scholarly journals Improving the analysis of biological ensembles through extended similarity measures

2021 ◽  
Author(s):  
Liwei Chang ◽  
Alberto Perez ◽  
Ramon Alain Miranda-Quintana
Keyword(s):  
Clustering Algorithm ◽  
Structural Information ◽  
Similarity Measures ◽  
Agglomerative Clustering ◽  
Excellent Performance ◽  
Biological Interest ◽  
Structural Ensembles ◽  
Similarity Indices ◽  
Hierarchical Agglomerative Clustering ◽  
New Algorithms

We present new algorithms to classify structural ensembles of macromolecules, based on the recently proposed extended similarity measures. Molecular Dynamics provides a wealth of structural information on systems of biologically interest. As computer power increases we capture larger ensembles and larger conformational transitions between states. Typically, structural clustering provides the statistical mechanics treatment of the system to identify relevant biological states. The key advantage of our approach is that the newly introduced extended similiarity indices reduce the computational complexity of assessing the similarity of a set of structures from O(N2) to O(N). Here we take advantage of this favorable cost to develop several highly efficient techniques, including a linear-scaling algorithm to determine the medoid of a set (which we effectively use to select the most representative structure of a cluster). Moreover, we use our extended similarity indices as a linkage criterion in a novel hierarchical agglomerative clustering algorithm. We apply these new metrics to analyze the ensembles of several systems of biological interest such as folding and binding of macromolecules (peptide, protein, DNA-protein). In particular, we design a new workflow that is capable of identifying the most important conformations contributing to the protein folding process. We show excellent performance in the resulting clusters (surpassing traditional linkage criteria), along with faster performance and an efficient cost-function to identify when to merge clusters.

scholarly journals Improving the analysis of biological ensembles through extended similarity measures

2021 ◽  
Author(s):  
Liwei Chang ◽  
Alberto Perez ◽  
Ramon Alain Miranda-Quintana
Keyword(s):  
Molecular Dynamics ◽  
Structural Information ◽  
Similarity Measures ◽  
Structural Ensembles ◽  
New Algorithms

We present new algorithms to classify structural ensembles of macromolecules, based on the recently proposed extended similarity measures. Molecular Dynamics provides a wealth of structural information on systems of biologically...

scholarly journals Radar Emission Sources Identification Based on Hierarchical Agglomerative Clustering for Large Data Sets

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Janusz Dudczyk
Keyword(s):  
Clustering Algorithm ◽  
Large Data ◽  
Large Data Sets ◽  
Emission Sources ◽  
Data Sets ◽  
Agglomerative Clustering ◽  
Distinctive Features ◽  
Identification Process ◽  
Hierarchical Agglomerative Clustering ◽  
Repetition Interval

More advanced recognition methods, which may recognize particular copies of radars of the same type, are called identification. The identification process of radar devices is a more specialized task which requires methods based on the analysis of distinctive features. These features are distinguished from the signals coming from the identified devices. Such a process is called Specific Emitter Identification (SEI). The identification of radar emission sources with the use of classic techniques based on the statistical analysis of basic measurable parameters of a signal such as Radio Frequency, Amplitude, Pulse Width, or Pulse Repetition Interval is not sufficient for SEI problems. This paper presents the method of hierarchical data clustering which is used in the process of radar identification. The Hierarchical Agglomerative Clustering Algorithm (HACA) based on Generalized Agglomerative Scheme (GAS) implemented and used in the research method is parameterized; therefore, it is possible to compare the results. The results of clustering are presented in dendrograms in this paper. The received results of grouping and identification based on HACA are compared with other SEI methods in order to assess the degree of their usefulness and effectiveness for systems of ESM/ELINT class.

Research on NMF Based Hierarchical Clustering Methods

2010 ◽  
Vol 439-440 ◽  
pp. 1306-1311
Author(s):  
Fang Li ◽  
Qun Xiong Zhu
Keyword(s):  
Hierarchical Clustering ◽  
Clustering Algorithm ◽  
Clustering Methods ◽  
Agglomerative Clustering ◽  
Clustering Method ◽  
Hierarchical Agglomerative Clustering ◽  
Hierarchical Clustering Methods

LSI based hierarchical agglomerative clustering algorithm is studied. Aiming to the problems of LSI based hierarchical agglomerative clustering method, NMF based hierarchical clustering method is proposed and analyzed. Two ways of implementing NMF based method are introduced. Finally the result of two groups of experiment based on the TanCorp document corpora show that the method proposed is effective.

Clustering of Micro-Messages Using Similarity Upper Approximation

2017 ◽  
Vol 25 (01) ◽  
pp. 53-79 ◽  
Author(s):  
Mukul Gupta ◽  
Pradeep Kumar ◽  
Bharat Bhasker
Keyword(s):  
Clustering Algorithm ◽  
State Of The Art ◽  
Clustering Algorithms ◽  
Mining Operation ◽  
Agglomerative Clustering ◽  
Upper Approximation ◽  
Affinity Propagation Clustering ◽  
Hierarchical Agglomerative Clustering ◽  
Text Content ◽  
Extract Information

Microblogging platforms like Twitter, Tumblr and Plurk have radically changed our lives. The presence of millions of people has made these platforms a preferred channel for communication. A large amount of User Generated Content, on these platforms, has attracted researchers and practitioners to mine and extract information nuggets. For information extraction, clustering is an important and widely used mining operation. This paper addresses the issue of clustering of micro-messages and corresponding users based on the text content of micro-messages that reflect their primitive interest. In this paper, we performed modification of the Similarity Upper Approximation based clustering algorithm for clustering of micro-messages. We compared the performance of the modified Similarity Upper Approximation based clustering algorithm with state-of-the-art clustering algorithms such as Partition Around Medoids, Hierarchical Agglomerative Clustering, Affinity Propagation Clustering and DBSCAN. Experiments were performed on micro-messages collected from Twitter. Experimental results show the effectiveness of the proposed algorithm.

