60 research outputs found
Frequent Pattern Finding in Integrated Biological Networks
Biomedical research is undergoing a revolution with the advance of high-throughput technologies. A major challenge in the post-genomic era is to understand how genes, proteins and small molecules are organized into signaling pathways and regulatory networks. To simplify the analysis of large complex molecular networks, strategies are sought to break them down into small yet relatively independent network modules, e.g. pathways and protein complexes.
In fulfillment of the motivation to find evolutionary origins of network modules, a novel strategy has been developed to uncover duplicated pathways and protein complexes. This search was first formulated into a computational problem which finds frequent patterns in integrated graphs. The whole framework was then successfully implemented as the software package BLUNT, which includes a parallelized version.
To evaluate the biological significance of the work, several large datasets were chosen, with each dataset targeting a different biological question. An application of BLUNT was performed on the yeast protein-protein interaction network, which is described. A large number of frequent patterns were discovered and predicted to be duplicated pathways. To explore how these pathways may have diverged since duplication, the differential regulation of duplicated pathways was studied at the transcriptional level, both in terms of time and location.
As demonstrated, this algorithm can be used as new data mining tool for large scale biological data in general. It also provides a novel strategy to study the evolution of pathways and protein complexes in a systematic way. Understanding how pathways and protein complexes evolve will greatly benefit the fundamentals of biomedical research
Multilevel Combinatorial Optimization Across Quantum Architectures
Emerging quantum processors provide an opportunity to explore new approaches for solving traditional problems in the Post Moore\u27s law supercomputing era. However, the limited number of qubits makes it infeasible to tackle massive real-world datasets directly in the near future, leading to new challenges in utilizing these quantum processors for practical purposes. Hybrid quantum-classical algorithms that leverage both quantum and classical types of devices are considered as one of the main strategies to apply quantum computing to large-scale problems. In this paper, we advocate the use of multilevel frameworks for combinatorial optimization as a promising general paradigm for designing hybrid quantum-classical algorithms. In order to demonstrate this approach, we apply this method to two well-known combinatorial optimization problems, namely, the Graph Partitioning Problem, and the Community Detection Problem. We develop hybrid multilevel solvers with quantum local search on D-Wave\u27s quantum annealer and IBM\u27s gate-model based quantum processor. We carry out experiments on graphs that are orders of magnitudes larger than the current quantum hardware size and observe results comparable to state-of-the-art solvers
Clustering and Community Detection in Directed Networks: A Survey
Networks (or graphs) appear as dominant structures in diverse domains,
including sociology, biology, neuroscience and computer science. In most of the
aforementioned cases graphs are directed - in the sense that there is
directionality on the edges, making the semantics of the edges non symmetric.
An interesting feature that real networks present is the clustering or
community structure property, under which the graph topology is organized into
modules commonly called communities or clusters. The essence here is that nodes
of the same community are highly similar while on the contrary, nodes across
communities present low similarity. Revealing the underlying community
structure of directed complex networks has become a crucial and
interdisciplinary topic with a plethora of applications. Therefore, naturally
there is a recent wealth of research production in the area of mining directed
graphs - with clustering being the primary method and tool for community
detection and evaluation. The goal of this paper is to offer an in-depth review
of the methods presented so far for clustering directed networks along with the
relevant necessary methodological background and also related applications. The
survey commences by offering a concise review of the fundamental concepts and
methodological base on which graph clustering algorithms capitalize on. Then we
present the relevant work along two orthogonal classifications. The first one
is mostly concerned with the methodological principles of the clustering
algorithms, while the second one approaches the methods from the viewpoint
regarding the properties of a good cluster in a directed network. Further, we
present methods and metrics for evaluating graph clustering results,
demonstrate interesting application domains and provide promising future
research directions.Comment: 86 pages, 17 figures. Physics Reports Journal (To Appear
On the Origin of Biomolecular Networks
Biomolecular networks have already found great utility in characterizing complex biological systems arising from pairwise interactions amongst biomolecules. Here, we explore the important and hitherto neglected role of information asymmetry in the genesis and evolution of such pairwise biomolecular interactions. Information asymmetry between sender and receiver genes is identified as a key feature distinguishing early biochemical reactions from abiotic chemistry, and a driver of network topology as biomolecular systems become more complex. In this context, we review how graph theoretical approaches can be applied not only for a better understanding of various proximate (mechanistic) relations, but also, ultimate (evolutionary) structures encoded in such networks from among all types of variations they induce. Among many possible variations, we emphasize particularly the essential role of gene duplication in terms of signaling game theory, whereby sender and receiver gene players accrue benefit from gene duplication, leading to a preferential attachment mode of network growth. The study of the resulting dynamics suggests many mathematical/computational problems, the majority of which are intractable yet yield to efficient approximation algorithms, when studied through an algebraic graph theoretic lens. We relegate for future work the role of other possible generalizations, additionally involving horizontal gene transfer, sexual recombination, endo-symbiosis, etc., which enrich the underlying graph theory even further
