55,756 research outputs found
Genetic algorithm based two-mode clustering of metabolomics data
Metabolomics and other omics tools are generally characterized by large data sets with many variables obtained under different environmental conditions. Clustering methods and more specifically two-mode clustering methods are excellent tools for analyzing this type of data. Two-mode clustering methods allow for analysis of the behavior of subsets of metabolites under different experimental conditions. In addition, the results are easily visualized. In this paper we introduce a two-mode clustering method based on a genetic algorithm that uses a criterion that searches for homogeneous clusters. Furthermore we introduce a cluster stability criterion to validate the clusters and we provide an extended knee plot to select the optimal number of clusters in both experimental and metabolite modes. The genetic algorithm-based two-mode clustering gave biological relevant results when it was applied to two real life metabolomics data sets. It was, for instance, able to identify a catabolic pathway for growth on several of the carbon sources
Sparse Probit Linear Mixed Model
Linear Mixed Models (LMMs) are important tools in statistical genetics. When
used for feature selection, they allow to find a sparse set of genetic traits
that best predict a continuous phenotype of interest, while simultaneously
correcting for various confounding factors such as age, ethnicity and
population structure. Formulated as models for linear regression, LMMs have
been restricted to continuous phenotypes. We introduce the Sparse Probit Linear
Mixed Model (Probit-LMM), where we generalize the LMM modeling paradigm to
binary phenotypes. As a technical challenge, the model no longer possesses a
closed-form likelihood function. In this paper, we present a scalable
approximate inference algorithm that lets us fit the model to high-dimensional
data sets. We show on three real-world examples from different domains that in
the setup of binary labels, our algorithm leads to better prediction accuracies
and also selects features which show less correlation with the confounding
factors.Comment: Published version, 21 pages, 6 figure
Feature Selection of Post-Graduation Income of College Students in the United States
This study investigated the most important attributes of the 6-year
post-graduation income of college graduates who used financial aid during their
time at college in the United States. The latest data released by the United
States Department of Education was used. Specifically, 1,429 cohorts of
graduates from three years (2001, 2003, and 2005) were included in the data
analysis. Three attribute selection methods, including filter methods, forward
selection, and Genetic Algorithm, were applied to the attribute selection from
30 relevant attributes. Five groups of machine learning algorithms were applied
to the dataset for classification using the best selected attribute subsets.
Based on our findings, we discuss the role of neighborhood professional degree
attainment, parental income, SAT scores, and family college education in
post-graduation incomes and the implications for social stratification.Comment: 14 pages, 6 tables, 3 figure
Data mining as a tool for environmental scientists
Over recent years a huge library of data mining algorithms has been developed to tackle a variety of problems in fields such as medical imaging and network traffic analysis. Many of these techniques are far more flexible than more classical modelling approaches and could be usefully applied to data-rich environmental problems. Certain techniques such as Artificial Neural Networks, Clustering, Case-Based Reasoning and more recently Bayesian Decision Networks have found application in environmental modelling while other methods, for example classification and association rule extraction, have not yet been taken up on any wide scale. We propose that these and other data mining techniques could be usefully applied to difficult problems in the field. This paper introduces several data mining concepts and briefly discusses their application to environmental modelling, where data may be sparse, incomplete, or heterogenous
Biomedical Informatics Applications for Precision Management of Neurodegenerative Diseases
Modern medicine is in the midst of a revolution driven by ābig data,ā rapidly advancing computing power, and broader integration of technology into healthcare. Highly detailed and individualized profiles of both health and disease states are now possible, including biomarkers, genomic profiles, cognitive and behavioral phenotypes, high-frequency assessments, and medical imaging. Although these data are incredibly complex, they can potentially be used to understand multi-determinant causal relationships, elucidate modifiable factors, and ultimately customize treatments based on individual parameters. Especially for neurodegenerative diseases, where an effective therapeutic agent has yet to be discovered, there remains a critical need for an interdisciplinary perspective on data and information management due to the number of unanswered questions. Biomedical informatics is a multidisciplinary field that falls at the intersection of information technology, computer and data science, engineering, and healthcare that will be instrumental for uncovering novel insights into neurodegenerative disease research, including both causal relationships and therapeutic targets and maximizing the utility of both clinical and research data. The present study aims to provide a brief overview of biomedical informatics and how clinical data applications such as clinical decision support tools can be developed to derive new knowledge from the wealth of available data to advance clinical care and scientific research of neurodegenerative diseases in the era of precision medicine
Distributed classifier based on genetically engineered bacterial cell cultures
We describe a conceptual design of a distributed classifier formed by a
population of genetically engineered microbial cells. The central idea is to
create a complex classifier from a population of weak or simple classifiers. We
create a master population of cells with randomized synthetic biosensor
circuits that have a broad range of sensitivities towards chemical signals of
interest that form the input vectors subject to classification. The randomized
sensitivities are achieved by constructing a library of synthetic gene circuits
with randomized control sequences (e.g. ribosome-binding sites) in the front
element. The training procedure consists in re-shaping of the master population
in such a way that it collectively responds to the "positive" patterns of input
signals by producing above-threshold output (e.g. fluorescent signal), and
below-threshold output in case of the "negative" patterns. The population
re-shaping is achieved by presenting sequential examples and pruning the
population using either graded selection/counterselection or by
fluorescence-activated cell sorting (FACS). We demonstrate the feasibility of
experimental implementation of such system computationally using a realistic
model of the synthetic sensing gene circuits.Comment: 31 pages, 9 figure
Accelerated Particle Swarm Optimization and Support Vector Machine for Business Optimization and Applications
Business optimization is becoming increasingly important because all business
activities aim to maximize the profit and performance of products and services,
under limited resources and appropriate constraints. Recent developments in
support vector machine and metaheuristics show many advantages of these
techniques. In particular, particle swarm optimization is now widely used in
solving tough optimization problems. In this paper, we use a combination of a
recently developed Accelerated PSO and a nonlinear support vector machine to
form a framework for solving business optimization problems. We first apply the
proposed APSO-SVM to production optimization, and then use it for income
prediction and project scheduling. We also carry out some parametric studies
and discuss the advantages of the proposed metaheuristic SVM.Comment: 12 page
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