A New Soft Computing Method for K-Harmonic Means Clustering.
Ontology highlight
ABSTRACT:
SUBMITTER: Yeh WC
PROVIDER: S-EPMC5112810 | biostudies-literature | 2016
REPOSITORIES: biostudies-literature
Publications
A New Soft Computing Method for K-Harmonic Means Clustering.
Yeh Wei-Chang WC Jiang Yunzhi Y Chen Yee-Fen YF Chen Zhe Z
PloS one 20161115 11
The K-harmonic means clustering algorithm (KHM) is a new clustering method used to group data such that the sum of the harmonic averages of the distances between each entity and all cluster centroids is minimized. Because it is less sensitive to initialization than K-means (KM), many researchers have recently been attracted to studying KHM. In this study, the proposed iSSO-KHM is based on an improved simplified swarm optimization (iSSO) and integrates a variable neighborhood search (VNS) for KHM ...[more]
Similar Datasets
Project description:BackgroundSingle-cell RNA-Sequencing (scRNA-Seq) has provided single-cell level insights into complex biological processes. However, the high frequency of gene expression detection failures in scRNA-Seq data make it challenging to achieve reliable identification of cell-types and Differentially Expressed Genes (DEG). Moreover, with the explosive growth of single-cell data using 10x genomics protocol, existing methods will soon reach the computation limit due to scalability issues. The single-cell transcriptomics field desperately need new tools and framework to facilitate large-scale single-cell analysis.ResultsIn order to improve the accuracy, robustness, and speed of scRNA-Seq data processing, we propose a generalized zero-inflated negative binomial mixture model, "JOINT," that can perform probability-based cell-type discovery and DEG analysis simultaneously without the need for imputation. JOINT performs soft-clustering for cell-type identification by computing the probability of individual cells, i.e. each cell can belong to multiple cell types with different probabilities. This is drastically different from existing hard-clustering methods where each cell can only belong to one cell type. The soft-clustering component of the algorithm significantly facilitates the accuracy and robustness of single-cell analysis, especially when the scRNA-Seq datasets are noisy and contain a large number of dropout events. Moreover, JOINT is able to determine the optimal number of cell-types automatically rather than specifying it empirically. The proposed model is an unsupervised learning problem which is solved by using the Expectation and Maximization (EM) algorithm. The EM algorithm is implemented using the TensorFlow deep learning framework, dramatically accelerating the speed for data analysis through parallel GPU computing.ConclusionsTaken together, the JOINT algorithm is accurate and efficient for large-scale scRNA-Seq data analysis via parallel computing. The Python package that we have developed can be readily applied to aid future advances in parallel computing-based single-cell algorithms and research in various biological and biomedical fields.
| S-EPMC7798298 | biostudies-literature
Project description:BackgroundSingle individual haplotype problem refers to reconstructing haplotypes of an individual based on several input fragments sequenced from a specified chromosome. Solving this problem is an important task in computational biology and has many applications in the pharmaceutical industry, clinical decision-making, and genetic diseases. It is known that solving the problem is NP-hard. Although several methods have been proposed to solve the problem, it is found that most of them have low performances in dealing with noisy input fragments. Therefore, proposing a method which is accurate and scalable, is a challenging task.ResultsIn this paper, we introduced a method, named NCMHap, which utilizes the Neutrosophic c-means (NCM) clustering algorithm. The NCM algorithm can effectively detect the noise and outliers in the input data. In addition, it can reduce their effects in the clustering process. The proposed method has been evaluated by several benchmark datasets. Comparing with existing methods indicates when NCM is tuned by suitable parameters, the results are encouraging. In particular, when the amount of noise increases, it outperforms the comparing methods.ConclusionThe proposed method is validated using simulated and real datasets. The achieved results recommend the application of NCMHap on the datasets which involve the fragments with a huge amount of gaps and noise.
