Abstract Drug response prediction is a well-studied problem in which the molecular profile of given sample used to predict effect drug on that sample. Effective solutions this hold key for precision medicine. In cancer research, genomic data from cell lines are often utilized as features develop machine learning models predictive response. Molecular networks provide functional context integration features, thereby resulting robust and reproducible models. However, inclusion network increases...

ABSTRACT Background Link prediction is an important and well-studied problem in network biology. Recently, graph representation learning methods, including Graph Convolutional Network (GCN)-based node embedding have drawn increasing attention link prediction. Motivation An component of GCN-based the convolution matrix, which used to propagate features across network. Existing algorithms use degree-normalized adjacency matrix for this purpose, as closely related Laplacian, capturing spectral...

Network proximity is at the heart of a large class network analytics and information retrieval techniques, including node/ edge rankings, alignment, randomwalk based queries, among many others. Owing to its importance, significant effort has been devoted accelerating iterative processes underlying computations. These techniques rely on numerical properties power iterations, as well structural networks reduce run time algorithms.

Drug response prediction is a well-studied problem in which the molecular profile of given sample used to predict effect drug on that sample. Effective solutions this hold key for precision medicine. In cancer research, genomic data from cell lines are often utilized as features develop machine learning models predictive response. Molecular networks provide functional context integration features, thereby resulting robust and reproducible models. However, inclusion network increases...

Deep Reinforcement Learning has the potential of practically addressing one most pressing problems in road traffic management, namely that light optimization (TLO). The objective TLO problem is to set timings (phase and duration) lights order minimize overall travel time vehicles traverse network. In this paper, we introduce a new reward function able decrease micro-simulator environment. More specifically, our simultaneously takes flow delay into account provide solution problem. We use...

Link prediction is an important and well-studiedproblem in network analysis, with a broad range of applicationsincluding recommender systems, anomaly detection, denoising. The general principle link to use thetopological characteristics the nodes predictedges that might be added or removed from network. While early research utilized local neighborhood tocharacterize topological relationship between pairs nodes, recent studies increasingly show global networkinformation improves performance....

Employees leave an organization when other organizations offer better opportunities than their current organizations. Continuity and sustenance even completion of jobs are crucial issues for the companies not to suffer financial losses. Especially if talented employees, who at critical positions in companies, job, it becomes difficult maintain businesses. Today, would like predict attrition employees plan prepare it. However, HR departments advanced enough make such predictions a handcrafted...

Node proximity queries are among the most common operations on network databases. A measure of node is random walk based proximity, which has been shown to be less susceptible noise and missing data. Real-time processing random-walk poses significant computational challenges for larger graphs with over billions nodes edges, since it involves solution large linear systems equations. Due importance this operation, effort devoted developing efficient methods computations. These either aim speed...

Biomolecular data stored in public databases is increasingly specialized to organisms, context/pathology and tissue type, potentially resulting significant overhead for analyses. These networks are often specializations of generic interaction sets, presenting opportunities reducing storage computational cost. Therefore, it desirable develop effective compression techniques, along with efficient algorithms a flexible query interface capable operating on compressed structures. Current graph...

While software inspection is an effective activity to detect defects early in the development lifecycle, it effort-intensive and error-prone activity. Motivated by a real need context of Turkish Aerospace Industries Inc. (TAI), tool named AutoInspect was developed (semi-) automate design documents and, as result, increase efficiency effectiveness process. We present this paper features tool, its details initial evaluation for inspecting three systems company. The results case-study reveal...

Spectral Graph Convolutional Networks (GCNs) have gained popularity in graph machine learning applications due, part, to their flexibility specification of network propagation rules. These rules are often constructed as polynomial filters whose coefficients learned using label information during training. In contrast filters, explicit filter functions useful capturing relationships between topology and distribution labels across the network. A number algorithms incorporating either approach...

In recent years, Graph Convolutional Networks (GCNs) and their variants have been widely utilized in learning tasks that involve graphs. These include recommendation systems, node classification, among many others. classification problem, the input is a graph which edges represent association between pairs of nodes, multi-dimensional feature vectors are associated with some nodes known labels. The objective to predict labels not labeled, using features, conjunction topology. While GCNs...

Ağ gömülümü öğrenme problemi bir çok ağ analizi gerektiren problemin ifade ve çözümlenmesi için büyük önem arz etmektedir. Bu bağlamda, içerisinde bulunan düğümlerin birbirleri ile olan gizli ilişkilerini açığa çıkarmak için, son yıllarda çokça çalışılmaktadır. ilişkinin çıkarılması, bağlantı tahminleme, öbekleme sınıflandırma gibi öğreme problemlerinin daha iyi çözümlenmesinde kullanılmaktadır. gömülümünü öğrenmek farklı yaklaşım algoritmalar geliştirilmiş olsada, matris ayrışımı bazlı...

Abstract Motivation Link prediction is an important and well-studied problem in computational biology, with a broad range of applications including disease gene prioritization, drug-disease associations, drug response cancer. The general principle link to use the topological characteristics attributes–if available– nodes network predict new links that are likely emerge/disappear. Recently, graph representation learning methods, which aim learn low-dimensional attributes nodes, have drawn...

Fine-grained smile analysis is a complicated and challenging process. Understanding other party's smiles one of the key tasks associated with realizing implicit messages transmitted by human. Considering this kind message transmission major feature human communication, understanding smiling has great potential value to promote development humanoid robots animated software agents. Therefore, fine-grained system proposed uncover hidden correlation across subjects. The incorporates head pose as...

Abstract Machine learning applications on large-scale network-structured data commonly encode network information in the form of node embeddings. Network embedding algorithms map nodes into a low-dimensional space such that are “similar” with respect to topology also close each other space. Real-world networks often have multiple versions or can be “multiplex” types edges different semantics. For networks, computation Consensus Embedding s based embeddings individual useful for various...

Graph or network embedding is a powerful method for extracting missing potential information from interactions between nodes in biological networks. methods learn representations of and graph with low-dimensional vectors, which facilitates research to predict However, most suffer high computational costs the form complexity learning times classifier, as well dimensionality complex To address these challenges, this study, we use Chopper algorithm an alternative approach embedding, accelerates...