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PLoS Computational Biology

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https://www.readbyqxmd.com/read/28821015/extracting-replicable-associations-across-multiple-studies-empirical-bayes-algorithms-for-controlling-the-false-discovery-rate
#1
David Amar, Ron Shamir, Daniel Yekutieli
In almost every field in genomics, large-scale biomedical datasets are used to report associations. Extracting associations that recur across multiple studies while controlling the false discovery rate is a fundamental challenge. Here, we propose a new method to allow joint analysis of multiple studies. Given a set of p-values obtained from each study, the goal is to identify associations that recur in at least k > 1 studies while controlling the false discovery rate. We propose several new algorithms that differ in how the study dependencies are modeled, and compare them and extant methods under various simulated scenarios...
August 18, 2017: PLoS Computational Biology
https://www.readbyqxmd.com/read/28821014/efficient-and-accurate-causal-inference-with-hidden-confounders-from-genome-transcriptome-variation-data
#2
Lingfei Wang, Tom Michoel
Mapping gene expression as a quantitative trait using whole genome-sequencing and transcriptome analysis allows to discover the functional consequences of genetic variation. We developed a novel method and ultra-fast software Findr for higly accurate causal inference between gene expression traits using cis-regulatory DNA variations as causal anchors, which improves current methods by taking into consideration hidden confounders and weak regulations. Findr outperformed existing methods on the DREAM5 Systems Genetics challenge and on the prediction of microRNA and transcription factor targets in human lymphoblastoid cells, while being nearly a million times faster...
August 18, 2017: PLoS Computational Biology
https://www.readbyqxmd.com/read/28821012/multidomain-analyses-of-a-longitudinal-human-microbiome-intestinal-cleanout-perturbation-experiment
#3
Julia Fukuyama, Laurie Rumker, Kris Sankaran, Pratheepa Jeganathan, Les Dethlefsen, David A Relman, Susan P Holmes
Our work focuses on the stability, resilience, and response to perturbation of the bacterial communities in the human gut. Informative flash flood-like disturbances that eliminate most gastrointestinal biomass can be induced using a clinically-relevant iso-osmotic agent. We designed and executed such a disturbance in human volunteers using a dense longitudinal sampling scheme extending before and after induced diarrhea. This experiment has enabled a careful multidomain analysis of a controlled perturbation of the human gut microbiota with a new level of resolution...
August 18, 2017: PLoS Computational Biology
https://www.readbyqxmd.com/read/28806730/a-systems-approach-reveals-distinct-metabolic-strategies-among-the-nci-60-cancer-cell-lines
#4
Maike K Aurich, Ronan M T Fleming, Ines Thiele
The metabolic phenotype of cancer cells is reflected by the metabolites they consume and by the byproducts they release. Here, we use quantitative, extracellular metabolomic data of the NCI-60 panel and a novel computational method to generate 120 condition-specific cancer cell line metabolic models. These condition-specific cancer models used distinct metabolic strategies to generate energy and cofactors. The analysis of the models' capability to deal with environmental perturbations revealed three oxotypes, differing in the range of allowable oxygen uptake rates...
August 14, 2017: PLoS Computational Biology
https://www.readbyqxmd.com/read/28800597/confirmation-bias-in-human-reinforcement-learning-evidence-from-counterfactual-feedback-processing
#5
Stefano Palminteri, Germain Lefebvre, Emma J Kilford, Sarah-Jayne Blakemore
Previous studies suggest that factual learning, that is, learning from obtained outcomes, is biased, such that participants preferentially take into account positive, as compared to negative, prediction errors. However, whether or not the prediction error valence also affects counterfactual learning, that is, learning from forgone outcomes, is unknown. To address this question, we analysed the performance of two groups of participants on reinforcement learning tasks using a computational model that was adapted to test if prediction error valence influences learning...
August 11, 2017: PLoS Computational Biology
https://www.readbyqxmd.com/read/28817622/ten-simple-rules-for-getting-the-most-out-of-a-summer-laboratory-internship
#6
EDITORIAL
Toby P Aicher, Dániel L Barabási, Benjamin D Harris, Ajay Nadig, Kaitlin L Williams
No abstract text is available yet for this article.
August 2017: PLoS Computational Biology
https://www.readbyqxmd.com/read/28817586/ten-simple-rules-to-initiate-and-run-a-postdoctoral-association
#7
EDITORIAL
Chiara Bruckmann, Endre Sebestyén
No abstract text is available yet for this article.
August 2017: PLoS Computational Biology
https://www.readbyqxmd.com/read/28817584/the-application-of-project-based-learning-in-bioinformatics-training
#8
Laura R Emery, Sarah L Morgan
No abstract text is available yet for this article.
