9,573 research outputs found
A biophysical approach to large-scale protein-DNA binding data
About this book * Cutting-edge genome analysis methods from leading bioinformaticians An accurate description of current scientific developments in the field of bioinformatics and computational implementation is presented by research of the BioSapiens Network of Excellence. Bioinformatics is essential for annotating the structure and function of genes, proteins and the analysis of complete genomes and to molecular biology and biochemistry. Included is an overview of bioinformatics, the full spectrum of genome annotation approaches including; genome analysis and gene prediction, gene regulation analysis and expression, genome variation and QTL analysis, large scale protein annotation of function and structure, annotation and prediction of protein interactions, and the organization and annotation of molecular networks and biochemical pathways. Also covered is a technical framework to organize and represent genome data using the DAS technology and work in the annotation of two large genomic sets: HIV/HCV viral genomes and splicing alternatives potentially encoded in 1% of the human genome
Propositionalisation of multiple sequence alignments using probabilistic models
Multiple sequence alignments play a central role in Bioinformatics. Most alignment representations are designed to facilitate knowledge extraction by human experts. Additionally statistical models like Profile Hidden Markov Models are used as representations. They offer the advantage to provide sound, probabilistic scores. The basic idea we present in this paper is to use the structure of a Profile Hidden Markov Model for propositionalisation. This way we get a simple, extendable representation of multiple sequence alignments which facilitates further analysis by Machine Learning algorighms
DeepCare: A Deep Dynamic Memory Model for Predictive Medicine
Personalized predictive medicine necessitates the modeling of patient illness
and care processes, which inherently have long-term temporal dependencies.
Healthcare observations, recorded in electronic medical records, are episodic
and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural
network that reads medical records, stores previous illness history, infers
current illness states and predicts future medical outcomes. At the data level,
DeepCare represents care episodes as vectors in space, models patient health
state trajectories through explicit memory of historical records. Built on Long
Short-Term Memory (LSTM), DeepCare introduces time parameterizations to handle
irregular timed events by moderating the forgetting and consolidation of memory
cells. DeepCare also incorporates medical interventions that change the course
of illness and shape future medical risk. Moving up to the health state level,
historical and present health states are then aggregated through multiscale
temporal pooling, before passing through a neural network that estimates future
outcomes. We demonstrate the efficacy of DeepCare for disease progression
modeling, intervention recommendation, and future risk prediction. On two
important cohorts with heavy social and economic burden -- diabetes and mental
health -- the results show improved modeling and risk prediction accuracy.Comment: Accepted at JBI under the new name: "Predicting healthcare
trajectories from medical records: A deep learning approach
Distributions associated with general runs and patterns in hidden Markov models
This paper gives a method for computing distributions associated with
patterns in the state sequence of a hidden Markov model, conditional on
observing all or part of the observation sequence. Probabilities are computed
for very general classes of patterns (competing patterns and generalized later
patterns), and thus, the theory includes as special cases results for a large
class of problems that have wide application. The unobserved state sequence is
assumed to be Markovian with a general order of dependence. An auxiliary Markov
chain is associated with the state sequence and is used to simplify the
computations. Two examples are given to illustrate the use of the methodology.
Whereas the first application is more to illustrate the basic steps in applying
the theory, the second is a more detailed application to DNA sequences, and
shows that the methods can be adapted to include restrictions related to
biological knowledge.Comment: Published in at http://dx.doi.org/10.1214/07-AOAS125 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
- …