Selection of important and related variables in omics data with surrogate minimal depth (SMD)

2020 ◽  
Vol 74 (S2) ◽  
Author(s):  
S. Seifert ◽  
S. Gundlach ◽  
S. Szymczak
Keyword(s):  
Omics Data ◽  
Minimal Depth ◽  
Selection Of

scholarly journals Leveraging network analysis to evaluate biomedical named entity recognition tools

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Eduardo P. García del Valle ◽  
Gerardo Lagunes García ◽  
Lucía Prieto Santamaría ◽  
Massimiliano Zanin ◽  
Ernestina Menasalvas Ruiz ◽  
...  
Keyword(s):  
Network Analysis ◽  
Named Entity Recognition ◽  
Quality Criteria ◽  
Entity Recognition ◽  
Biomedical Text ◽  
Omics Data ◽  
Named Entity ◽  
Novel Approaches ◽  
Biomedical Named Entity Recognition ◽  
Selection Of

AbstractThe ever-growing availability of biomedical text sources has resulted in a boost in clinical studies based on their exploitation. Biomedical named-entity recognition (bio-NER) techniques have evolved remarkably in recent years and their application in research is increasingly successful. Still, the disparity of tools and the limited available validation resources are barriers preventing a wider diffusion, especially within clinical practice. We here propose the use of omics data and network analysis as an alternative for the assessment of bio-NER tools. Specifically, our method introduces quality criteria based on edge overlap and community detection. The application of these criteria to four bio-NER solutions yielded comparable results to strategies based on annotated corpora, without suffering from their limitations. Our approach can constitute a guide both for the selection of the best bio-NER tool given a specific task, and for the creation and validation of novel approaches.

scholarly journals DSCN: double-target selection guided by CRISPR screening and network

2021 ◽  
Author(s):  
Enze Liu ◽  
Xue Wu ◽  
Lei Wang ◽  
Yang Huo ◽  
Huanmei Wu ◽  
...  
Keyword(s):  
Cell Lines ◽  
Drug Combinations ◽  
Superior Performance ◽  
Cancer Therapies ◽  
Omics Data ◽  
Ppi Network ◽  
Pancreatic Cell ◽  
Single Target ◽  
Target Combination ◽  
Selection Of

AbstractCancer is a complex disease with usually multiple disease mechanisms. Target combination is a better strategy than a single target in developing cancer therapies. However, target combinations are generally more difficult to be predicted. Current CRISPR-cas9 technology enables genome-wide screening for potential targets, but only a handful of genes have been screend as target combinations. Thus, an effective computational approach for selecting candidate target combinations is highly desirable. Selected target combinations also need to be translational between cell lines and cancer patients.We have therefore developed DSCN (double-target selection guided by CRISPR screening and network), a method that matches expression levels in patients and gene essentialities in cell lines through spectral-clustered protein-protein interaction (PPI) network. In DSCN, a sub-sampling approach is developed to model first-target knockdown and its impact on the PPI network, and it also facilitates the selection of a second target. Our analysis first demonstrated high correlation of the DSCN sub-sampling-based gene knockdown model and its predicted differential gene expressions using observed gene expression in 22 pancreatic cell lines before and after MAP2K1 and MAP2K2 inhibition (R2 = 0.75). In our DSCN algorithm, various scoring schemes were evaluated. The ‘diffusion-path’ method showed the most significant statistical power of differentialting known synthetic lethal (SL) versus non-SL gene pairs (P = 0.001) in pancreatic cancer. The superior performance of DSCN over existing network-based algorithms, such as OptiCon[1] and VIPER[2], in the selection of target combinations is attributable to its ability to calculate combinations for any gene pairs, whereas other approaches focus on the combinations among optimized regulators in the network. DSCN’s computational speed is also at least ten times faster than that of other methods. Finally, in applying DSCN to predict target combinations and drug combinations for individual samples (DSCNi), we showed high correlation of DSCNi predicted target combinations with synergistic drug combinations (P = 1e-5) in pancreatic cell lines. In summary, DSCN is a highly effective computational method for the selection of target combinations.Author SummaryCancer therapies require targets to function. Compared to single target, target combination is a better strategy for developing cancer therapies. However, predicting target combination is much complicated than predicting single target. Current CRISPR technology enables whole genome screening of potential targets. But most of the experiments have been conducted on single target (gene) level. To facilitate the prediction of target combinations, we developed DSCN (double-target selection guided by CRISPR screening and network) that utilize single target-level CRISPR screening data and expression profiles for predicting target combinations by connecting cell-line omics-data with tissue omics-data. DSCN showed great accuracy on different cancer types and superior performance compared to existing network-based prediction tools. We also introduced DSCNi derived from DSCN and designed specific for predicting target combinations for single-paitent. We showed synergistic target combinations predicted by DSCNi accurately reflected synergies on drug combination levels. Thus, DSCN and DSCNi have the potential be further applied in personalized medicine field.

