Ant Colony Optimization for Prediction of Compound-Protein Interactions

The prediction of Compound-Protein Interactions (CPI) is an essential step in drug-target analysis for developing new drugs. Therefore, it needs a good incentive to develop a faster and more effective method to predicting the interaction between compound and protein. Predicting the unobserved link of CPI can be done with Ant Colony Optimization for Link Prediction (ACO_LP) algorithms. Each ant selects its path according to the pheromone value and the heuristic information in the link. The path passed by the ant is evaluated and the pheromone information on each link is updated according to the quality of the path. The pheromones on each link are used as the final value of similarity between nodes. The ACO_LP are tested on benchmark CPI data: Nuclear Receptor, G-Protein Coupled Receptor (GPCR), Ion Channel, and Enzyme. Result show that the accuracy values for Nuclear Receptor, GPCR, Ion Channel, and Enzyme dataset are 0.62, 0.62, 0.74, and 0.79 respectively. The results indicate that ACO_LP has good accuracy for prediction of CPI.

Breaking the cipher: ant eavesdropping on the variational trail pheromone of its termite prey

Predators may eavesdrop on their prey using innate signals of varying nature. In regards to social prey, most of the prey signals are derived from social communication and may therefore be highly complex. The most efficient predators select signals that provide the highest benefits. Here, we showed the use of eusocial prey signals by the termite-raiding ant Odontoponera transversa . O. transversa selected the trail pheromone of termites as kairomone in several species of fungus-growing termites (Termitidae: Macrotermitinae: Odontotermes yunnanensis , Macrotermes yunnanensis , Ancistrotermes dimorphus ). The most commonly predated termite, O. yunnanensis, was able to regulate the trail pheromone component ratios during its foraging activity. The ratio of the two trail pheromone compounds was correlated with the number of termites in the foraging party. (3 Z )-Dodec-3-en-1-ol (DOE) was the dominant trail pheromone component in the initial foraging stages when fewer termites were present. Once a trail was established, (3 Z,6Z )-dodeca-3,6-dien-1-ol (DDE) became the major recruitment component in the trail pheromone and enabled mass recruitment of nest-mates to the food source. Although the ants could perceive both components, they revealed stronger behavioural responses to the recruitment component, DDE, than to the common major component, DOE. In other words, the ants use the trail pheromone information as an indication of suitable prey abundance, and regulate their behavioural responses based on the changing trail pheromone component. The eavesdropping behaviour in ants therefore leads to an arms race between predator and prey where the species specific production of trail pheromones in termites is targeted by predatory ant species.

Integrated action of pheromone signals in promoting courtship behavior in male mice

The mammalian vomeronasal organ encodes pheromone information about gender, reproductive status, genetic background and individual differences. It remains unknown how pheromone information interacts to trigger innate behaviors. In this study, we identify vomeronasal receptors responsible for detecting female pheromones. A sub-group of V1re clade members recognizes gender-identifying cues in female urine. Multiple members of the V1rj clade are cognate receptors for urinary estrus signals, as well as for sulfated estrogen (SE) compounds. In both cases, the same cue activates multiple homologous receptors, suggesting redundancy in encoding female pheromone cues. Neither gender-specific cues nor SEs alone are sufficient to promote courtship behavior in male mice, whereas robust courtship behavior can be induced when the two cues are applied together. Thus, integrated action of different female cues is required in pheromone-triggered mating behavior. These results suggest a gating mechanism in the vomeronasal circuit in promoting specific innate behavior.

An approximated Snake Function for Road Extraction from digital images

This paper proposes an optimized mathematical model (Snake-ant) for linear feature extraction from satellite images. The model first uses the Ant Colony Optimization (ACO) to establish a pheromone matrix that represents the pheromone information at each pixel position of the image, according to the movements of a number of ants which are sent to move on the image. Next pheromone matrix is used in the snake model as external energy to extract the linear features like roads edges in image. Snake is a parametric curve which is allowed to deform from some arbitrary initial location toward the desired final location by minimizing an energy function based on the internal and external energy. Our approach is validated by a series of tests on satellite images.

Minimum Cost Multicast Routing Using Ant Colony Optimization Algorithm

Multicast routing (MR) is a technology for delivering network data from some source node(s) to a group of destination nodes. The objective of the minimum cost MR (MCMR) problem is to find an optimal multicast tree with the minimum cost for MR. This problem is NP complete. In order to tackle the problem, this paper proposes a novel algorithm termed the minimum cost multicast routing ant colony optimization (MCMRACO). Based on the ant colony optimization (ACO) framework, the artificial ants in the proposed algorithm use a probabilistic greedy realization of Prim’s algorithm to construct multicast trees. Moving in a cost complete graph (CCG) of the network topology, the ants build solutions according to the heuristic and pheromone information. The heuristic information represents problem-specific knowledge for the ants to construct solutions. The pheromone update mechanisms coordinate the ants’ activities by modulating the pheromones. The algorithm can quickly respond to the changes of multicast nodes in a dynamic MR environment. The performance of the proposed algorithm has been compared with published results available in the literature. Results show that the proposed algorithm performs well in both static and dynamic MCMR problems.

Ant-Inspired Visual Saliency Detection in Image

Visual saliency detection aims to produce saliency map of images via simulating the behavior of the human visual system (HVS). An ant-inspired approach is proposed in this chapter. The proposed approach is inspired by the ant’s behavior to find the most saliency regions in image, by depositing the pheromone information (through ant’s movements) on the image to measure its saliency. Furthermore, the ant’s movements are steered by the local phase coherence of the image. Experimental results are presented to demonstrate the superior performance of the proposed approach.