| S-EPMC7579908 | biostudies-literature
Project description:Single-cell RNA sequencing (scRNA-seq) allows researchers to study cell heterogeneity at the cellular level. A crucial step in analyzing scRNA-seq data is to cluster cells into subpopulations to facilitate subsequent downstream analysis. However, frequent dropout events and increasing size of scRNA-seq data make clustering such high-dimensional, sparse and massive transcriptional expression profiles challenging. Although some existing deep learning-based clustering algorithms for single cells combine dimensionality reduction with clustering, they either ignore the distance and affinity constraints between similar cells or make some additional latent space assumptions like mixture Gaussian distribution, failing to learn cluster-friendly low-dimensional space. Therefore, in this paper, we combine the deep learning technique with the use of a denoising autoencoder to characterize scRNA-seq data while propose a soft self-training K-means algorithm to cluster the cell population in the learned latent space. The self-training procedure can effectively aggregate the similar cells and pursue more cluster-friendly latent space. Our method, called 'scziDesk', alternately performs data compression, data reconstruction and soft clustering iteratively, and the results exhibit excellent compatibility and robustness in both simulated and real data. Moreover, our proposed method has perfect scalability in line with cell size on large-scale datasets.
| S-EPMC7671315 | biostudies-literature
Project description:High harmonic generation (HHG) of an intense laser pulse is a highly nonlinear optical phenomenon that provides the only proven source of tabletop attosecond pulses, and it is the key technology in attosecond science. Recent developments in high-intensity infrared lasers have extended HHG beyond its traditional domain of the XUV spectral range (10-150?eV) into the soft X-ray regime (150?eV to 3?keV), allowing the compactness, stability and sub-femtosecond duration of HHG to be combined with the atomic site specificity and electronic/structural sensitivity of X-ray spectroscopy. HHG in the soft X-ray spectral region has significant differences from HHG in the XUV, which necessitate new approaches to generating and characterizing attosecond pulses. Here, we examine the challenges and opportunities of soft X-ray HHG, and we use simulations to examine the optimal generating conditions for the development of high-flux, attosecond-duration pulses in the soft X-ray spectral range. This article is part of the theme issue 'Measurement of ultrafast electronic and structural dynamics with X-rays'.
| S-EPMC6452054 | biostudies-literature
Project description:Mechanical computing offers a new modality to formulate computational autonomy in intelligent matter or machines without any external powering or active elements. Transition (or solitary) waves, induced by nonreciprocity in mechanical metamaterials comprising a chain of bistable elements, have proven to be a key ingredient for dissipation-free transmission and computation of mechanical information. However, advanced processing of mechanical information in existing designs is hindered by its dissipation when interacting with networked logic gates. Here, we present a metamaterial design strategy that allows non-dispersive mechanical solitary waves to compute multi-level cascaded logic functions, termed 'integrated mechanical computing', by propagating through a network of structurally heterogeneous computing units. From a perspective of characteristic potential energy, we establish an analytical framework that helps in understanding the solitary wave-based mechanical computation, and governs the mechanical design of key determinants for realizing cascaded logic computation, such as soliton profile and logic elements. The developed integrated mechanical computing systems are shown to receive, transmit and compute mechanical information to actuate intelligent soft machine prototypes in a seamless and integrated manner. These findings would pave the way for future intelligent robots and machines that perform computational operations between various non-electrical environmental inputs.
| S-EPMC10995184 | biostudies-literature
Project description:MotivationCancer genomes exhibit a large number of different alterations that affect many genes in a diverse manner. An improved understanding of the generative mechanisms behind the mutation rules and their influence on gene community behavior is of great importance for the study of cancer.ResultsTo expand our capability to analyze combinatorial patterns of cancer alterations, we developed a rigorous methodology for cancer mutation pattern discovery based on a new, constrained form of correlation clustering. Our new algorithm, named C3 (Cancer Correlation Clustering), leverages mutual exclusivity of mutations, patient coverage and driver network concentration principles. To test C3, we performed a detailed analysis on TCGA breast cancer and glioblastoma data and showed that our algorithm outperforms the state-of-the-art CoMEt method in terms of discovering mutually exclusive gene modules and identifying biologically relevant driver genes. The proposed agnostic clustering method represents a unique tool for efficient and reliable identification of mutation patterns and driver pathways in large-scale cancer genomics studies, and it may also be used for other clustering problems on biological graphs.Availability and implementationThe source code for the C3 method can be found at https://github.com/jackhou2/C3 CONTACTS: jianma@cs.cmu.edu or milenkov@illinois.eduSupplementary information: Supplementary data are available at Bioinformatics online.