scholarly journals Novel feature selection via kernel tensor decomposition for improved multi-omics data analysis

2021 ◽  
Author(s):  
Y-h. Taguchi ◽  
Turki Turki
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Data Analysis ◽  
Tensor Decomposition ◽  
Processing Unit ◽  
Omics Data ◽  
Central Processing ◽  
Unsupervised Feature Extraction ◽  
Selection Of ◽  
Omics Data Analysis

Motivation: Feature selection of multi-omics data analysis remains challenging since omics data include 102-105 features. How to weight an individual omics dataset is unclear and greatly affects feature selection consequences. In this study, a recently proposed kernel tensor decomposition (KTD)-based unsupervised feature extraction (FE) was extended to integrate multi-omics datasets measured over common samples in a weight-free manner. Results: KTD-based unsupervised FE was reformatted as the collection of kernelized tensors sharing common samples and was applied to synthetic, as well as real, datasets. The proposed advanced KTD-based unsupervised FE performed comparatively with the previously proposed KTD, as well as TD-based unsupervised FE, with reduced memory and central processing unit time. This advanced KTD method, specifically designed for multi-omics analysis, attributes P-values to features, which other multi-omics-oriented methods rarely do. Availability: Sample R code is available in https://github.com/tagtag/MultiR/

scholarly journals Set Covering-based Feature Selection of Large-scale Omics Data

2014 ◽  
Vol 39 (4) ◽  
pp. 75-84
Author(s):  
Zhengyu Ma ◽  
Kedong Yan ◽  
Kwangsoo Kim ◽  
Hong Seo Ryoo
Keyword(s):  
Feature Selection ◽  
Large Scale ◽  
Set Covering ◽  
Omics Data ◽  
Selection Of

Cognitive gadgets and cognitive priors

2019 ◽  
Vol 42 ◽  
Author(s):  
Gian Domenico Iannetti ◽  
Giorgio Vallortigara
Keyword(s):  
Cognitive Neuroscience ◽  
Selection Of

Abstract Some of the foundations of Heyes’ radical reasoning seem to be based on a fractional selection of available evidence. Using an ethological perspective, we argue against Heyes’ rapid dismissal of innate cognitive instincts. Heyes’ use of fMRI studies of literacy to claim that culture assembles pieces of mental technology seems an example of incorrect reverse inferences and overlap theories pervasive in cognitive neuroscience.

Note on Selection of Coordinate Systems for Geodynamics

1975 ◽  
Vol 26 ◽  
pp. 395-407
Author(s):  
S. Henriksen
Keyword(s):  
Sea Level ◽  
The Other ◽  
Coordinate Systems ◽  
Mean Sea Level ◽  
The Earth ◽  
The One ◽  
Static Systems ◽  
Selection Of

The first question to be answered, in seeking coordinate systems for geodynamics, is: what is geodynamics? The answer is, of course, that geodynamics is that part of geophysics which is concerned with movements of the Earth, as opposed to geostatics which is the physics of the stationary Earth. But as far as we know, there is no stationary Earth – epur sic monere. So geodynamics is actually coextensive with geophysics, and coordinate systems suitable for the one should be suitable for the other. At the present time, there are not many coordinate systems, if any, that can be identified with a static Earth. Certainly the only coordinate of aeronomic (atmospheric) interest is the height, and this is usually either as geodynamic height or as pressure. In oceanology, the most important coordinate is depth, and this, like heights in the atmosphere, is expressed as metric depth from mean sea level, as geodynamic depth, or as pressure. Only for the earth do we find “static” systems in use, ana even here there is real question as to whether the systems are dynamic or static. So it would seem that our answer to the question, of what kind, of coordinate systems are we seeking, must be that we are looking for the same systems as are used in geophysics, and these systems are dynamic in nature already – that is, their definition involvestime.