scholarly journals Clustering of a Health Dataset Using Diagnosis Co-Occurrences

2021 ◽  
Vol 11 (5) ◽  
pp. 2373
Author(s):  
Adrien Wartelle ◽  
Farah Mourad-Chehade ◽  
Farouk Yalaoui ◽  
Jan Chrusciel ◽  
David Laplanche ◽  
...  
Keyword(s):  
Quality Indicators ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Agglomerative Clustering ◽  
Cluster Membership ◽  
Healthcare Needs ◽  
Similar Patient ◽  
Hierarchical Agglomerative Clustering ◽  
Patient Profiles ◽  
Cluster Compactness

Assessing the health profiles of populations is a crucial task to create a coherent healthcare offer. Emergency Departments (EDs) are at the core of the healthcare system and could benefit from this evaluation via an improved understanding of the healthcare needs of their population. This paper proposes a novel hierarchical agglomerative clustering algorithm based on multimorbidity analysis. The proposed approach constructs the clustering dendrogram by introducing new quality indicators based on the relative risk of co-occurrences of patient diagnoses. This algorithm enables the detection of multimorbidity patterns by merging similar patient profiles according to their common diagnoses. The multimorbidity approach has been applied to the data of the largest ED of the Aube Department (Eastern France) to cluster its patient visits. Among the 120,718 visits identified during a 24-month period, 16 clusters were identified, accounting for 94.8% of the visits, with the five most prevalent clusters representing 63.0% of them. The new quality indicators show a coherent and good clustering solution with a cluster membership of 1.81 based on a cluster compactness of 1.40 and a cluster separation of 0.77. Compared to the literature, the proposed approach is appropriate for the discovery of multimorbidity patterns and could help to develop better clustering algorithms for more diverse healthcare datasets.

scholarly journals Neuronal classification from network connectivity via adjacency spectral embedding

2021 ◽  
pp. 1-35
Author(s):  
Ketan Mehta ◽  
Rebecca F. Goldin ◽  
David Marchette ◽  
Joshua T. Vogelstein ◽  
Carey E. Priebe ◽  
...  
Keyword(s):  
Large Scale ◽  
Clustering Algorithm ◽  
Probability Distributions ◽  
Network Connectivity ◽  
Gaussian Mixture ◽  
Model Parameters ◽  
Agglomerative Clustering ◽  
Spectral Embedding ◽  
Hierarchical Agglomerative Clustering ◽  
Novel Strategy

Abstract This work presents a novel strategy for classifying neurons, represented by nodes of a directed graph, based on their circuitry (edge connectivity). We assume a stochastic block model (SBM) in which neurons belong together if they connect to neurons of other groups according to the same probability distributions. Following adjacency spectral embedding of the SBM graph, we derive the number of classes and assign each neuron to a class with a Gaussian mixture model-based expectation-maximization (EM) clustering algorithm. To improve accuracy, we introduce a simple variation using random hierarchical agglomerative clustering to initialize the EM algorithm and picking the best solution over multiple EM restarts. We test this procedure on a large (≈212–215 neurons), sparse, biologically inspired connectome with eight neuron classes. The simulation results demonstrate that the proposed approach is broadly stable to the choice of embedding dimension, and scales extremely well as the number of neurons in the network increases. Clustering accuracy is robust to variations in model parameters and highly tolerant to simulated experimental noise, achieving perfect classifications with up to 40% of swapped edges. Thus, this approach may be useful to analyze and interpret large-scale brain connectomics data in terms of underlying cellular components.

scholarly journals Neuronal Classification from Network Connectivity via Adjacency Spectral Embedding

2020 ◽  
Author(s):  
Ketan Mehta ◽  
Rebecca F. Goldin ◽  
David Marchette ◽  
Joshua T. Vogelstein ◽  
Carey E. Priebe ◽  
...  
Keyword(s):  
Large Scale ◽  
Clustering Algorithm ◽  
Probability Distributions ◽  
Network Connectivity ◽  
Gaussian Mixture ◽  
Model Parameters ◽  
Agglomerative Clustering ◽  
Spectral Embedding ◽  
Hierarchical Agglomerative Clustering ◽  
Novel Strategy

AbstractThis work presents a novel strategy for classifying neurons, represented by nodes of a directed graph, based on their circuitry (edge connectivity). We assume a stochastic block model (SBM) where neurons belong together if they connect to neurons of other groups according to the same probability distributions. Following adjacency spectral embedding (ASE) of the SBM graph, we derive the number of classes and assign each neuron to a class with a Gaussian mixture model-based expectation-maximization (EM) clustering algorithm. To improve accuracy, we introduce a simple variation using random hierarchical agglomerative clustering to initialize the EM algorithm and picking the best solution over multiple EM restarts. We test this procedure on a large (n ~ 212 − 215 neurons), sparse, biologically inspired connectome with eight neuron classes. The simulation results demonstrate that the proposed approach is broadly stable to the choice of dimensional embedding and scales extremely well as the number of neurons in the network increases. Clustering accuracy is robust to variations in model parameters and highly tolerant to simulated experimental noise, achieving perfect classifications with up to 40% of swapped edges. Thus, this approach may be useful to analyze and interpret large-scale brain connectomics data in terms of underlying cellular components.

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