A new evolutionary algorithm with locally assisted heuristic for complex detection in protein interaction networks

By definition, the detection of protein complexes that form protein-protein interaction networks (PPINs) is an NP-hard problem. Evolutionary algorithms (EAs), as global search methods, are proven in the literature to be more successful than greedy methods in detecting protein complexes. However, the design of most of these EA-based approaches relies on the topological information of the proteins in the PPIN. Biological information, as a key resource for molecular profiles, on the other hand, acquired a little interest in the design of the components in these EA-based methods. The main aim of this paper is to redesign two operators in the EA based on the functional domain rather than the graph topological domain. The perturbation mechanism of both crossover and mutation operators is designed based on the direct gene ontology annotations and Jaccard similarity coefficients for the proteins. The results on yeast Saccharomyces cerevisiae PPIN provide a useful perspective that the functional domain of the proteins, as compared with the topological domain, is more consistent with the true information reported in the Munich Information Center for Protein Sequence (MIPS) catalog. The evaluation at both complex and protein levels reveals that feeding the components of the EA with biological information will imply more accurate complex structures, whereas topological information may mislead the algorithm towards a faulty structure.

“…The setting of the parameters is allowed to match, more or less, the settings used in the literature [8][9][10][11][12], [16], and [17]. The population size is set to 100.…”

Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Biological versus Topological Domains in Improving the Reliability of Evolutionary-Based Protein Complex Detection Algorithms

Abdulateef,

Attea,

Alzubaydi

2024

“…The next effort came in 2018 and 2019 by Abdulateef et al [15,16] to support the claim that the topological-based design for the component(s) of the evolutionary-based complex detection algorithms would hopefully improve their detection ability to hit more correct complex structures. They developed a heuristic mutation operator based on the topological properties of the tested PPIN.…”

Section: Related Workmentioning

confidence: 99%

Topology-Based Modularity and Modularity Density for Detecting Protein Complexes: A Comparative Study

Abdulsahib,

Attea

2024

Binary relations or interactions among bio-entities, such as proteins, set up the essential part of any living biological system. Protein-protein interactions are usually structured in a graph data structure called "protein-protein interaction networks" (PPINs). Analysis of PPINs into complexes tries to lay out the significant knowledge needed to answer many unresolved questions, including how cells are organized and how proteins work. However, complex detection problems fall under the category of non-deterministic polynomial-time hard (NP-Hard) problems due to their computational complexity. To accommodate such combinatorial explosions, evolutionary algorithms (EAs) are proven effective alternatives to heuristics in solving NP-hard problems. The main aim of this study is to make a close examination of the performance of the EAs where modularity and modularity density are selected as two different objective functions. Topology-based modularity and topology-based modularity density are designed to examine the detection ability of the EAs and to compare their performance. To conduct the experiments, two yeast Saccharomyces cerevisiae PPINs are used and evaluated under nine evaluation metrics. The results reveal the potential impact of the topology-based modularity density to outperform the counterpart modularity functions in almost all evaluation metrics.

“…However, the main interest of many metaheuristic-based complex detection algorithms is to formulate their components from topological structure only. For example, a topological-based mutation operator is proposed in [8,9]. The basic idea of the proposed operator is to break up the coexistence of a pair of proteins according to their topological similarity.…”

Section: Metaheuristic Based Complex Detection Algorithms: a Reviewmentioning

confidence: 99%

A Tri-Gene Ontology Migration Operator for Improving the Performance of Meta-heuristics in Complex Detection Problems

Abdulateef

Alzubaydi

Attea

2023

Detecting protein complexes in protein-protein interaction (PPI) networks is a challenging problem in computational biology. To uncover a PPI network into a complex structure, different meta-heuristic algorithms have been proposed in the literature. Unfortunately, many of such methods, including evolutionary algorithms (EAs), are based solely on the topological information of the network rather than on biological information. Despite the effectiveness of EAs over heuristic methods, more inherent biological properties of proteins are rarely investigated and exploited in these approaches. In this paper, we proposed an EA with a new mutation operator for complex detection problems. The proposed mutation operator is formulated under four expressions depending on the type of gene sub-ontology. To demonstrate the performance of the proposed evolutionary based complex detection algorithm, the Saccharomyces Cerevisiae (yeast) PPI network is used in the evaluation. The results reveal that the proposed algorithm achieves more accurate complex structures than the counterpart heuristic algorithms and the canonical evolutionary algorithm based on the topological-aware mutation operator.