Luís Paquete ◽

José Rui Figueira

Keyword(s):

Data Structures ◽

Knapsack Problems ◽

Compressed Data Structures ◽

Compressed Data

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A Scalable Algorithm for Constructing Frequent Pattern Tree

International Journal of Intelligent Information Technologies ◽

10.4018/ijiit.2014010103 ◽

2014 ◽

Vol 10 (1) ◽

pp. 42-56 ◽

Cited By ~ 3

Author(s):

Zailani Abdullah ◽

Tutut Herawan ◽

A. Noraziah ◽

Mustafa Mat Deris

Keyword(s):

Data Structure ◽

Frequent Pattern ◽

Frequent Patterns ◽

Scalable Algorithm ◽

Tree Construction ◽

Frequent Pattern Tree ◽

Support Threshold ◽

Benchmark Datasets ◽

Tree Data ◽

Tree Data Structure

Frequent Pattern Tree (FP-Tree) is a compact data structure of representing frequent itemsets. The construction of FP-Tree is very important prior to frequent patterns mining. However, there have been too limited efforts specifically focused on constructing FP-Tree data structure beyond from its original database. In typical FP-Tree construction, besides the prior knowledge on support threshold, it also requires two database scans; first to build and sort the frequent patterns and second to build its prefix paths. Thus, twice database scanning is a key and major limitation in completing the construction of FP-Tree. Therefore, this paper suggests scalable Trie Transformation Technique Algorithm (T3A) to convert our predefined tree data structure, Disorder Support Trie Itemset (DOSTrieIT) into FP-Tree. Experiment results through two UCI benchmark datasets show that the proposed T3A generates FP-Tree up to 3 magnitudes faster than that the benchmarked FP-Growth.

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