Biomedical subjects

Y Chauvin

Publications and source records attributed to Y Chauvin.

3 recordsLinked to original sources

PubMed1995

Periodic sequence patterns in human exons.

We analyse the sequential structure of human exons and their flanking introns by hidden Markov models. Together, models of donor site regions, acceptor site regions and flanked internal exons, show that exons--besides the reading frame--hold a specific periodic pattern. The pattern, which has the consensus: non-T(A/T)G and a minimal periodicity of roughly 10 nucleotides, is not a consequence of the nucleotide statistics in the three codon positions, nor of the well known nucleosome positioning signal. We discuss the relation between the pattern and other known sequence elements responsible for the intrinsic bending or curvature of DNA.

Base Sequence↗

PubMed1995

Protein modeling with hybrid Hidden Markov Model/neural network architectures.

Hidden Markov Models (HMMs) are useful in a number of tasks in computational molecular biology, and in particular to model and align protein families. We argue that HMMs are somewhat optimal within a certain modeling hierarchy. Single first order HMMs, however, have two potential limitations: a large number of unstructured parameters, and a built-in inability to deal with long-range dependencies. Hybrid HMM/Neural Network (NN) architectures attempt to overcome these limitations. In hybrid HMM/NN, the HMM parameters are computed by a NN. This provides a reparametrization that allows for flexible control of model complexity, and incorporation of constraints. The approach is tested on the immunoglobulin family. A hybrid model is trained, and a multiple alignment derived, with less than a fourth of the number of parameters used with previous single HMMs. To capture dependencies, however, one must resort to a larger hybrid model class, where the data is modeled by multiple HMMs. The parameters of the HMMs, and their modulation as a function of input or context, is again calculated by a NN.

Amino Acid Sequence↗

PubMed1994

Hidden Markov models of biological primary sequence information.

Hidden Markov model (HMM) techniques are used to model families of biological sequences. A smooth and convergent algorithm is introduced to iteratively adapt the transition and emission parameters of the models from the examples in a given family. The HMM approach is applied to three protein families: globins, immunoglobulins, and kinases. In all cases, the models derived capture the important statistical characteristics of the family and can be used for a number of tasks, including multiple alignments, motif detection, and classification. For K sequences of average length N, this approach yields an effective multiple-alignment algorithm which requires O(KN2) operations, linear in the number of sequences.

Algorithms↗