scholarly journals hcga: Highly Comparative Graph Analysis for network phenotyping

2020 ◽  
Author(s):  
Robert L. Peach ◽  
Alexis Arnaudon ◽  
Julia A. Schmidt ◽  
Henry A. Palasciano ◽  
Nathan R. Bernier ◽  
...  
Keyword(s):  
Statistical Learning ◽  
Organic Semiconductors ◽  
Scientific Discovery ◽  
Theoretical Research ◽  
Data Sets ◽  
Regression Problem ◽  
Data Set ◽  
Graph Data ◽  
Open Platform ◽  
Graph Properties

AbstractNetworks are widely used as mathematical models of complex systems across many scientific disciplines, not only in biology and medicine but also in the social sciences, physics, computing and engineering. Decades of work have produced a vast corpus of research characterising the topological, combinatorial, statistical and spectral properties of graphs. Each graph property can be thought of as a feature that captures important (and some times overlapping) characteristics of a network. In the analysis of real-world graphs, it is crucial to integrate systematically a large number of diverse graph features in order to characterise and classify networks, as well as to aid network-based scientific discovery. In this paper, we introduce HCGA, a framework for highly comparative analysis of graph data sets that computes several thousands of graph features from any given network. HCGA also offers a suite of statistical learning and data analysis tools for automated identification and selection of important and interpretable features underpinning the characterisation of graph data sets. We show that HCGA outperforms other methodologies on supervised classification tasks on benchmark data sets whilst retaining the interpretability of network features. We also illustrate how HCGA can be used for network-based discovery through two examples where data is naturally represented as graphs: the clustering of a data set of images of neuronal morphologies, and a regression problem to predict charge transfer in organic semiconductors based on their structure. HCGA is an open platform that can be expanded to include further graph properties and statistical learning tools to allow researchers to leverage the wide breadth of graph-theoretical research to quantitatively analyse and draw insights from network data.

scholarly journals Graph representation learning: a survey

2020 ◽  
Vol 9 ◽  
Author(s):  
Fenxiao Chen ◽  
Yun-Cheng Wang ◽  
Bin Wang ◽  
C.-C. Jay Kuo
Keyword(s):  
Graph Embedding ◽  
Large Data ◽  
Representation Learning ◽  
Graph Representation ◽  
Data Sets ◽  
Graph Data ◽  
Graph Properties ◽  
Wide Range ◽  
Regular Lattices ◽  
Low Dimensional

Abstract Research on graph representation learning has received great attention in recent years since most data in real-world applications come in the form of graphs. High-dimensional graph data are often in irregular forms. They are more difficult to analyze than image/video/audio data defined on regular lattices. Various graph embedding techniques have been developed to convert the raw graph data into a low-dimensional vector representation while preserving the intrinsic graph properties. In this review, we first explain the graph embedding task and its challenges. Next, we review a wide range of graph embedding techniques with insights. Then, we evaluate several stat-of-the-art methods against small and large data sets and compare their performance. Finally, potential applications and future directions are presented.

scholarly journals Analysis of High-Throughput Flow Cytometry Data Using plateCore

2009 ◽  
Vol 2009 ◽  
pp. 1-10 ◽  
Author(s):  
Errol Strain ◽  
Florian Hahne ◽  
Ryan R. Brinkman ◽  
Perry Haaland
Keyword(s):  
Flow Cytometry ◽  
High Throughput ◽  
Negative Control ◽  
Data Sets ◽  
Data Set ◽  
Flow Cytometry Data ◽  
Open Platform ◽  
Screening Experiments ◽  
Software Packages ◽  
Good Agreement

Flow cytometry (FCM) software packages from R/Bioconductor, such as flowCore and flowViz, serve as an open platform for development of new analysis tools and methods. We created plateCore, a new package that extends the functionality in these core packages to enable automated negative control-based gating and make the processing and analysis of plate-based data sets from high-throughput FCM screening experiments easier. plateCore was used to analyze data from a BD FACS CAP screening experiment where five Peripheral Blood Mononucleocyte Cell (PBMC) samples were assayed for 189 different human cell surface markers. This same data set was also manually analyzed by a cytometry expert using the FlowJo data analysis software package (TreeStar, USA). We show that the expression values for markers characterized using the automated approach in plateCore are in good agreement with those from FlowJo, and that using plateCore allows for more reproducible analyses of FCM screening data.

