16,856 research outputs found
Rank discriminants for predicting phenotypes from RNA expression
Statistical methods for analyzing large-scale biomolecular data are
commonplace in computational biology. A notable example is phenotype prediction
from gene expression data, for instance, detecting human cancers,
differentiating subtypes and predicting clinical outcomes. Still, clinical
applications remain scarce. One reason is that the complexity of the decision
rules that emerge from standard statistical learning impedes biological
understanding, in particular, any mechanistic interpretation. Here we explore
decision rules for binary classification utilizing only the ordering of
expression among several genes; the basic building blocks are then two-gene
expression comparisons. The simplest example, just one comparison, is the TSP
classifier, which has appeared in a variety of cancer-related discovery
studies. Decision rules based on multiple comparisons can better accommodate
class heterogeneity, and thereby increase accuracy, and might provide a link
with biological mechanism. We consider a general framework ("rank-in-context")
for designing discriminant functions, including a data-driven selection of the
number and identity of the genes in the support ("context"). We then specialize
to two examples: voting among several pairs and comparing the median expression
in two groups of genes. Comprehensive experiments assess accuracy relative to
other, more complex, methods, and reinforce earlier observations that simple
classifiers are competitive.Comment: Published in at http://dx.doi.org/10.1214/14-AOAS738 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Community detection for networks with unipartite and bipartite structure
Finding community structures in networks is important in network science,
technology, and applications. To date, most algorithms that aim to find
community structures only focus either on unipartite or bipartite networks. A
unipartite network consists of one set of nodes and a bipartite network
consists of two nonoverlapping sets of nodes with only links joining the nodes
in different sets. However, a third type of network exists, defined here as the
mixture network. Just like a bipartite network, a mixture network also consists
of two sets of nodes, but some nodes may simultaneously belong to two sets,
which breaks the nonoverlapping restriction of a bipartite network. The mixture
network can be considered as a general case, with unipartite and bipartite
networks viewed as its limiting cases. A mixture network can represent not only
all the unipartite and bipartite networks, but also a wide range of real-world
networks that cannot be properly represented as either unipartite or bipartite
networks in fields such as biology and social science. Based on this
observation, we first propose a probabilistic model that can find modules in
unipartite, bipartite, and mixture networks in a unified framework based on the
link community model for a unipartite undirected network [B Ball et al (2011
Phys. Rev. E 84 036103)]. We test our algorithm on synthetic networks (both
overlapping and nonoverlapping communities) and apply it to two real-world
networks: a southern women bipartite network and a human transcriptional
regulatory mixture network. The results suggest that our model performs well
for all three types of networks, is competitive with other algorithms for
unipartite or bipartite networks, and is applicable to real-world networks.Comment: 27 pages, 8 figures.
(http://iopscience.iop.org/1367-2630/16/9/093001
Determining the Rationality of Marketing Strategy on Farms
The focus of farm management, as a discipline, has reflected historically the assumption that farms are embedded in near-perfectly competitive market structures. The common validity of this assumption is plain. As open systems, farms have asymmetric relationships with their environment: they are significantly more influenced by it than influencing it. However, farmers seem often not to appreciate the implications of this for their management options. Nor, arguably, is the farm management discipline yet well equipped to analyse initiatives that farmers might contemplate to enhance their control over market outcomes, specifically, as a means of exerting greater control over business performance. In this paper a framework for the analysis of the prospects for product differentiation of farm output is presented in an attempt to fill this lacuna. Introduction As an academic discipline, historically farm management (FM) has been focused on management decision making (Charry and Parton 2002). The domain of physical agricultural production activities may have been taught within farm management qualifications, but the discipline has persistently involved analysis for decisions. Within it farms are characterised as purposeful, open, complex systems having to cope with substantial stochasticity (Dillon 1992). Economics has been the discipline used to most effect to analyse farm management decisions (Malcolm 2004).Farm Management,
An efficient and principled method for detecting communities in networks
A fundamental problem in the analysis of network data is the detection of
network communities, groups of densely interconnected nodes, which may be
overlapping or disjoint. Here we describe a method for finding overlapping
communities based on a principled statistical approach using generative network
models. We show how the method can be implemented using a fast, closed-form
expectation-maximization algorithm that allows us to analyze networks of
millions of nodes in reasonable running times. We test the method both on
real-world networks and on synthetic benchmarks and find that it gives results
competitive with previous methods. We also show that the same approach can be
used to extract nonoverlapping community divisions via a relaxation method, and
demonstrate that the algorithm is competitively fast and accurate for the
nonoverlapping problem.Comment: 14 pages, 5 figures, 1 tabl
The impact of horizontal mergers on rivals: Gains to being left outside a merger
It is commonly perceived that firms do not want to be outsiders to a merger between competitor firms. We instead argue that it is beneficial to be a non-merging rival firm to a large horizontal merger. Using a sample of mergers with expert-identification of relevant rivals and the event-study methodology, we find rivals generally experience positive abnormal returns at the merger announcement date. Further, we find that the stock reaction of rivals to merger events is not sensitive to merger waves; hence, 'future acquisition probability' does not drive the positive abnormal returns of rivals. We then build a conceptual framework that encompasses the impact of merger events on both merging and rival firms in order to provide a schematic to elicit more information on merger type
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