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openGEO-OpenSep 2021View details →

Scalable Production of Mouse Erythroid Cells using an in vitro Embryoid Body differentiation System [Capture-C]

GEO Series GSE184432. Mus musculus. 12 samples. Type: Other.

ClinicalTrials.gov20/100

Scalable Expanded Access With Analysis of Neurofilament and Other Biomarkers for Ibudilast in ALS

ClinicalTrials.gov study NCT06743776. IPD Sharing: Not stated. Countries: 1. Publications: 0.

restrictedIPD-UNDECIDEDFeb 2026View details →

openGEO-OpenOct 2023View details →

TIME-Seq Enables Scalable and Inexpensive Epigenetic Age Predictions.

GEO Series GSE245630. Mus musculus; Homo sapiens. 175 samples. Type: Methylation profiling by array; Methylation profiling by high throughput sequencing.

openGEO-OpenJan 2020View details →

Rapid and scalable profiling of nascent RNA with fastGRO

GEO Series GSE143844. Homo sapiens. 28 samples. Type: Expression profiling by high throughput sequencing.

openGEO-OpenJul 2023View details →

Scalable single-cell profiling of chromatin modifications with sciCUT&Tag

GEO Series GSE224579. Mus musculus; Homo sapiens. 34 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.

openGEO-OpenMar 2016View details →

Enhanced CLIP (eCLIP) enables robust and scalable transcriptome-wide discovery and characterization of RNA binding protein binding sites [array]

GEO Series GSE77339. Homo sapiens. 12 samples. Type: Expression profiling by array.

openGEO-OpenSep 2021View details →

Scalable Production of Mouse Erythroid Cells using an in vitro Embryoid Body differentiation System [ATAC-Seq]

GEO Series GSE184429. Mus musculus. 20 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.

Scalable, Asynchronous, Distributed Eigen-Monitoring of Astronomy Data Streams

In this paper, we develop a distributed algorithm for monitoring the principal components (PCs) for next generation of astronomy petascale data pipelines such as the Large Synoptic Survey Telescopes (LSST). This telescope will take repeated images of the night sky every 20 s, thereby generating 30 terabytes of calibrated imagery every night that will need to be co-analyzed with other astronomical data stored at different locations around the world. Event detection, classification, and isolation in such data sets may provide useful insights to unique astronomical phenomenon displaying astrophysically significant variations: quasars, supernovae, variable stars, and potentially hazardous asteroids. However, performing such data mining tasks is a challenging problem for such high-throughput distributed data streams. In this paper, we propose a highly scalable and distributed asynchronous algorithm for monitoring the PCs of such dynamic data streams and discuss a prototype web-based system PADMINI (Peer-to-Peer Astronomy Data Mining) which implements this algorithm for use by the astronomers. We demonstrate the algorithm on a large set of distributed astronomical data to accomplish well-known astronomy tasks such as measuring variations in the fundamental plane of galaxy parameters. The proposed algorithm is provably correct (i.e., converges to the correct PCs without centralizing any data) and can seamlessly handle changes to the data or the network. Real experiments performed on Sloan Digital Sky Survey (SDSS) catalogue data show the effectiveness of the algorithm.

SCALABLE TIME SERIES CHANGE DETECTION FOR BIOMASS MONITORING USING GAUSSIAN PROCESS

SCALABLE TIME SERIES CHANGE DETECTION FOR BIOMASS MONITORING USING GAUSSIAN PROCESS VARUN CHANDOLA* AND RANGA RAJU VATSAVAI* Abstract. Biomass monitoring, specifically, detecting changes in the biomass or vegetation of a geographical region, is vital for studying the carbon cycle of the system and has significant implications in the context of understanding climate change and its impacts. Recently, several time series change detection methods have been proposed to identify land cover changes in temporal profiles (time series) of vegetation collected using remote sensing instruments. In this paper, we adapt Gaussian process regression to detect changes in such time series in an online fashion. While Gaussian process (GP) has been widely used as a kernel based learning method for regression and classification, their applicability to massive spatio-temporal data sets, such as remote sensing data, has been limited owing to the high computational costs involved. In our previous work we proposed an efficient Toeplitz matrix based solution for scalable GP parameter estimation. In this paper we apply these solutions to a GP based change detection algorithm. The proposed change detection algorithm requires a memory footprint which is linear in the length of the input time series and runs in time which is quadratic to the length of the input time series. Experimental results show that both serial and parallel implementations of our proposed method achieve significant speedups over the serial implementation. Finally, we demonstrate the effectiveness of the proposed change detection method in identifying changes in Normalized Difference Vegetation Index (NDVI) data.