scholarly journals Optimisation of Point-Set Matching Model for Robust Fingerprint Verification in Changing Weather Conditions

2019 ◽  
pp. 1-9
Author(s):  
I. J. Udo ◽  
B. I. Akhigbe ◽  
B. S. Afolabi
Keyword(s):  
Environmental Conditions ◽  
Weather Conditions ◽  
Theoretical Research ◽  
Data Sets ◽  
Linear Regression Models ◽  
Matching Model ◽  
Fingerprint Verification ◽  
Data Set ◽  
Performance Accuracy ◽  
Point Set

Aims: To provide a baseline for the configuration of Automated Fingerprint Verification System (AFVS) in the face of changing weather and environmental conditions in order to ensure performance accuracy.  Study Design:  Statistical and theoretical research approaches. Place and Duration of Study: Department of Computer Science and Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria, between July 2017 and July 2018. Methodology: Data set were collected in the South-South geopolitical zone of Nigeria. We use 10,000 minutiae points defined by location and orientation features extracted from fingerprint samples obtained at 9 various physical and environmental conditions over 12 months period. These data were used to formulate linear regression models that were used as constraints to the verification objective function derived as constrained linear least squares. The effects of the changing weather and environmental conditions were incorporated into the optimised point-set matching model in order to minimise the total relative error on location and orientation differences between pairs of minutiae. The model was implemented using interior-point convex quadratic programming was implemented in Matlab. Results: The results obtained from the optimisation function by adjusting the thresholds of the effects of weather and environmental conditions to 0.0, 0.0 for location and orientation properties of minutiae, respectively, showed minimal total relative errors on the corresponding pairs of matched minutiae, when compared with using the default threshold values of the selected conditions. Conclusion: The optimisation of point-set based model could provide a computational basis for accurate fingerprint verification for low and high-security AFVS in unfavourable conditions if they are incorporated into the matching model. However, further validation and evaluation of the model with data sets from regions with similar weather and environmental conditions is needed to further validate its robustness in terms of performance accuracy

scholarly journals Adaptive Online Sequential ELM for Concept Drift Tackling

2016 ◽  
Vol 2016 ◽  
pp. 1-17 ◽  
Author(s):  
Arif Budiman ◽  
Mohamad Ivan Fanany ◽  
Chan Basaruddin
Keyword(s):  
Concept Drift ◽  
Classification Problem ◽  
Data Sets ◽  
Regression Problem ◽  
Data Set ◽  
Public Data ◽  
Kappa Value ◽  
Time Changes ◽  
Learning Machine ◽  
Hidden Nodes

A machine learning method needs to adapt to over time changes in the environment. Such changes are known as concept drift. In this paper, we propose concept drift tackling method as an enhancement of Online Sequential Extreme Learning Machine (OS-ELM) and Constructive Enhancement OS-ELM (CEOS-ELM) by adding adaptive capability for classification and regression problem. The scheme is named as adaptive OS-ELM (AOS-ELM). It is a single classifier scheme that works well to handle real drift, virtual drift, and hybrid drift. The AOS-ELM also works well for sudden drift and recurrent context change type. The scheme is a simple unified method implemented in simple lines of code. We evaluated AOS-ELM on regression and classification problem by using concept drift public data set (SEA and STAGGER) and other public data sets such as MNIST, USPS, and IDS. Experiments show that our method gives higher kappa value compared to the multiclassifier ELM ensemble. Even though AOS-ELM in practice does not need hidden nodes increase, we address some issues related to the increasing of the hidden nodes such as error condition and rank values. We propose taking the rank of the pseudoinverse matrix as an indicator parameter to detect “underfitting” condition.

scholarly journals A transformation-driven approach for recognizing textual entailment

