Biomedical subjects

M J Nijman

Publications and source records attributed to M J Nijman.

3 recordsLinked to original sources

PubMed1999

A development protocol for a diagnostic DSS.

A diagnostic decision support system (DSS) in medicine is an expert system that aids the physician in the determination of the diagnosis based on findings and test results. The DSS can be divided into 2 different types of components: the knowledge component and the information system component. Methods from software engineering, knowledge engineering and management are combined into a dynamic development cycle that allows stepwise update and refinement. The development of the knowledge component is based on knowledge engineering. In the starting phases, rapid prototyping is convenient to determine and evaluate the specification. The content of the knowledge base is frequently updated during its life time. For this purpose, a knowledge modelling protocol is supplied.

Artificial Intelligence↗

PubMed1998

Apparent local stability of the secondary structure of Azotobacter vinelandii holoflavodoxin II as probed by hydrogen exchange: implications for redox potential regulation and flavodoxin folding.

As a first step to determine the folding pathway of a protein with an alpha/beta doubly wound topology, the 1H, 13C, and 15N backbone chemical shifts of Azotobacter vinelandii holoflavodoxin II (179 residues) have been determined using multidimensional NMR spectroscopy. Its secondary structure is shown to contain a five-stranded parallel beta-sheet (beta2-beta1-beta3-beta4-beta5) and five alpha-helices. Exchange rates for the individual amide protons of holoflavodoxin were determined using the hydrogen exchange method. The amide protons of 65 residues distributed throughout the structure of holoflavodoxin exchange slowly at pH* 6.2 [kex < 10(-5) s(-1)] and can be used as probes in future folding studies. Measured exchange rates relate to apparent local free energies for transient opening. We propose that the amide protons in the core of holoflavodoxin only exchange by global unfolding of the apo state of the protein. The results obtained are discussed with respect to their implications for flavodoxin folding and for modulation of the flavin redox potential by the apoprotein. We do not find any evidence that A. vinelandii holoflavodoxin II is divided into two subdomains based on its amide proton exchange rates, as opposed to what is found for the structurally but not sequentially homologous alpha/beta doubly wound protein Che Y.

Amino Acid Sequence↗

PubMed1997

Symmetry breaking and training from incomplete data with Radial Basis Boltzmann Machines.

A Radial Basis Boltzmann Machine (RBBM) is a specialized Boltzmann Machine architecture that combines feed-forward mapping with probability estimation in the input space, and for which very efficient learning rules exist. The hidden representation of the network displays symmetry breaking as a function of the noise in the dynamics. Thus, generalization can be studied as a function of the noise in the neuron dynamics instead of as a function of the number of hidden units. We show that the RBBM can be seen as an elegant alternative of k-nearest neighbor, leading to comparable performance without the need to store all data. We show that the RBBM has good classification performance compared to the MLP. The main advantage of the RBBM is that simultaneously with the input-output mapping, a model of the input space is obtained which can be used for learning with missing values. We derive learning rules for the case of incomplete data, and show that they perform better on incomplete data than the traditional learning rules on a 'repaired' data set.

Computer Simulation↗