Biomedical subjects

Guang-Zheng Zhang

Publications and source records attributed to Guang-Zheng Zhang.

4 recordsLinked to original sources

PubMed2006

[Following up investigation of forensic psychiatry judicial expert mental testimony].

OBJECTIVE: To study different viewpoints to the conclusion and treatment, And the following up factors concerning social effect after the judgment of forensic psychiatry. METHODS: By letters, calls, visits and by investigation forms made by myself, 208 testimonied cases were followed up separately from judicial organ or other organizations and institutes, individuals testimonied or their family members, victims and their family members. RESULTS: Most testimonied individuals were married man of 30 years old or so whose average education were 4.84 years and testimonied by the public security organs. In criminal cases, violental criminals (129 cases, 83.77%) were much more than non-violental criminals (25 cases, 16.23%) and homicide criminals (44 cases, 28.57%) were most common in the former but civil cases were few. The rate of retstinvony was 2.93%. The testimonied individuals and their family members thought the condemn were appropriate (76.47%) for the people without criminal capacity and thought the condemn were appropriate (41.94%) or not appropriate (41.94%) for the people with criminal capacity. The opinions of the condemn for partial criminal capacity were between the former two cases. And they thought the condemn for 28 cases of non-guilty were appropriate (71.43%) and not appropriate (10.7%). 7 victims were dead. 10 victims were crippled. 10 victims restored to health. In 41 persons testimonied and set free with a verdict of "not guilty", 4 homicided again (4.87%), 2 set on fire (4.87%), 2 stealed (4.87%) and 3 had wrecked behavior (7.31%). CONCLUSION: So it is suggested that the department concerned should keep criminals with mental disorder under control and treatment.

Adolescent↗

PubMed2005

Prediction of inter-residue contacts map based on genetic algorithm optimized radial basis function neural network and binary input encoding scheme.

Inter-residue contacts map prediction is one of the most important intermediate steps to the protein folding problem. In this paper, we focus on the problem of protein inter-residue contacts map prediction based on neural network technique. Firstly, we use a genetic algorithm (GA) to optimize the radial basis function widths and hidden centers of a radial basis function neural network (RBFNN), then a novel binary encoding scheme is employed to train the network for the purpose of learning and predicting the inter-residue contacts patterns of protein sequences got from the protein data bank (PDB). The experimental evidence indicates the utility of our proposed encoding strategy and GA optimized RBFNN. Moreover, the simulation results demonstrate that the network got a better performance for these proteins, whose residue length falls into the area of (100, 300), and the predicted accuracy with a contact threshold of 7 Angstroms scores higher than the other 3 values with 5, 6, and 8 Angstroms.

Algorithms↗

PubMed2005

Prediction of protein secondary structure using improved two-level neural network architecture.

In this paper we propose constructing an improved two-level neural network to predict protein secondary structure. Firstly, we code the whole protein composition information as the inputs to the first-level network besides the evolutionary information. Secondly, we calculate the reliability score for each residue position based on the output of the first-level network, and the role of the second-level network is to take full advantage of the residues with a higher reliability score to impact the neighboring residues with a lower one for improving the whole prediction accuracy. Thirdly, considering it is indeed a problem that the target protein can be lost in the multiple sequence alignment we propose to code single sequence into the second-level network. The experimental results show that our proposed method can efficiently improve the prediction accuracy.

Algorithms↗

PubMed2004

Inter-residue spatial distance map prediction by using integrating GA with RBFNN.

The spatial ordering information of amino acid residue in protein primary sequence is an important determinant of protein three-dimensional structure. In this paper, we describe a radial basis function neural network (RBFNN), whose hidden centers and basis function widths are optimized by a genetic algorithm (GA), for the purpose of predicting three dimensional spatial distance location from primary sequence information. Experimental evidence on soybean protein sequences indicates the utility of this approach.

Algorithms↗