2016 ◽  
Vol 23 (4) ◽  
pp. 507-534 ◽  
Author(s):  
ROBERTO ZANOLI ◽  
SILVIA COLOMBO
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Sets ◽  
Learning Approaches ◽  
Data Set ◽  
Open Platform ◽  
Text Fragment ◽  
Textual Entailment ◽  
Order Of Magnitude ◽  
Recognizing Textual Entailment

AbstractTextual Entailment is a directional relation between two text fragments. The relation holds whenever the truth of one text fragment, called Hypothesis (H), follows from another text fragment, called Text (T). Up until now, using machine learning approaches for recognizing textual entailment has been hampered by the limited availability of data. We present an approach based on syntactic transformations and machine learning techniques which is designed to fit well with a new type of available data sets that are larger but less complex than data sets used in the past. The transformations are not predefined, but calculated from the data sets, and then used as features in a supervised learning classifier. The method has been evaluated using two data sets: the SICK data set and the EXCITEMENT English data set. While both data sets are of a larger order of magnitude than data sets such as RTE-3, they are also of lower levels of complexity, each in its own way. SICK consists of pairs created by applying a predefined set of syntactic and lexical rules to its T and H pairs, which can be accurately captured by our transformations. The EXCITEMENT English data contains short pieces of text that do not require a high degree of text understanding to be annotated. The resulting AdArte system is simple to understand and implement, but also effective when compared with other existing systems. AdArte has been made freely available with the EXCITEMENT Open Platform, an open source platform for textual inference.

The social wasp Vespula germanica (Fabricius) (Hymenoptera: Vespidae) population dynamics in England over 39 years.

2018 ◽  
Vol 154 (2) ◽  
pp. 149-155
Author(s):  
Michael Archer
Keyword(s):  
Population Dynamics ◽  
Population Dynamic ◽  
Ecological Factors ◽  
Social Wasp ◽  
Data Sets ◽  
Data Set ◽  
Vespula Germanica ◽  
The Social ◽  
Minimum Number ◽  
Suction Traps

1. Yearly records of worker Vespula germanica (Fabricius) taken in suction traps at Silwood Park (28 years) and at Rothamsted Research (39 years) are examined. 2. Using the autocorrelation function (ACF), a significant negative 1-year lag followed by a lesser non-significant positive 2-year lag was found in all, or parts of, each data set, indicating an underlying population dynamic of a 2-year cycle with a damped waveform. 3. The minimum number of years before the 2-year cycle with damped waveform was shown varied between 17 and 26, or was not found in some data sets. 4. Ecological factors delaying or preventing the occurrence of the 2-year cycle are considered.

Predictive and Descriptive CoMFA Models: The Effect of Variable Selection

2018 ◽  
Vol 21 (2) ◽  
pp. 117-124 ◽  
Author(s):  
Bakhtyar Sepehri ◽  
Nematollah Omidikia ◽  
Mohsen Kompany-Zareh ◽  
Raouf Ghavami
Keyword(s):  
Variable Selection ◽  
Predictive Power ◽  
Selection Method ◽  
Data Sets ◽  
Data Set ◽  
Comfa Model ◽  
Variable Selection Method

Aims & Scope: In this research, 8 variable selection approaches were used to investigate the effect of variable selection on the predictive power and stability of CoMFA models. Materials & Methods: Three data sets including 36 EPAC antagonists, 79 CD38 inhibitors and 57 ATAD2 bromodomain inhibitors were modelled by CoMFA. First of all, for all three data sets, CoMFA models with all CoMFA descriptors were created then by applying each variable selection method a new CoMFA model was developed so for each data set, 9 CoMFA models were built. Obtained results show noisy and uninformative variables affect CoMFA results. Based on created models, applying 5 variable selection approaches including FFD, SRD-FFD, IVE-PLS, SRD-UVEPLS and SPA-jackknife increases the predictive power and stability of CoMFA models significantly. Result & Conclusion: Among them, SPA-jackknife removes most of the variables while FFD retains most of them. FFD and IVE-PLS are time consuming process while SRD-FFD and SRD-UVE-PLS run need to few seconds. Also applying FFD, SRD-FFD, IVE-PLS, SRD-UVE-PLS protect CoMFA countor maps information for both fields.

