6,840 research outputs found
Kernel methods for in silico chemogenomics
Predicting interactions between small molecules and proteins is a crucial
ingredient of the drug discovery process. In particular, accurate predictive
models are increasingly used to preselect potential lead compounds from large
molecule databases, or to screen for side-effects. While classical in silico
approaches focus on predicting interactions with a given specific target, new
chemogenomics approaches adopt cross-target views. Building on recent
developments in the use of kernel methods in bio- and chemoinformatics, we
present a systematic framework to screen the chemical space of small molecules
for interaction with the biological space of proteins. We show that this
framework allows information sharing across the targets, resulting in a
dramatic improvement of ligand prediction accuracy for three important classes
of drug targets: enzymes, GPCR and ion channels
Epitope prediction improved by multitask support vector machines
Motivation: In silico methods for the prediction of antigenic peptides
binding to MHC class I molecules play an increasingly important role in the
identification of T-cell epitopes. Statistical and machine learning methods, in
particular, are widely used to score candidate epitopes based on their
similarity with known epitopes and non epitopes. The genes coding for the MHC
molecules, however, are highly polymorphic, and statistical methods have
difficulties to build models for alleles with few known epitopes. In this case,
recent works have demonstrated the utility of leveraging information across
alleles to improve the performance of the prediction. Results: We design a
support vector machine algorithm that is able to learn epitope models for all
alleles simultaneously, by sharing information across similar alleles. The
sharing of information across alleles is controlled by a user-defined measure
of similarity between alleles. We show that this similarity can be defined in
terms of supertypes, or more directly by comparing key residues known to play a
role in the peptide-MHC binding. We illustrate the potential of this approach
on various benchmark experiments where it outperforms other state-of-the-art
methods
Active skeleton for bacteria modeling
The investigation of spatio-temporal dynamics of bacterial cells and their
molecular components requires automated image analysis tools to track cell
shape properties and molecular component locations inside the cells. In the
study of bacteria aging, the molecular components of interest are protein
aggregates accumulated near bacteria boundaries. This particular location makes
very ambiguous the correspondence between aggregates and cells, since computing
accurately bacteria boundaries in phase-contrast time-lapse imaging is a
challenging task. This paper proposes an active skeleton formulation for
bacteria modeling which provides several advantages: an easy computation of
shape properties (perimeter, length, thickness, orientation), an improved
boundary accuracy in noisy images, and a natural bacteria-centered coordinate
system that permits the intrinsic location of molecular components inside the
cell. Starting from an initial skeleton estimate, the medial axis of the
bacterium is obtained by minimizing an energy function which incorporates
bacteria shape constraints. Experimental results on biological images and
comparative evaluation of the performances validate the proposed approach for
modeling cigar-shaped bacteria like Escherichia coli. The Image-J plugin of the
proposed method can be found online at http://fluobactracker.inrialpes.fr.Comment: Published in Computer Methods in Biomechanics and Biomedical
Engineering: Imaging and Visualizationto appear i
Clustered Multi-Task Learning: A Convex Formulation
In multi-task learning several related tasks are considered simultaneously,
with the hope that by an appropriate sharing of information across tasks, each
task may benefit from the others. In the context of learning linear functions
for supervised classification or regression, this can be achieved by including
a priori information about the weight vectors associated with the tasks, and
how they are expected to be related to each other. In this paper, we assume
that tasks are clustered into groups, which are unknown beforehand, and that
tasks within a group have similar weight vectors. We design a new spectral norm
that encodes this a priori assumption, without the prior knowledge of the
partition of tasks into groups, resulting in a new convex optimization
formulation for multi-task learning. We show in simulations on synthetic
examples and on the IEDB MHC-I binding dataset, that our approach outperforms
well-known convex methods for multi-task learning, as well as related non
convex methods dedicated to the same problem
Particle detection and tracking in fluorescence time-lapse imaging: a contrario approach
This paper proposes a probabilistic approach for the detection and the
tracking of particles in fluorescent time-lapse imaging. In the presence of a
very noised and poor-quality data, particles and trajectories can be
characterized by an a contrario model, that estimates the probability of
observing the structures of interest in random data. This approach, first
introduced in the modeling of human visual perception and then successfully
applied in many image processing tasks, leads to algorithms that neither
require a previous learning stage, nor a tedious parameter tuning and are very
robust to noise. Comparative evaluations against a well-established baseline
show that the proposed approach outperforms the state of the art.Comment: Published in Journal of Machine Vision and Application
Increasing stability and interpretability of gene expression signatures
Motivation : Molecular signatures for diagnosis or prognosis estimated from
large-scale gene expression data often lack robustness and stability, rendering
their biological interpretation challenging. Increasing the signature's
interpretability and stability across perturbations of a given dataset and, if
possible, across datasets, is urgently needed to ease the discovery of
important biological processes and, eventually, new drug targets. Results : We
propose a new method to construct signatures with increased stability and
easier interpretability. The method uses a gene network as side interpretation
and enforces a large connectivity among the genes in the signature, leading to
signatures typically made of genes clustered in a few subnetworks. It combines
the recently proposed graph Lasso procedure with a stability selection
procedure. We evaluate its relevance for the estimation of a prognostic
signature in breast cancer, and highlight in particular the increase in
interpretability and stability of the signature
La antiglobalizacion contra el desarrollo, la comunidad contra la sociedad Anti-globalization against development, community against society
Alimentados a menudo por la ecologĂa profunda, la antiglobalizaciĂłn encuentra actualmente muchos mĂ©ritos en las comunidades tradicionales más respetuosas de la "Mother earth". Esta corriente tambiĂ©n rehabilita posiciones conservadoras hasta reunirse en ciertos casos con pensamientos claramente reaccionarios.Often fed by deep ecology, currently the anti-globalization presents many merits in traditional communities respectful of the "Mother earth". This current also rehabilitates conservative positions to meet in certain cases clearly reactionary thoughts
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