August 2017: PLoS Computational Biology
https://www.readbyqxmd.com/read/28817568/an-optimal-strategy-for-epilepsy-surgery-disruption-of-the-rich-club
#9
Marinho A Lopes, Mark P Richardson, Eugenio Abela, Christian Rummel, Kaspar Schindler, Marc Goodfellow, John R Terry
Surgery is a therapeutic option for people with epilepsy whose seizures are not controlled by anti-epilepsy drugs. In pre-surgical planning, an array of data modalities, often including intra-cranial EEG, is used in an attempt to map regions of the brain thought to be crucial for the generation of seizures. These regions are then resected with the hope that the individual is rendered seizure free as a consequence. However, post-operative seizure freedom is currently sub-optimal, suggesting that the pre-surgical assessment may be improved by taking advantage of a mechanistic understanding of seizure generation in large brain networks...
August 2017: PLoS Computational Biology
https://www.readbyqxmd.com/read/28817561/comparing-efficacies-of-moxifloxacin-levofloxacin-and-gatifloxacin-in-tuberculosis-granulomas-using-a-multi-scale-systems-pharmacology-approach
#10
Elsje Pienaar, Jansy Sarathy, Brendan Prideaux, Jillian Dietzold, Véronique Dartois, Denise E Kirschner, Jennifer J Linderman
Granulomas are complex lung lesions that are the hallmark of tuberculosis (TB). Understanding antibiotic dynamics within lung granulomas will be vital to improving and shortening the long course of TB treatment. Three fluoroquinolones (FQs) are commonly prescribed as part of multi-drug resistant TB therapy: moxifloxacin (MXF), levofloxacin (LVX) or gatifloxacin (GFX). To date, insufficient data are available to support selection of one FQ over another, or to show that these drugs are clinically equivalent. To predict the efficacy of MXF, LVX and GFX at a single granuloma level, we integrate computational modeling with experimental datasets into a single mechanistic framework, GranSim...
August 2017: PLoS Computational Biology
https://www.readbyqxmd.com/read/28792499/revisiting-chemoaffinity-theory-chemotactic-implementation-of-topographic-axonal-projection
#11
Honda Naoki
Neural circuits are wired by chemotactic migration of growth cones guided by extracellular guidance cue gradients. How growth cone chemotaxis builds the macroscopic structure of the neural circuit is a fundamental question in neuroscience. I addressed this issue in the case of the ordered axonal projections called topographic maps in the retinotectal system. In the retina and tectum, the erythropoietin-producing hepatocellular (Eph) receptors and their ligands, the ephrins, are expressed in gradients. According to Sperry's chemoaffinity theory, gradients in both the source and target areas enable projecting axons to recognize their proper terminals, but how axons chemotactically decode their destinations is largely unknown...
August 2017: PLoS Computational Biology
https://www.readbyqxmd.com/read/28792496/stabilization-of-diastolic-calcium-signal-via-calcium-pump-regulation-of-complex-local-calcium-releases-and-transient-decay-in-a-computational-model-of-cardiac-pacemaker-cell-with-individual-release-channels
#12
Alexander V Maltsev, Victor A Maltsev, Michael D Stern
Intracellular Local Ca releases (LCRs) from sarcoplasmic reticulum (SR) regulate cardiac pacemaker cell function by activation of electrogenic Na/Ca exchanger (NCX) during diastole. Prior studies demonstrated the existence of powerful compensatory mechanisms of LCR regulation via a complex local cross-talk of Ca pump, release and NCX. One major obstacle to study these mechanisms is that LCR exhibit complex Ca release propagation patterns (including merges and separations) that have not been characterized. Here we developed new terminology, classification, and computer algorithms for automatic detection of numerically simulated LCRs and examined LCR regulation by SR Ca pumping rate (Pup) that provides a major contribution to fight-or-flight response...
August 2017: PLoS Computational Biology
https://www.readbyqxmd.com/read/28787447/a-stochastic-field-description-of-finite-size-spiking-neural-networks
#13
Grégory Dumont, Alexandre Payeur, André Longtin
Neural network dynamics are governed by the interaction of spiking neurons. Stochastic aspects of single-neuron dynamics propagate up to the network level and shape the dynamical and informational properties of the population. Mean-field models of population activity disregard the finite-size stochastic fluctuations of network dynamics and thus offer a deterministic description of the system. Here, we derive a stochastic partial differential equation (SPDE) describing the temporal evolution of the finite-size refractory density, which represents the proportion of neurons in a given refractory state at any given time...
August 2017: PLoS Computational Biology
https://www.readbyqxmd.com/read/28787438/computational-experimental-approach-to-drug-target-interaction-mapping-a-case-study-on-kinase-inhibitors
#14
Anna Cichonska, Balaguru Ravikumar, Elina Parri, Sanna Timonen, Tapio Pahikkala, Antti Airola, Krister Wennerberg, Juho Rousu, Tero Aittokallio
Due to relatively high costs and labor required for experimental profiling of the full target space of chemical compounds, various machine learning models have been proposed as cost-effective means to advance this process in terms of predicting the most potent compound-target interactions for subsequent verification. However, most of the model predictions lack direct experimental validation in the laboratory, making their practical benefits for drug discovery or repurposing applications largely unknown. Here, we therefore introduce and carefully test a systematic computational-experimental framework for the prediction and pre-clinical verification of drug-target interactions using a well-established kernel-based regression algorithm as the prediction model...