Human Activity Recognition using Fourier Transform Inspired Deep Learning Combination Model

2019 ◽  
Vol 9 (1) ◽  
pp. 16-31
Author(s):  
Kyungkoo Jun
Keyword(s):  
Fourier Transform ◽  
Deep Learning ◽  
Short Term Memory ◽  
Window Size ◽  
Sensor Data ◽  
Data Sets ◽  
Data Set ◽  
Proposed Model ◽  
Testing Data ◽  
Labeling Scheme

Background & Objective: This paper proposes a Fourier transform inspired method to classify human activities from time series sensor data. Methods: Our method begins by decomposing 1D input signal into 2D patterns, which is motivated by the Fourier conversion. The decomposition is helped by Long Short-Term Memory (LSTM) which captures the temporal dependency from the signal and then produces encoded sequences. The sequences, once arranged into the 2D array, can represent the fingerprints of the signals. The benefit of such transformation is that we can exploit the recent advances of the deep learning models for the image classification such as Convolutional Neural Network (CNN). Results: The proposed model, as a result, is the combination of LSTM and CNN. We evaluate the model over two data sets. For the first data set, which is more standardized than the other, our model outperforms previous works or at least equal. In the case of the second data set, we devise the schemes to generate training and testing data by changing the parameters of the window size, the sliding size, and the labeling scheme. Conclusion: The evaluation results show that the accuracy is over 95% for some cases. We also analyze the effect of the parameters on the performance.

scholarly journals An Algorithm for the Removal of Cosmic Ray Artifacts in Spectral Data Sets

2019 ◽  
Vol 73 (8) ◽  
pp. 893-901
Author(s):  
Sinead J. Barton ◽  
Bryan M. Hennelly
Keyword(s):  
Cosmic Ray ◽  
Data Sets ◽  
Biological Cells ◽  
Statistical Classification ◽  
Signal To Noise ◽  
Multivariate Statistical ◽  
Data Set ◽  
Artefact Removal ◽  
Single Capture ◽  
Acquisition Method

Cosmic ray artifacts may be present in all photo-electric readout systems. In spectroscopy, they present as random unidirectional sharp spikes that distort spectra and may have an affect on post-processing, possibly affecting the results of multivariate statistical classification. A number of methods have previously been proposed to remove cosmic ray artifacts from spectra but the goal of removing the artifacts while making no other change to the underlying spectrum is challenging. One of the most successful and commonly applied methods for the removal of comic ray artifacts involves the capture of two sequential spectra that are compared in order to identify spikes. The disadvantage of this approach is that at least two recordings are necessary, which may be problematic for dynamically changing spectra, and which can reduce the signal-to-noise (S/N) ratio when compared with a single recording of equivalent duration due to the inclusion of two instances of read noise. In this paper, a cosmic ray artefact removal algorithm is proposed that works in a similar way to the double acquisition method but requires only a single capture, so long as a data set of similar spectra is available. The method employs normalized covariance in order to identify a similar spectrum in the data set, from which a direct comparison reveals the presence of cosmic ray artifacts, which are then replaced with the corresponding values from the matching spectrum. The advantage of the proposed method over the double acquisition method is investigated in the context of the S/N ratio and is applied to various data sets of Raman spectra recorded from biological cells.

Imbalanced Data Detection Kernel Method in Closed Systems

2013 ◽  
Vol 756-759 ◽  
pp. 3652-3658
Author(s):  
You Li Lu ◽  
Jun Luo
Keyword(s):  
Kernel Methods ◽  
Kernel Method ◽  
Imbalanced Data ◽  
Data Detection ◽  
Data Sets ◽  
System Call ◽  
Data Set ◽  
Imbalanced Data Sets ◽  
Lower Complexity ◽  
Closed Systems

Under the study of Kernel Methods, this paper put forward two improved algorithm which called R-SVM & I-SVDD in order to cope with the imbalanced data sets in closed systems. R-SVM used K-means algorithm clustering space samples while I-SVDD improved the performance of original SVDD by imbalanced sample training. Experiment of two sets of system call data set shows that these two algorithms are more effectively and R-SVM has a lower complexity.

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