An Enhanced Multi-Objective Biogeography-Based Optimization Algorithm for Automatic Detection of Overlapping Communities in a Social Network with Node Attributes
Community detection is one of the most important and interesting issues in
social network analysis. In recent years, simultaneous considering of nodes'
attributes and topological structures of social networks in the process of
community detection has attracted the attentions of many scholars, and this
consideration has been recently used in some community detection methods to
increase their efficiencies and to enhance their performances in finding
meaningful and relevant communities. But the problem is that most of these
methods tend to find non-overlapping communities, while many real-world
networks include communities that often overlap to some extent. In order to
solve this problem, an evolutionary algorithm called MOBBO-OCD, which is based
on multi-objective biogeography-based optimization (BBO), is proposed in this
paper to automatically find overlapping communities in a social network with
node attributes with synchronously considering the density of connections and
the similarity of nodes' attributes in the network. In MOBBO-OCD, an extended
locus-based adjacency representation called OLAR is introduced to encode and
decode overlapping communities. Based on OLAR, a rank-based migration operator
along with a novel two-phase mutation strategy and a new double-point crossover
are used in the evolution process of MOBBO-OCD to effectively lead the
population into the evolution path. In order to assess the performance of
MOBBO-OCD, a new metric called alpha_SAEM is proposed in this paper, which is
able to evaluate the goodness of both overlapping and non-overlapping
partitions with considering the two aspects of node attributes and linkage
structure. Quantitative evaluations reveal that MOBBO-OCD achieves favorable
results which are quite superior to the results of 15 relevant community
detection algorithms in the literature
A Survey on Influence Maximization: From an ML-Based Combinatorial Optimization
Influence Maximization (IM) is a classical combinatorial optimization
problem, which can be widely used in mobile networks, social computing, and
recommendation systems. It aims at selecting a small number of users such that
maximizing the influence spread across the online social network. Because of
its potential commercial and academic value, there are a lot of researchers
focusing on studying the IM problem from different perspectives. The main
challenge comes from the NP-hardness of the IM problem and \#P-hardness of
estimating the influence spread, thus traditional algorithms for overcoming
them can be categorized into two classes: heuristic algorithms and
approximation algorithms. However, there is no theoretical guarantee for
heuristic algorithms, and the theoretical design is close to the limit.
Therefore, it is almost impossible to further optimize and improve their
performance. With the rapid development of artificial intelligence, the
technology based on Machine Learning (ML) has achieved remarkable achievements
in many fields. In view of this, in recent years, a number of new methods have
emerged to solve combinatorial optimization problems by using ML-based
techniques. These methods have the advantages of fast solving speed and strong
generalization ability to unknown graphs, which provide a brand-new direction
for solving combinatorial optimization problems. Therefore, we abandon the
traditional algorithms based on iterative search and review the recent
development of ML-based methods, especially Deep Reinforcement Learning, to
solve the IM problem and other variants in social networks. We focus on
summarizing the relevant background knowledge, basic principles, common
methods, and applied research. Finally, the challenges that need to be solved
urgently in future IM research are pointed out.Comment: 45 page
Multilevel Combinatorial Optimization Across Quantum Architectures
Emerging quantum processors provide an opportunity to explore new approaches
for solving traditional problems in the post Moore's law supercomputing era.
However, the limited number of qubits makes it infeasible to tackle massive
real-world datasets directly in the near future, leading to new challenges in
utilizing these quantum processors for practical purposes. Hybrid
quantum-classical algorithms that leverage both quantum and classical types of
devices are considered as one of the main strategies to apply quantum computing
to large-scale problems. In this paper, we advocate the use of multilevel
frameworks for combinatorial optimization as a promising general paradigm for
designing hybrid quantum-classical algorithms. In order to demonstrate this
approach, we apply this method to two well-known combinatorial optimization
problems, namely, the Graph Partitioning Problem, and the Community Detection
Problem. We develop hybrid multilevel solvers with quantum local search on
D-Wave's quantum annealer and IBM's gate-model based quantum processor. We
carry out experiments on graphs that are orders of magnitudes larger than the
current quantum hardware size, and we observe results comparable to
state-of-the-art solvers in terms of quality of the solution
REAL-TIME DATA MINING FOR PROCESS OPERATIONS USING GRAPHICS PROCESSING UNIT (GPU)-BASED HIGH PERFORMANCE COMPUTING
Ph.DDOCTOR OF PHILOSOPH
- …