Link prediction attempts to estimate the likelihood of the existence of links between nodes based on available brain network information, such as node attributes and observed links. In response to the problem of the p...Link prediction attempts to estimate the likelihood of the existence of links between nodes based on available brain network information, such as node attributes and observed links. In response to the problem of the poor efficiency of general link prediction methods applied to brain networks, this paper proposes a hierarchical random graph model based on maximum likelihood estimation. This algorithm uses brain network data to create a hierarchical random graph model. Then, it samples the space of all possible dendrograms using a Markov-chain Monte Carlo algorithm. Finally, it calculates the average connection probability. It also employs an evaluation index. Comparing link prediction in a brain network with link prediction in three different networks (Treponemapallidum metabolic network, terrorist networks, and grassland species food webs) using the hierarchical random graph model, experimental results show that the algorithm applied to the brain network has the highest prediction accuracy in terms of AUC scores. With the increase of network scale, AUC scores of the brain network reach 0.8 before gradually leveling off. In addition, the results show AUC scores of various algorithms computed in networks of eight different scales in 28 normal people. They show that the HRG algorithm is far better than random prediction and the ACT global index, and slightly inferior to local indexes CN and LP. Although the HRG algorithm does not produce the best results, its forecast effect is obvious, and shows good time complexity.展开更多
Let G : Gn,p be a binomial random graph with n vertices and edge probability p = p(n), and f be a nonnegative integer-valued function defined on V(G) such that 0 〈 a ≤ f(x) ≤ b 〈 np- 2√nplogn for every ...Let G : Gn,p be a binomial random graph with n vertices and edge probability p = p(n), and f be a nonnegative integer-valued function defined on V(G) such that 0 〈 a ≤ f(x) ≤ b 〈 np- 2√nplogn for every E V(G). An fractional f-indicator function is an function h that assigns to each edge of a graph G a number h(e) in [0, 1] so that for each vertex x, we have d^hG(x) = f(x), where dh(x) = ∑ h(e) is the fractional degree xEe ofx inG. Set Eh = {e : e e E(G) and h(e) ≠ 0}. IfGh isaspanningsubgraphofGsuchthat E(Gh) = Eh, then Gh is called an fractional f-factor of G. In this paper, we prove that for any binomial random graph Gn,p 2 with p 〉 n^-2/3, almost surely Gn,p contains an fractional f-factor.展开更多
Aims: Steadily the clinicians of our team in inflammatory bowel disease encounter ulcerative colitis patients that develop deep ulcers during their treatment. Currently, these practitioners are only equipped with thei...Aims: Steadily the clinicians of our team in inflammatory bowel disease encounter ulcerative colitis patients that develop deep ulcers during their treatment. Currently, these practitioners are only equipped with their grade of expertise in inflammatory domains to decide what new therapy maybe use in such cases. Encouraged by the limited knowledge of this frequent pathology, we seek to determine the molecular conditions underlying the recurrent formation of deep ulcerations in certain group of patients. Method: The goal of this strategy is to expose differences between groups of patients based on similarities computed by random walk graph kernels and performing functional inference on those differences. Results: We apply the methodology to a cohort of eleven miRNA microarrays of ulcerative colitis patients. Our results showed how the group of ulcerative colitis patients with presence of deep ulcers is topologically more similar (0.35) than ulcerative colitis patients (0.18) to control. Such topological constraint drove functional inference to complete the information that clinicians need. Conclusions: Our analyses reveal highly interpretable in the guidance of practitioners to eventually correct initial therapies of ulcerative colitis patients that develop deep ulcers. The methodology can provide them with useful molecular hypotheses necessaries prior to make any decision on the newest course of the treatment.展开更多
基金financially supported by the National Natural Science Foundation of China (Nos. 61170136, 61373101, 61472270, and 61402318)the Natural Science Foundation of Shanxi (No. 2014021022-5)+1 种基金the Special/Youth Foundation of Taiyuan University of Technology (No. 2012L014)Youth Team Fund of Taiyuan University of Technology (Nos. 2013T047 and 2013T048)
文摘Link prediction attempts to estimate the likelihood of the existence of links between nodes based on available brain network information, such as node attributes and observed links. In response to the problem of the poor efficiency of general link prediction methods applied to brain networks, this paper proposes a hierarchical random graph model based on maximum likelihood estimation. This algorithm uses brain network data to create a hierarchical random graph model. Then, it samples the space of all possible dendrograms using a Markov-chain Monte Carlo algorithm. Finally, it calculates the average connection probability. It also employs an evaluation index. Comparing link prediction in a brain network with link prediction in three different networks (Treponemapallidum metabolic network, terrorist networks, and grassland species food webs) using the hierarchical random graph model, experimental results show that the algorithm applied to the brain network has the highest prediction accuracy in terms of AUC scores. With the increase of network scale, AUC scores of the brain network reach 0.8 before gradually leveling off. In addition, the results show AUC scores of various algorithms computed in networks of eight different scales in 28 normal people. They show that the HRG algorithm is far better than random prediction and the ACT global index, and slightly inferior to local indexes CN and LP. Although the HRG algorithm does not produce the best results, its forecast effect is obvious, and shows good time complexity.
基金Supported by NSFSD(No.ZR2013AM001)NSFC(No.11001055),NSFC11371355
文摘Let G : Gn,p be a binomial random graph with n vertices and edge probability p = p(n), and f be a nonnegative integer-valued function defined on V(G) such that 0 〈 a ≤ f(x) ≤ b 〈 np- 2√nplogn for every E V(G). An fractional f-indicator function is an function h that assigns to each edge of a graph G a number h(e) in [0, 1] so that for each vertex x, we have d^hG(x) = f(x), where dh(x) = ∑ h(e) is the fractional degree xEe ofx inG. Set Eh = {e : e e E(G) and h(e) ≠ 0}. IfGh isaspanningsubgraphofGsuchthat E(Gh) = Eh, then Gh is called an fractional f-factor of G. In this paper, we prove that for any binomial random graph Gn,p 2 with p 〉 n^-2/3, almost surely Gn,p contains an fractional f-factor.
文摘Aims: Steadily the clinicians of our team in inflammatory bowel disease encounter ulcerative colitis patients that develop deep ulcers during their treatment. Currently, these practitioners are only equipped with their grade of expertise in inflammatory domains to decide what new therapy maybe use in such cases. Encouraged by the limited knowledge of this frequent pathology, we seek to determine the molecular conditions underlying the recurrent formation of deep ulcerations in certain group of patients. Method: The goal of this strategy is to expose differences between groups of patients based on similarities computed by random walk graph kernels and performing functional inference on those differences. Results: We apply the methodology to a cohort of eleven miRNA microarrays of ulcerative colitis patients. Our results showed how the group of ulcerative colitis patients with presence of deep ulcers is topologically more similar (0.35) than ulcerative colitis patients (0.18) to control. Such topological constraint drove functional inference to complete the information that clinicians need. Conclusions: Our analyses reveal highly interpretable in the guidance of practitioners to eventually correct initial therapies of ulcerative colitis patients that develop deep ulcers. The methodology can provide them with useful molecular hypotheses necessaries prior to make any decision on the newest course of the treatment.