paraSNF: An Parallel Approach for Large-Scale Similarity Network Fusion

2018 ◽  
pp. 155-167
Author(s):  
Xiaolong Shen ◽  
Song He ◽  
Minquan Fang ◽  
Yuqi Wen ◽  
Xiaochen Bo ◽  
...  
Keyword(s):  
Large Scale ◽  
Similarity Network ◽  
Scale Similarity

scholarly journals ToxDBScan: Large-Scale Similarity Screening of Toxicological Databases for Drug Candidates

2014 ◽  
Vol 15 (10) ◽  
pp. 19037-19055 ◽  
Author(s):  
Michael Römer ◽  
Linus Backert ◽  
Johannes Eichner ◽  
Andreas Zell
Keyword(s):  
Large Scale ◽  
Drug Candidates ◽  
Scale Similarity

Crustal-scale ramp in a Middle Proterozoic orogen, Northwest Territories, Canada

1992 ◽  
Vol 29 (1) ◽  
pp. 142-157 ◽  
Author(s):  
Elizabeth A. Clark ◽  
Frederick A. Cook
Keyword(s):  
Northwest Territories ◽  
Seismic Data ◽  
Large Scale ◽  
Thrust Faults ◽  
Crustal Structures ◽  
Scale Similarity ◽  
Compressional Deformation ◽  
Middle Proterozoic

Deep crustal seismic data from the Fort Goodhope area, Northwest Territories, Canada, image crustal structures associated with Middle Proterozoic compressional deformation. These include 10–20 km wide antiforms and thrust faults that lie above a west-dipping crustal-scale ramp with at least 10 km of vertical relief. The deformation is interpreted as being associated with structures observed in the subsurface to the east and may be partly coeval with deformation originally detected in outcrop in the Rackla Range of the Wernecke Mountains. These new deep crustal profiles, coupled with data to the east that delineate structures to 15 km depth, reveal large-scale similarity between this Middle Proterozoic orogen and many Phanerozoic compressional orogens.

scholarly journals Multimodal phenotypic axes of Parkinson’s disease

2020 ◽  
Author(s):  
Ross D. Markello ◽  
Golia Shafiei ◽  
Christina Tremblay ◽  
Ronald B. Postuma ◽  
Alain Dagher ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Large Scale ◽  
De Novo ◽  
Patient Characteristics ◽  
Multimodal Fusion ◽  
Compact Set ◽  
Similarity Network ◽  
Mri Morphometry ◽  
Low Dimensional

Individuals with Parkinson’s disease present with a complex clinical phenotype, encompassing sleep, motor, cognitive, and affective disturbances. However, characterizations of PD are typically made for the “average” patient, ignoring patient heterogeneity and obscuring important individual differences. Modern large-scale data sharing efforts provide a unique opportunity to precisely investigate individual patient characteristics, but there exists no analytic framework for comprehensively integrating data modalities. Here we apply an unsupervised learning method—similarity network fusion—to objectively integrate MRI morphometry, dopamine active transporter binding, protein assays, and clinical measurements from n = 186 individuals with de novo Parkinson’s disease from the Parkinson’s Progression Markers Initiative. We show that multimodal fusion captures inter-dependencies among data modalities that would otherwise be overlooked by field standard techniques like data concatenation. We then examine how patient subgroups derived from fused data map onto clinical phenotypes, and how neuroimaging data is critical to this delineation. Finally, we identify a compact set of phenotypic axes that span the patient population, demonstrating that this continuous, low-dimensional projection of individual patients presents a more parsimonious representation of heterogeneity in the sample compared to discrete biotypes. Altogether, these findings showcase the potential of similarity network fusion for combining multimodal data in heterogeneous patient populations.

scholarly journals SPRDA: a matrix completion approach based on the structural perturbation to infer disease-associated Piwi-Interacting RNAs

2020 ◽  
Author(s):  
Kai Zheng ◽  
Zhu-Hong You ◽  
Lei Wang ◽  
Leon Wong ◽  
Zhao-hui Zhan
Keyword(s):  
Perturbation Method ◽  
High Performance ◽  
Large Scale ◽  
Matrix Completion ◽  
Disease Diagnosis ◽  
Diagnosis And Treatment ◽  
Biological Research ◽  
Sequence Information ◽  
Similarity Network ◽  
Structural Perturbation

AbstractEmerging evidence suggests that PIWI-interacting RNAs (piRNAs) are one of the most influential small non-coding RNAs (ncRNAs) that regulate RNA silencing. piRNA and PIWI proteins have been confirmed for disease diagnosis and treatment as novel biomarkers due to its abnormal expression in various cancers. However, the current research is not strong enough to further clarify the functions of piRNA in cancer and its underlying mechanism. Therefore, how to provide large-scale and serious piRNA candidates for biological research has grown up to be a pressing issue. The main motivation of this work is tantamount to fill the gap in research on large-scale prediction of disease-related piRNAs. In this study, a novel computational model based on the structural perturbation method is proposed, called SPRDA. In detail, the duplex network is constructed based on the piRNA similarity network and disease similarity network extracted from piRNA sequence information, Gaussian interaction profile kernel similarity information and gene-disease association information. The structural perturbation method is then used to predict the potential associations on the duplex network, which is more predictive than other network structures in terms of structural consistency. In the five-fold cross-validation, SPRDA shows high performance on the benchmark dataset piRDisease, with an AUC of 0.9529. Furthermore, the predictive performance of SPRDA for 10 diseases shows the robustness of the proposed method. Overall, the proposed approach can provide unique insights into the pathogenesis of the disease and will advance the field of oncology diagnosis and treatment.

Sign in / Sign up

Export Citation Format

Share Document