9,123 research outputs found
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
An Unsupervised Approach for Automatic Activity Recognition based on Hidden Markov Model Regression
Using supervised machine learning approaches to recognize human activities
from on-body wearable accelerometers generally requires a large amount of
labelled data. When ground truth information is not available, too expensive,
time consuming or difficult to collect, one has to rely on unsupervised
approaches. This paper presents a new unsupervised approach for human activity
recognition from raw acceleration data measured using inertial wearable
sensors. The proposed method is based upon joint segmentation of
multidimensional time series using a Hidden Markov Model (HMM) in a multiple
regression context. The model is learned in an unsupervised framework using the
Expectation-Maximization (EM) algorithm where no activity labels are needed.
The proposed method takes into account the sequential appearance of the data.
It is therefore adapted for the temporal acceleration data to accurately detect
the activities. It allows both segmentation and classification of the human
activities. Experimental results are provided to demonstrate the efficiency of
the proposed approach with respect to standard supervised and unsupervised
classification approache
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
Handwritten digit recognition by bio-inspired hierarchical networks
The human brain processes information showing learning and prediction
abilities but the underlying neuronal mechanisms still remain unknown.
Recently, many studies prove that neuronal networks are able of both
generalizations and associations of sensory inputs. In this paper, following a
set of neurophysiological evidences, we propose a learning framework with a
strong biological plausibility that mimics prominent functions of cortical
circuitries. We developed the Inductive Conceptual Network (ICN), that is a
hierarchical bio-inspired network, able to learn invariant patterns by
Variable-order Markov Models implemented in its nodes. The outputs of the
top-most node of ICN hierarchy, representing the highest input generalization,
allow for automatic classification of inputs. We found that the ICN clusterized
MNIST images with an error of 5.73% and USPS images with an error of 12.56%
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