16 papers · ranked by Valyu relevance
Etienne Ackermann, Caleb T. Kemere, John P. Cunningham
Spike sorting is a standard preprocessing step to obtain ensembles of single unit data from multiunit, multichannel recordings in neuroscience. However, more recently, some researchers have started doing analyses directly on the unsorted data. Here we present a new computational model that is an extension of the…
Reza Farahani, Mario Colosi, Ilir Murturi, Stefan Nastic + 3 more
The recent convergence of edge computing, serverless execution, and Kubernetes (K8s) based container orchestration has enabled the processing of application workflows close to data sources. While effective within a single edge cluster, existing schemes do not generalize to federated multi edge environments, where…
C.H. Trasviña-Arenas, Mohammad Hashemian, Melody Malek, Steven Merrill + 2 more
The [4Fe-4S] cluster is an important cofactor of the base excision repair (BER) adenine DNA glycosylase MutY to prevent mutations associated with 8-oxoguanine (OG). Several MutYs lacking the [4Fe-4S] cofactor have been identified. Phylogenetic analysis shows that clusterless MutYs are distributed in two clades…
Ghahari, Azar, Eden, Uri T.
In the last decade, there have been major advances in clusterless decoding algorithms for neural data analysis. These algorithms use the theory of marked point processes to describe the joint activity of many neurons simultaneously, without the need for spike sorting. In this study, we examine information-theoretic…
Joshua P. Chu, Michael E. Coulter, Eric L. Denovellis, Trevor Thai K. Nguyen + 5 more
Decoding algorithms provide a powerful tool for understanding the firing patterns that underlie cognitive processes such as motor control, learning, and recall. When implemented in the context of a real-time system, decoders also make it possible to deliver feedback based on the representational content of ongoing…
Luke Zappia, Alicia Oshlack
Clustering techniques are widely used in the analysis of large datasets to group together samples with similar properties. For example, clustering is often used in the field of single-cell RNA-sequencing in order to identify different cell types present in a tissue sample. There are many algorithms for performing…
Linda Dib, Alessandra Carbone
Background Searching for similarities in a set of biological data is intrinsically difficult due to possible data points that should not be clustered, or that should group within several clusters. Under these hypotheses, hierarchical agglomerative clustering is not appropriate. Moreover, if the dataset is not known…
Joao C. Marques, Michael B. Orger
How to partition a data set into a set of distinct clusters is a ubiquitous and challenging problem. The fact that data sets vary widely in features such as cluster shape, cluster number, density distribution, background noise, outliers and degree of overlap, makes it difficult to find a single algorithm that can be…
Basel Abu-Jamous, Steven Kelly
Identification of co-expressed genes within a given experimental or biological context can provide evidence for genetic or physical interactions between genes. Thus, detection of co-expression has become a routine step in large-scale analyses of gene expression data. In this work, we show that application of the most…
Stijn van Dongen
Clustering (a large class of methods) is a standard and often used approach in data analysis for separating data into groups, called clusters, often in large-scale high-dimensional data. A clustering (a data structure) is a partitioning of the data into disjoint clusters. This is sometimes called a flat clustering to…
Marco Rovere, Ziheng Chen, Antonio Di Pilato, Felice Pantaleo + 1 more
'Chris Seez'] One of the challenges of high granularity calorimeters, such as that to be built to cover the endcap region in the CMS Phase-2 Upgrade for HL-LHC, is that the large number of channels causes a surge in the computing load when clustering numerous digitized energy deposits (hits) in the reconstruction…
Sudhakar Jonnalagadda, Rajagopalan Srinivasan
Background Clustering techniques are routinely used in gene expression data analysis to organize the massive data. Clustering techniques arrange a large number of genes or assays into a few clusters while maximizing the intra-cluster similarity and inter-cluster separation. While clustering of genes facilitates…
Haide Wu, Morten Engsvang, Yosef Knattrup, Jakub Kubečka + 1 more
The nucleation process leading to the formation of new atmospheric particles plays a crucial role in aerosol research. Quantum chemical (QC) calculations can be used to model the early stages of aerosol formation, where atmospheric vapor molecules interact and form stable molecular clusters. However, QC calculations…
Behnam Yousefi, Benno Schwikowski
Clustering plays an important role in a multitude of bioinformatics applications, including protein function prediction, population genetics, and gene expression analysis. The results of most clustering algorithms are sensitive to variations of the input data, the clustering algorithm and its parameters, and individual…
Dimitrios Saligkaras, Vasileios E. Papageorgiou
Clustering is an unsupervised machine learning methodology where unlabeled elements/objects are grouped together aiming to the construction of well-established clusters that their elements are classified according to their similarity. The goal of this process is to provide a useful aid to the researcher that will help…
Robert A. Kłopotek, Mieczysław A. Kłopotek
This paper investigates the validity of Kleinberg's axioms for clustering functions with respect to the quite popular clustering algorithm called k-means.We suggest that the reason why this algorithm does not fit Kleinberg's axiomatic system stems from missing match between informal intuitions and formal formulations…