AIM:To identify the genetic factors underlying anophthalmia and microphthalmia(A/M),and to perform computational analysis to verify the pathophysiological mechanisms of the disease.METHODS:This study investigated a co...AIM:To identify the genetic factors underlying anophthalmia and microphthalmia(A/M),and to perform computational analysis to verify the pathophysiological mechanisms of the disease.METHODS:This study investigated a consanguineous Pakistani family with multiple affected individuals.Clinical evaluations were conducted using A-Scan and ophthalmic B-Scan ultrasonography(B-Scan).To identify the diseasecausing variant,whole exome sequencing(WES)and Sanger sequencing were performed.In silico functional analyses were carried out using AlphaFold(for protein modeling)and ClusPro(for protein docking analysis)tools,and the hydrodynamic properties of the protein were determined via molecular dynamics simulations.RESULTS:Clinical analysis of the five patients revealed severe phenotypes of bilateral anophthalmia.Ocular B-Scan did not detect ocular tissue or intraocular fluid,thus confirming the diagnosis of anophthalmia in all patients.Due to these structural defects,all patients exhibited complete blindness and absence of light perception;additionally,two patients had mild to moderate intellectual disability.Genetic analysis identified a splice site variant[NM_000693.2:c.884-2_885dup;p.(Asp296SerfsTer35)]in the 9^(th)exon of the ALDH1A3 gene.CONCLUSION:The present study expands the genetic spectrum of ALDH1A3 and contributes to establishing the genotype-phenotype correlation for this gene.展开更多
Privacy preservation is a primary concern in social networks which employ a variety of privacy preservations mechanisms to preserve and protect sensitive user information including age,location,education,interests,and...Privacy preservation is a primary concern in social networks which employ a variety of privacy preservations mechanisms to preserve and protect sensitive user information including age,location,education,interests,and others.The task of matching user identities across different social networks is considered a challenging task.In this work,we propose an algorithm to reveal user identities as a set of linked accounts from different social networks using limited user profile data,i.e,user-name and friendship.Thus,we propose a framework,ExpandUIL,that includes three standalone al-gorithms based on(i)the percolation graph matching in Ex-pand FullName algorithm,(i)a supervised machine learning algorithm that works with the graph embedding,and(ii)a combination of the two,ExpandUserLinkage algorithm.The proposed framework as a set of algorithms is significant as,(i)it is based on the network topology and requires only name feature of the nodes,(i)it requires a considerably low initial seed,as low as one initial seed suffices,(ii)it is iterative and scalable with applicability to online incoming stream graphs,and(iv)it has an experimental proof of stability over a real ground-truth dataset.Experiments on real datasets,Instagram and VK social networks,show upto 75%recall for linked ac-counts with 96%accuracy using only one given seed pair.展开更多
Attributed graphs have an additional sign vector for each node.Typically,edge signs represent like or dislike relationship between the node pairs.This has applications in domains,such as recommender systems,personalis...Attributed graphs have an additional sign vector for each node.Typically,edge signs represent like or dislike relationship between the node pairs.This has applications in domains,such as recommender systems,personalised search,etc.However,limited availability of edge sign information in attributed networks requires inferring the underlying graph embeddings to fill-in the knowledge gap.Such inference is performed by way of node classification which aims to deduce the node characteristics based on the topological structure of the graph and signed interactions between the nodes.The study of attributed networks is challenging due to noise,sparsity,and class imbalance issues.In this work,we consider node centrality in conjunction with edge signs to contemplate the node classification problem in attributed networks.We propose Semi-supervised Node Classification in Attributed graphs(SNCA).SNCA is robust to underlying network noise,and has in-built class imbalance handling capabilities.We perform an extensive experimental study on real-world datasets to showcase the efficiency,scalability,robustness,and pertinence of the solution.The performance results demonstrate the suitability of the solution for large attributed graphs in real-world settings.展开更多
基金Supported by Taif University,Taif,Saudi Arabia(TU-DSPP-2024-05).
文摘AIM:To identify the genetic factors underlying anophthalmia and microphthalmia(A/M),and to perform computational analysis to verify the pathophysiological mechanisms of the disease.METHODS:This study investigated a consanguineous Pakistani family with multiple affected individuals.Clinical evaluations were conducted using A-Scan and ophthalmic B-Scan ultrasonography(B-Scan).To identify the diseasecausing variant,whole exome sequencing(WES)and Sanger sequencing were performed.In silico functional analyses were carried out using AlphaFold(for protein modeling)and ClusPro(for protein docking analysis)tools,and the hydrodynamic properties of the protein were determined via molecular dynamics simulations.RESULTS:Clinical analysis of the five patients revealed severe phenotypes of bilateral anophthalmia.Ocular B-Scan did not detect ocular tissue or intraocular fluid,thus confirming the diagnosis of anophthalmia in all patients.Due to these structural defects,all patients exhibited complete blindness and absence of light perception;additionally,two patients had mild to moderate intellectual disability.Genetic analysis identified a splice site variant[NM_000693.2:c.884-2_885dup;p.(Asp296SerfsTer35)]in the 9^(th)exon of the ALDH1A3 gene.CONCLUSION:The present study expands the genetic spectrum of ALDH1A3 and contributes to establishing the genotype-phenotype correlation for this gene.
文摘Privacy preservation is a primary concern in social networks which employ a variety of privacy preservations mechanisms to preserve and protect sensitive user information including age,location,education,interests,and others.The task of matching user identities across different social networks is considered a challenging task.In this work,we propose an algorithm to reveal user identities as a set of linked accounts from different social networks using limited user profile data,i.e,user-name and friendship.Thus,we propose a framework,ExpandUIL,that includes three standalone al-gorithms based on(i)the percolation graph matching in Ex-pand FullName algorithm,(i)a supervised machine learning algorithm that works with the graph embedding,and(ii)a combination of the two,ExpandUserLinkage algorithm.The proposed framework as a set of algorithms is significant as,(i)it is based on the network topology and requires only name feature of the nodes,(i)it requires a considerably low initial seed,as low as one initial seed suffices,(ii)it is iterative and scalable with applicability to online incoming stream graphs,and(iv)it has an experimental proof of stability over a real ground-truth dataset.Experiments on real datasets,Instagram and VK social networks,show upto 75%recall for linked ac-counts with 96%accuracy using only one given seed pair.
基金supported by the National Key Research and Development Program of China(No.2020YFA0909100).
文摘Attributed graphs have an additional sign vector for each node.Typically,edge signs represent like or dislike relationship between the node pairs.This has applications in domains,such as recommender systems,personalised search,etc.However,limited availability of edge sign information in attributed networks requires inferring the underlying graph embeddings to fill-in the knowledge gap.Such inference is performed by way of node classification which aims to deduce the node characteristics based on the topological structure of the graph and signed interactions between the nodes.The study of attributed networks is challenging due to noise,sparsity,and class imbalance issues.In this work,we consider node centrality in conjunction with edge signs to contemplate the node classification problem in attributed networks.We propose Semi-supervised Node Classification in Attributed graphs(SNCA).SNCA is robust to underlying network noise,and has in-built class imbalance handling capabilities.We perform an extensive experimental study on real-world datasets to showcase the efficiency,scalability,robustness,and pertinence of the solution.The performance results demonstrate the suitability of the solution for large attributed graphs in real-world settings.