A Local Scalable Distributed Expectation Maximization Algorithm for Large Peer-to-Peer Networks

This paper describes a local and distributed expectation maximization algorithm for learning parameters of Gaussian mixture models (GMM) in large peer-to-peer (P2P) environments. The algorithm can be used for a variety of well-known data mining tasks in distributed environments such as clustering, anomaly detection, target tracking, and density estimation to name a few, necessary for many emerging P2P applications in bioinformatics, webmining and sensor networks. Centralizing all or some of the data to build global models is impractical in such P2P environments because of the large number of data sources, the asynchronous nature of the P2P networks, and dynamic nature of the data/network. The proposed algorithm takes a two-step approach. In the monitoring phase, the algorithm checks if the model ‘quality’ is acceptable by using an efficient local algorithm. This is then used as a feedback loop to sample data from the network and rebuild the GMM when it is outdated. We present thorough experimental results to verify our theoretical claims.

restrictednotspecifiedApr 2025View details →

A Local Scalable Distributed EM Algorithm for Large P2P Networks

his paper describes a local and distributed expectation maximization algorithm for learning parameters of Gaussian mixture models (GMM) in large peer-to-peer (P2P) environments. The algorithm can be used for a variety of well-known data mining tasks in distributed environments such as clustering, anomaly detection, target tracking, and density estimation to name a few, necessary for many emerging P2P applications in bioinformatics, webmining and sensor networks. Centralizing all or some of the data to build global models is impractical in such P2P environments because of the large number of data sources, the asynchronous nature of the P2P networks, and dynamic nature of the data/network. The proposed algorithm takes a two-step approach. In the monitoring phase, the algorithm checks if the model ‘quality’ is acceptable by using an efficient local algorithm. This is then used as a feedback loop to sample data from the network and rebuild the GMM when it is outdated. We present thorough experimental results to verify our theoretical claims.

Highly Scalable Matching Pursuit Signal Decomposition Algorithm

In this research, we propose a variant of the classical Matching Pursuit Decomposition (MPD) algorithm with significantly improved scalability and computational performance. MPD is a powerful iterative algorithm that decomposes a signal into linear combinations of its dictionary elements or “atoms”. A best fit atom from an arbitrarily defined dictionary is determined through cross-correlation. The selected atom is subtracted from the signal and this procedure is repeated on the residual in the subsequent iterations until a stopping criteria is met. A sufficiently large dictionary is required for an accurate reconstruction; this in return increases the computational burden of the algorithm, thus limiting its applicability and level of adoption. Our main contribution lies in improving the computational efficiency of the algorithm to allow faster decomposition while maintaining a similar level of accuracy. The Correlation Thresholding and Multiple Atom Extractions techniques were proposed to decrease the computational burden of the algorithm. Correlation thresholds prune insignificant atoms from the dictionary. The ability to extract multiple atoms within a single iteration enhanced the effectiveness and efficiency of each iteration. The proposed algorithm, entitled MPD++, was demonstrated using real world data set.

ANALYZING AVIATION SAFETY REPORTS: FROM TOPIC MODELING TO SCALABLE MULTI-LABEL CLASSIFICATION

ANALYZING AVIATION SAFETY REPORTS: FROM TOPIC MODELING TO SCALABLE MULTI-LABEL CLASSIFICATION AMRUDIN AGOVIC*, HANHUAI SHAN*, AND ARINDAM BANERJEE* Abstract. The Aviation Safety Reporting System (ASRS) is used to collect voluntarily submitted aviation safety reports from pilots, controllers and others. As such it is particularly useful in researching aviation safety deficiencies. In this paper we address two challenges related to the analysis of ASRS data: (1) the unsupervised extraction of meaningful and interpretable topics from ASRS reports and (2) multi-label classification of ASRS data based on a set of predefined categories. For topic modeling we investigate the practical usefulness of Latent Dirichlet Allocation (LDA) when it comes to modeling ASRS reports in terms of interpretable topics. We also utilize LDA to generate a more compact representation of ASRS reports to be used in multi-label classification. For multi-label classification we propose a novel and highly scalable multi-label classification algorithm based on multi-variate regression. Empirical results indicate that our approach is superior to several baseline and state-of-the-art approaches.

