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10/17/2018

Research Article

Predicting B cell receptor substitution profiles using public repertoire data

Given the public release of many high-quality large B cell receptor datasets, one may ask whether it is possible to use such data in a prediction model for clonal-family-specific substitution profiles. In this paper, Dhar, Davidsen et al. present the method “Substitution Profiles Using Related Families”, a penalized tensor regression framework that integrates information from a rich assemblage of datasets to predict the clonal-family-specific substitution profile for any single input sequence.

Image credit: Dhar, Davidsen et al.

Predicting B cell receptor substitution profiles using public repertoire data

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10/18/2018

Research Article

A multi-scale layer-resolved spiking network model of resting-state dynamics in macaque visual cortical areas

Since models of cortex tend to concentrate on a single scale, little is known about how cortical structure shapes the multi-scale dynamics of the network. Schmidt et al. present dynamical simulations of a multi-area network model at neuronal and synaptic resolution with population-specific connectivity based on extensive experimental data.

Image credit: Schmidt et al.

A multi-scale layer-resolved spiking network model of resting-state dynamics in macaque visual cortical areas

10/18/2018

Research Article

Virus and CTL dynamics in the extrafollicular and follicular tissue compartments in SIV-infected macaques

Recent data indicate that killer T cell responses, or CTL, show reduced homing to the follicular compartment of the lymphoid tissue, while the majority of the CTL remain in the extrafollicular site, which appears to contribute to the observed unequal distribution of virus load in the two locations. Here, these dynamics are studied with 2-compartment mathematical models.

Image credit: Wodarz et al.

Virus and CTL dynamics in the extrafollicular and follicular tissue compartments in SIV-infected macaques

10/17/2018

Research Article

Genomic data integration systematically biases interactome mapping

Identification of true protein-protein interactions within massive proteomic datasets is challenging. One widely used approach integrates proteomic data with publicly available functional genomics data, such as mRNA coexpression or protein coevolution, to define likely interactions. Here we report that this technique introduces systematic biases into the resulting protein interaction networks.

Genomic data integration systematically biases interactome mapping

Image credit: Skinnider et al.

10/13/2018

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Edward Hill

What to know before forecasting the flu - Informative perspective piece in @PLOSCompBiol presenting "a set of considerations for flu forecasters to take into account prior to applying forecasting algorithms."

https://journals.plos.org/...

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09/27/2018

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Antimicrobial Resistance

Antimicrobial Resistance is a serious threat to global public health that requires action across all sectors and society. This Channel is a global forum for Antimicrobial Resistance research. 


Antimicrobial Resistance

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11/07/2013

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About My Lab

'About My Lab' aims to share knowledge about lab organization and management. Each Perspective represents an interview with a Principal Investigator, who share their experience running a lab by discussing selected topics in an informal style.

About My Lab

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