August 2017: PLoS Computational Biology
https://www.readbyqxmd.com/read/28771616/quantification-of-transplant-derived-circulating-cell-free-dna-in-absence-of-a-donor-genotype
#15
Eilon Sharon, Hao Shi, Sandhya Kharbanda, Winston Koh, Lance R Martin, Kiran K Khush, Hannah Valantine, Jonathan K Pritchard, Iwijn De Vlaminck
Quantification of cell-free DNA (cfDNA) in circulating blood derived from a transplanted organ is a powerful approach to monitoring post-transplant injury. Genome transplant dynamics (GTD) quantifies donor-derived cfDNA (dd-cfDNA) by taking advantage of single-nucleotide polymorphisms (SNPs) distributed across the genome to discriminate donor and recipient DNA molecules. In its current implementation, GTD requires genotyping of both the transplant recipient and donor. However, in practice, donor genotype information is often unavailable...
August 2017: PLoS Computational Biology
https://www.readbyqxmd.com/read/28771581/measuring-distance-through-dense-weighted-networks-the-case-of-hospital-associated-pathogens
#16
Tjibbe Donker, Timo Smieszek, Katherine L Henderson, Alan P Johnson, A Sarah Walker, Julie V Robotham
Hospital networks, formed by patients visiting multiple hospitals, affect the spread of hospital-associated infections, resulting in differences in risks for hospitals depending on their network position. These networks are increasingly used to inform strategies to prevent and control the spread of hospital-associated pathogens. However, many studies only consider patients that are received directly from the initial hospital, without considering the effect of indirect trajectories through the network. We determine the optimal way to measure the distance between hospitals within the network, by reconstructing the English hospital network based on shared patients in 2014-2015, and simulating the spread of a hospital-associated pathogen between hospitals, taking into consideration that each intermediate hospital conveys a delay in the further spread of the pathogen...
August 2017: PLoS Computational Biology
https://www.readbyqxmd.com/read/28771570/multi-scale-approaches-for-high-speed-imaging-and-analysis-of-large-neural-populations
#17
Johannes Friedrich, Weijian Yang, Daniel Soudry, Yu Mu, Misha B Ahrens, Rafael Yuste, Darcy S Peterka, Liam Paninski
Progress in modern neuroscience critically depends on our ability to observe the activity of large neuronal populations with cellular spatial and high temporal resolution. However, two bottlenecks constrain efforts towards fast imaging of large populations. First, the resulting large video data is challenging to analyze. Second, there is an explicit tradeoff between imaging speed, signal-to-noise, and field of view: with current recording technology we cannot image very large neuronal populations with simultaneously high spatial and temporal resolution...
August 2017: PLoS Computational Biology
https://www.readbyqxmd.com/read/28767650/the-stochastic-early-reaction-inhibition-and-late-action-seria-model-for-antisaccades
#18
Eduardo A Aponte, Dario Schöbi, Klaas E Stephan, Jakob Heinzle
The antisaccade task is a classic paradigm used to study the voluntary control of eye movements. It requires participants to suppress a reactive eye movement to a visual target and to concurrently initiate a saccade in the opposite direction. Although several models have been proposed to explain error rates and reaction times in this task, no formal model comparison has yet been performed. Here, we describe a Bayesian modeling approach to the antisaccade task that allows us to formally compare different models on the basis of their evidence...
August 2017: PLoS Computational Biology
https://www.readbyqxmd.com/read/28767646/a-model-of-human-motor-sequence-learning-explains-facilitation-and-interference-effects-based-on-spike-timing-dependent-plasticity
#19
Quan Wang, Constantin A Rothkopf, Jochen Triesch
The ability to learn sequential behaviors is a fundamental property of our brains. Yet a long stream of studies including recent experiments investigating motor sequence learning in adult human subjects have produced a number of puzzling and seemingly contradictory results. In particular, when subjects have to learn multiple action sequences, learning is sometimes impaired by proactive and retroactive interference effects. In other situations, however, learning is accelerated as reflected in facilitation and transfer effects...
August 2017: PLoS Computational Biology
https://www.readbyqxmd.com/read/28767643/bow-tie-signaling-in-c-di-gmp-machine-learning-in-a-simple-biochemical-network
#20
Jinyuan Yan, Maxime Deforet, Kerry E Boyle, Rayees Rahman, Raymond Liang, Chinweike Okegbe, Lars E P Dietrich, Weigang Qiu, Joao B Xavier
Bacteria of many species rely on a simple molecule, the intracellular secondary messenger c-di-GMP (Bis-(3'-5')-cyclic dimeric guanosine monophosphate), to make a vital choice: whether to stay in one place and form a biofilm, or to leave it in search of better conditions. The c-di-GMP network has a bow-tie shaped architecture that integrates many signals from the outside world-the input stimuli-into intracellular c-di-GMP levels that then regulate genes for biofilm formation or for swarming motility-the output phenotypes...
August 2017: PLoS Computational Biology
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