A Scalable Local Algorithm for Distributed Multivariate Regression

This paper offers a local distributed algorithm for multivariate regression in large peer-to-peer environments. The algorithm can be used for distributed inferencing, data compaction, data modeling and classification tasks in many emerging peer-to-peer applications for bioinformatics, astronomy, social networking, sensor networks and web mining. Computing a global regression model from data available at the different peer-nodes using a traditional centralized algorithm for regression can be very costly and impractical because of the large number of data sources, the asynchronous nature of the peer-to-peer networks, and dynamic nature of the data/network. This paper proposes a two-step approach to deal with this problem. First, it offers an efficient local distributed algorithm that monitors the quality of the current regression model. If the model is outdated, it uses this algorithm as a feedback mechanism for rebuilding the model. The local nature of the monitoring algorithm guarantees low monitoring cost. Experimental results presented in this paper strongly support the theoretical claims.

Scalable Distributed Change Detection from Astronomy Data Streams using Local, Asynchronous Eigen Monitoring Algorithms

This paper considers the problem of change detection using local distributed eigen monitoring algorithms for next generation of astronomy petascale data pipelines such as the Large Synoptic Survey Telescopes (LSST). This telescope will take repeat images of the night sky every 20 seconds, thereby generating 30 terabytes of calibrated imagery every night that will need to be coanalyzed with other astronomical data stored at different locations around the world. Change point detection and event classification in such data sets may provide useful insights to unique astronomical phenomenon displaying astrophysically significant variations: quasars, supernovae, variable stars, and potentially hazardous asteroids. However, performing such data mining tasks is a challenging problem for such high-throughput distributed data streams. In this paper we propose a highly scalable and distributed asynchronous algorithm for monitoring the principal components (PC) of such dynamic data streams. We demonstrate the algorithm on a large set of distributed astronomical data to accomplish well-known astronomy tasks such as measuring variations in the fundamental plane of galaxy parameters. The proposed algorithm is provably correct (i.e. converges to the correct PCs without centralizing any data) and can seamlessly handle changes to the data or the network. Real experiments performed on Sloan Digital Sky Survey (SDSS) catalogue data show the effectiveness of the algorithm.

Discovering Anomalous Aviation Safety Events Using Scalable Data Mining Algorithms

The worldwide civilian aviation system is one of the most complex dynamical systems created. Most modern commercial aircraft have onboard flight data recorders that record several hundred discrete and continuous parameters at approximately 1Hz for the entire duration of the flight. These data contain information about the flight control systems, actuators, engines, landing gear, avionics, and pilot commands. In this paper, recent advances in the development of a novel knowledge discovery process consisting of a suite of data mining techniques for identifying precursors to aviation safety incidents are discussed. The data mining techniques include scalable multiple-kernel learning for large-scale distributed anomaly detection. A novel multivariate time-series search algorithm is used to search for signatures of discovered anomalies on massive datasets. The process can identify operationally significant events due to environmental, mechanical, and human factors issues in the high-dimensional flight operations quality assurance data. All discovered anomalies are validated by a team of independent domain experts. This novel automated knowledge discovery process is aimed at complementing the state-of-the-art human-generated exceedance-based analysis that fails to discover previously unknown aviation safety incidents. In this paper, the discovery pipeline, the methods used, and some of the significant anomalies detected on real-world commercial aviation data are discussed.

ARC Code TI: Block-GP: Scalable Gaussian Process Regression

Block GP is a Gaussian Process regression framework for multimodal data, that can be an order of magnitude more scalable than existing state-of-the-art nonlinear regression algorithms. The framework builds local Gaussian Processes on semantically meaningful partitions of the data and provides higher prediction accuracy than a single global model with very high confidence.

Block-GP: Scalable Gaussian Process Regression for Multimodal Data

Regression problems on massive data sets are ubiquitous in many application domains including the Internet, earth and space sciences, and finances. In many cases, regression algorithms such as linear regression or neural networks attempt to fit the target variable as a function of the input variables without regard to the underlying joint distribution of the variables. As a result, these global models are not sensitive to variations in the local structure of the input space. Several algorithms, including the mixture of experts model, classification and regression trees (CART), and others have been developed, motivated by the fact that a variability in the local distribution of inputs may be reflective of a significant change in the target variable. While these methods can handle the non-stationarity in the relationships to varying degrees, they are often not scalable and, therefore, not used in large scale data mining applications. In this paper we develop Block-GP, a Gaussian Process regression framework for multimodal data, that can be an order of magnitude more scalable than existing state-of-the-art nonlinear regression algorithms. The framework builds local Gaussian Processes on semantically meaningful partitions of the data and provides higher prediction accuracy than a single global model with very high confidence. The method relies on approximating the covariance matrix of the entire input space by smaller covariance matrices that can be modeled independently, and can therefore be parallelized for faster execution. Theoretical analysis and empirical studies on various synthetic and real data sets show high accuracy and scalability of Block-GP compared to existing nonlinear regression techniques.