55,153 research outputs found
SPECS: a non-parametric method to identify tissue-specific molecular features for unbalanced sample groups
Background
To understand biology and differences among various tissues or cell types, one typically searches for molecular features that display characteristic abundance patterns. Several specificity metrics have been introduced to identify tissue-specific molecular features, but these either require an equal number of replicates per tissue or they can’t handle replicates at all.
Results
We describe a non-parametric specificity score that is compatible with unequal sample group sizes. To demonstrate its usefulness, the specificity score was calculated on all GTEx samples, detecting known and novel tissue-specific genes. A webtool was developed to browse these results for genes or tissues of interest. An example python implementation of SPECS is available at https://github.com/celineeveraert/SPECS. The precalculated SPECS results on the GTEx data are available through a user-friendly browser at specs.cmgg.be.
Conclusions
SPECS is a non-parametric method that identifies known and novel specific-expressed genes. In addition, SPECS could be adopted for other features and applications
Structure Learning in Nested Effects Models
Nested Effects Models (NEMs) are a class of graphical models introduced to
analyze the results of gene perturbation screens. NEMs explore noisy subset
relations between the high-dimensional outputs of phenotyping studies, e.g. the
effects showing in gene expression profiles or as morphological features of the
perturbed cell.
In this paper we expand the statistical basis of NEMs in four directions:
First, we derive a new formula for the likelihood function of a NEM, which
generalizes previous results for binary data. Second, we prove model
identifiability under mild assumptions. Third, we show that the new formulation
of the likelihood allows to efficiently traverse model space. Fourth, we
incorporate prior knowledge and an automated variable selection criterion to
decrease the influence of noise in the data
Primary visual cortex as a saliency map: parameter-free prediction of behavior from V1 physiology
It has been hypothesized that neural activities in the primary visual cortex
(V1) represent a saliency map of the visual field to exogenously guide
attention. This hypothesis has so far provided only qualitative predictions and
their confirmations. We report this hypothesis' first quantitative prediction,
derived without free parameters, and its confirmation by human behavioral data.
The hypothesis provides a direct link between V1 neural responses to a visual
location and the saliency of that location to guide attention exogenously. In a
visual input containing many bars, one of them saliently different from all the
other bars which are identical to each other, saliency at the singleton's
location can be measured by the shortness of the reaction time in a visual
search task to find the singleton. The hypothesis predicts quantitatively the
whole distribution of the reaction times to find a singleton unique in color,
orientation, and motion direction from the reaction times to find other types
of singletons. The predicted distribution matches the experimentally observed
distribution in all six human observers. A requirement for this successful
prediction is a data-motivated assumption that V1 lacks neurons tuned
simultaneously to color, orientation, and motion direction of visual inputs.
Since evidence suggests that extrastriate cortices do have such neurons, we
discuss the possibility that the extrastriate cortices play no role in guiding
exogenous attention so that they can be devoted to other functional roles like
visual decoding or endogenous attention.Comment: 11 figures, 66 page
Growth rings in tropical trees : role of functional traits, environment, and phylogeny
Acknowledgments Financial support of the Centre National de la Recherche Scientifique (USR 3330), France, and from the Rufford Small Grants Foundation (UK) is acknowledged. We thank the private farmers and coffee plantation companies of Kodagu for providing permissions and logistical support for this project. We are grateful to N. Barathan for assistance with slide preparation and data entry, S. Aravajy for botanical assistance, S. Prasad and G. Orukaimoni for technical inputs, and A. Prathap, S. Shiva, B. Saravana, and P. Shiva for field assistance. The corresponding editor and three anonymous reviewers provided insightful comments that improved the manuscript.Peer reviewedPostprin
Evaluating Leniency with Missing Information on Undetected Cartels: Exploring Time-Varying Policy Impacts on Cartel Duration
This paper examines the effects of European Commission’s (EC) new leniency program on the EC’s capabilities in detecting and deterring cartels. As a supplementary analysis, the US leniency is studied. I discuss a dynamic model of cartel formation and dissolution to illustrate how changes in antitrust policies and economic conditions might affect cartel duration. Comparative statics results are then corroborated with empirical estimates of hazard functions adjusted to account for both the heterogeneity of cartels and the time-varying policy impacts suggested by theory. Contrary to earlier studies, my statistical tests are consistent with the theoretic predictions that following an efficacious leniency program, the average duration of discovered cartels rises in the short run and falls in the long run. The results shed light on the design of enforcement programs against cartels and other forms of conspiracy