Measurements of the Cape Astrometric Survey of the Southern Hemisphere

1978 ◽  
Vol 48 ◽  
pp. 515-521
Author(s):  
W. Nicholson
Keyword(s):  
Southern Hemisphere ◽  
Automatic Measuring ◽  
Measuring Machine ◽  
Final Selection ◽  
Selection Of

SummaryA routine has been developed for the processing of the 5820 plates of the survey. The plates are measured on the automatic measuring machine, GALAXY, and the measures are subsequently processed by computer, to edit and then refer them to the SAO catalogue. A start has been made on measuring the plates, but the final selection of stars to be made is still a matter for discussion.

Application of Improved Voltage Compensation in the SEM to Imaging of Organic Materials

1973 ◽  
Vol 31 ◽  
pp. 444-445
Author(s):  
P.J. Killingworth ◽  
M. Warren
Keyword(s):  
Electron Beam ◽  
Organic Materials ◽  
Operating Parameters ◽  
Low Density ◽  
Beam Diameter ◽  
Incident Electron Beam ◽  
Specimen Material ◽  
Voltage Compensation ◽  
Selection Of ◽  
Scanning Electron

Ultimate resolution in the scanning electron microscope is determined not only by the diameter of the incident electron beam, but by interaction of that beam with the specimen material. Generally, while minimum beam diameter diminishes with increasing voltage, due to the reduced effect of aberration component and magnetic interference, the excited volume within the sample increases with electron energy. Thus, for any given material and imaging signal, there is an optimum volt age to achieve best resolution.In the case of organic materials, which are in general of low density and electric ally non-conducting; and may in addition be susceptible to radiation and heat damage, the selection of correct operating parameters is extremely critical and is achiev ed by interative adjustment.

Selection and Preparation of Specific Tissue Regions For Tem Using Large Epoxy-Embedded Blocks

1973 ◽  
Vol 31 ◽  
pp. 324-325 ◽  
Author(s):  
P. M. Lowrie ◽  
W. S. Tyler
Keyword(s):  
Electron Microscopy ◽  
Anatomical Structures ◽  
Ultrastructural Studies ◽  
Transmission Electron ◽  
Microscopic Evaluation ◽  
Embedded Blocks ◽  
Specific Tissue ◽  
Definition Of ◽  
Areas Of Interest ◽  
Selection Of

The importance of examining stained 1 to 2μ plastic sections by light microscopy has long been recognized, both for increased definition of many histologic features and for selection of specimen samples to be used in ultrastructural studies. Selection of specimens with specific orien ation relative to anatomical structures becomes of critical importance in ultrastructural investigations of organs such as the lung. The uantity of blocks necessary to locate special areas of interest by random sampling is large, however, and the method is lacking in precision. Several methods have been described for selection of specific areas for electron microscopy using light microscopic evaluation of paraffin, epoxy-infiltrated, or epoxy-embedded large blocks from which thick sections were cut. Selected areas from these thick sections were subsequently removed and re-embedded or attached to blank precasted blocks and resectioned for transmission electron microscopy (TEM).

The DQE of an electron image recording system

1978 ◽  
Vol 36 (1) ◽  
pp. 100-101
Author(s):  
K.-H. Herrmann ◽  
D. Krahl ◽  
H.-P Rust
Keyword(s):  
Primary Electron ◽  
Ideal Case ◽  
Image Recording ◽  
Main Requirement ◽  
High Detection ◽  
Electron Image ◽  
Video Amplifier ◽  
Detection Quantum Efficiency ◽  
Selection Of ◽  
The Ideal

The high detection quantum efficiency (DQE) is the main requirement for an imagerecording system used in electron microscopy of radiation-sensitive specimens. An electronic TV system of the type shown in Fig. 1 fulfills these conditions and can be used for either analog or digital image storage and processing [1], Several sources of noise may reduce the DQE, and therefore a careful selection of various elements is imperative.The noise of target and of video amplifier can be neglected when the converter stages produce sufficient target electrons per incident primary electron. The required gain depends on the type of the tube and also on the type of the signal processing chosen. For EBS tubes, for example, it exceeds 10. The ideal case, in which all impinging electrons create uniform charge peaks at the target, is not obtainable for several reasons, and these will be discussed as they relate to a system with a scintillator, fiber-optic and photo-cathode combination as the first stage.

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