| S-EPMC6078169 | biostudies-literature
Project description:One of the difficulties in using gene expression profiles to predict cancer is how to effectively select a few informative genes to construct accurate prediction models from thousands or ten thousands of genes. We screen highly discriminative genes and gene pairs to create simple prediction models involved in single genes or gene pairs on the basis of soft computing approach and rough set theory. Accurate cancerous prediction is obtained when we apply the simple prediction models for four cancerous gene expression datasets: CNS tumor, colon tumor, lung cancer and DLBCL. Some genes closely correlated with the pathogenesis of specific or general cancers are identified. In contrast with other models, our models are simple, effective and robust. Meanwhile, our models are interpretable for they are based on decision rules. Our results demonstrate that very simple models may perform well on cancerous molecular prediction and important gene markers of cancer can be detected if the gene selection approach is chosen reasonably.
| S-EPMC2730177 | biostudies-literature
Project description:This study addresses the nonlinear structure-extended cavity interaction simulation using a new version of the multilevel residue harmonic balance method. This method has only been adopted once to solve a nonlinear beam problem. This is the first study to use this method to solve a nonlinear structural acoustic problem. This study has two focuses: 1) the new version of the multilevel residue harmonic balance method can generate the higher-level nonlinear solutions ignored in the previous version and 2) the effect of the extended cavity, which has not been considered in previous studies, is examined. The cavity length of a panel-cavity system is sometimes longer than the panel length. However, many studies have adopted a model in which the cavity length is equal to the panel length. The effects of excitation magnitude, cavity depth, damping and number of structural modes on sound and vibration responses are investigated for various panel cases. In the simulations, the present harmonic balance solutions agree reasonably well with those obtained from the classical harmonic balance method. There are two important findings. First, the nonlinearity of a structural acoustic system highly depends on the cavity size. If the cavity size is smaller, the nonlinearity is higher. A large cavity volume implies a low stiffness or small acoustic pressure transmitted from the source panel to the nonlinear panel. In other words, the additional volume in an extended cavity affects the nonlinearity, sound and vibration responses of a structural acoustic system. Second, if an acoustic resonance couples with a structural resonance, nonlinearity is amplified and thus the insertion loss is adversely affected.
| S-EPMC6029766 | biostudies-literature
Project description:We propose a novel semi-supervised clustering method called GO Fuzzy c-means, which enables the simultaneous use of biological knowledge and gene expression data in a probabilistic clustering algorithm. Our method is based on the fuzzy c-means clustering algorithm and utilizes the Gene Ontology annotations as prior knowledge to guide the process of grouping functionally related genes. Unlike traditional clustering methods, our method is capable of assigning genes to multiple clusters, which is a more appropriate representation of the behavior of genes. Two datasets of yeast (Saccharomyces cerevisiae) expression profiles were applied to compare our method with other state-of-the-art clustering methods. Our experiments show that our method can produce far better biologically meaningful clusters even with the use of a small percentage of Gene Ontology annotations. In addition, our experiments further indicate that the utilization of prior knowledge in our method can predict gene functions effectively. The source code is freely available at http://sysbio.fulton.asu.edu/gofuzzy/.
| S-EPMC2673503 | biostudies-literature
Project description:MotivationWe propose a novel algorithm to combine multiple kernels and Laplacians for clustering analysis. The new algorithm is formulated on a Rayleigh quotient objective function and is solved as a bi-level alternating minimization procedure. Using the proposed algorithm, the coefficients of kernels and Laplacians can be optimized automatically.ResultsThree variants of the algorithm are proposed. The performance is systematically validated on two real-life data fusion applications. The proposed Optimized Kernel Laplacian Clustering (OKLC) algorithms perform significantly better than other methods. Moreover, the coefficients of kernels and Laplacians optimized by OKLC show some correlation with the rank of performance of individual data source. Though in our evaluation the K values are predefined, in practical studies, the optimal cluster number can be consistently estimated from the eigenspectrum of the combined kernel Laplacian matrix.AvailabilityThe MATLAB code of algorithms implemented in this paper is downloadable from http://homes.esat.kuleuven.be/~sistawww/bioi/syu/oklc.html.
| S-EPMC3008636 | biostudies-literature