Distribution of Mutual Information from Complete and Incomplete Data
Mutual information is widely used, in a descriptive way, to measure the
stochastic dependence of categorical random variables. In order to address
questions such as the reliability of the descriptive value, one must consider
sample-to-population inferential approaches. This paper deals with the
posterior distribution of mutual information, as obtained in a Bayesian
framework by a second-order Dirichlet prior distribution. The exact analytical
expression for the mean, and analytical approximations for the variance,
skewness and kurtosis are derived. These approximations have a guaranteed
accuracy level of the order O(1/n^3), where n is the sample size. Leading order
approximations for the mean and the variance are derived in the case of
incomplete samples. The derived analytical expressions allow the distribution
of mutual information to be approximated reliably and quickly. In fact, the
derived expressions can be computed with the same order of complexity needed
for descriptive mutual information. This makes the distribution of mutual
information become a concrete alternative to descriptive mutual information in
many applications which would benefit from moving to the inductive side. Some
of these prospective applications are discussed, and one of them, namely
feature selection, is shown to perform significantly better when inductive
mutual information is used.Comment: 26 pages, LaTeX, 5 figures, 4 table
Prosody-Based Automatic Segmentation of Speech into Sentences and Topics
A crucial step in processing speech audio data for information extraction,
topic detection, or browsing/playback is to segment the input into sentence and
topic units. Speech segmentation is challenging, since the cues typically
present for segmenting text (headers, paragraphs, punctuation) are absent in
spoken language. We investigate the use of prosody (information gleaned from
the timing and melody of speech) for these tasks. Using decision tree and
hidden Markov modeling techniques, we combine prosodic cues with word-based
approaches, and evaluate performance on two speech corpora, Broadcast News and
Switchboard. Results show that the prosodic model alone performs on par with,
or better than, word-based statistical language models -- for both true and
automatically recognized words in news speech. The prosodic model achieves
comparable performance with significantly less training data, and requires no
hand-labeling of prosodic events. Across tasks and corpora, we obtain a
significant improvement over word-only models using a probabilistic combination
of prosodic and lexical information. Inspection reveals that the prosodic
models capture language-independent boundary indicators described in the
literature. Finally, cue usage is task and corpus dependent. For example, pause
and pitch features are highly informative for segmenting news speech, whereas
pause, duration and word-based cues dominate for natural conversation.Comment: 30 pages, 9 figures. To appear in Speech Communication 32(1-2),
Special Issue on Accessing Information in Spoken Audio, September 200
The reentry hypothesis: The putative interaction of the frontal eye field, ventrolateral prefrontal cortex, and areas V4, IT for attention and eye movement
Attention is known to play a key role in perception, including action selection, object recognition and memory. Despite findings revealing competitive interactions among cell populations, attention remains difficult to explain. The central purpose of this paper is to link up a large number of findings in a single computational approach. Our simulation results suggest that attention can be well explained on a network level involving many areas of the brain. We argue that attention is an emergent phenomenon that arises from reentry and competitive interactions. We hypothesize that guided visual search requires the usage of an object-specific template in prefrontal cortex to sensitize V4 and IT cells whose preferred stimuli match the target template. This induces a feature-specific bias and provides guidance for eye movements. Prior to an eye movement, a spatially organized reentry from occulomotor centers, specifically the movement cells of the frontal eye field, occurs and modulates the gain of V4 and IT cells. The processes involved are elucidated by quantitatively comparing the time course of simulated neural activity with experimental data. Using visual search tasks as an example, we provide clear and empirically testable predictions for the participation of IT, V4 and the frontal eye field in attention. Finally, we explain a possible physiological mechanism that can lead to non-flat search slopes as the result of a slow, parallel discrimination process
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