Biomedical subjects

Giuseppina Gini

Publications and source records attributed to Giuseppina Gini.

5 recordsLinked to original sources

PubMed2003

Database mining with adaptive fuzzy partition: application to the prediction of pesticide toxicity on rats.

A data set of 235 pesticide compounds, divided into three classes according to their toxicity toward rats, was analyzed by a fuzzy logic procedure called adaptive fuzzy partition (AFP). This method allows the establishment of molecular descriptor/chemical activity relationships by dynamically dividing the descriptor space into a set of fuzzily partitioned subspaces. A set of 153 molecular descriptors was analyzed, including topological, physicochemical, quantum mechanical, constitutional, and electronic parameters, and the most relevant descriptors were selected with the help of a procedure combining genetic algorithm concepts and a stepwise method. The ability of this AFP model to classify the three toxicity classes was validated after dividing the data set compounds into training and test sets, including 165 and 70 molecules, respectively. The experimental class was correctly predicted for 76% of the test-set compounds. Furthermore, the most toxic class, particularly important for real applications of the toxicity models, was correctly predicted in 86% of cases. Finally, a comparison between the results obtained by AFP and those obtained by other classic classification techniques showed that AFP improved the predictive power of the proposed models.

Algorithms↗

PubMedDate not supplied

Human-like reflex control for an artificial hand.

In this paper, we illustrate the low level reflex control used to govern an anthropomorphic artificial hand. The paper develops the position and stiffness control strategy based on dynamic artificial neurons able to simulate the neurons acting in the human reflex control. The controller has a hierarchical structure. At the lowest level there are the receptors able to convert the analogical signal into a neural impulsive signal appropriate to govern the reflex control neurons. Immediately upon it, the artificial motoneurons set the actuators inner pressure to control the finger joint position and moment. Other auxiliary neurons in combination with the motoneurons are able to set the finger stiffness and emulate the inverse myotatic reflex control. Stiffness modulation is important both to save energy during task execution, and to manage objects made of different materials. The inverse myotatic reflex is able to protect the hand from possible harmful external actions. The paper also presents the dynamic model of the joints and of the artificial muscles actuating Blackfingers, our artificial hand. This new type of neural control has been simulated on the Blackfingers model; the results indicate that the developed control is very flexible and efficient for all kind of joints present in the humanoid hand.

Artificial Limbs↗

PubMedDate not supplied

The importance of scaling in data mining for toxicity prediction.

While mining a data set of 554 chemicals in order to extract information on their toxicity value, we faced the problem of scaling all the data. There are numerous different approaches to this procedure, and in most cases the choice greatly influences the results. The aim of this paper is 2-fold. First, we propose a universal scaling procedure for acute toxicity in fish according to the Directive 92/32/EEC. Second, we look at how expert preprocessing of the data effects the performance of qualitative structure-activity relationship (QSAR) approach to toxicity prediction.

Animals↗

PubMedDate not supplied

Combining unsupervised and supervised artificial neural networks to predictaquatic toxicity.

Most quantitative structure-activity relationship (QSAR) models are linear relationships and significant for only a limited domain of compounds. Here we propose a data-driven approach with a flexible combination of unsupervised and supervised neural networks able to predict the toxicity of a large set of different chemicals while still respecting the QSAR postulates. Since QSAR is applicable only to similar compounds, which have similar biological and physicochemical properties, large numbers of compounds are clustered before building local models, and local models are ensembled to obtain the final result. The approach has been used to develop models to predict the fish toxicity of Pimephales promelas and Tetrahymena pyriformis, a protozoan.

Journal Article↗

PubMedDate not supplied

Description of the electronic structure of organic chemicals using semiempirical and ab initio methods for development of toxicological QSARs.

The quality of quantitative structure-activity relationship (QSAR) models depends on the quality of their constitutive elements including the biological activity, statistical procedure applied, and the physicochemical and structural descriptors. The aim of this study was to assess the comparative use of ab initio and semiempirical quantum chemical calculations for the development of toxicological QSARs applied to a large and chemically diverse data set. A heterogeneous collection of 568 organic compounds with 96 h acute toxicity measured to the fish fathead minnow (Pimephales promelas) was utilized. A total of 162 descriptors were calculated using the semiempirical AM1 Hamiltonian, and 121 descriptors were compiled using an ab initio (B3LYP/6-31G**) method. The QSARs were derived using multiple linear regression (MLR) and partial least squares (PLS) analyses. Statistically similar models were obtained using AM1 and B3LYP calculated descriptors supported by the use of the logarithm of the octanol-water partition coefficient (log K(ow)). The main difference between the models derived by both MLR and PLS with the two sets of quantum chemical descriptors was concentrated on the type of descriptors selected. It was concluded that for large-scale predictions, irrespective of the mechanism of toxic action, the use of precise but time-consuming ab initio methods does not offer considerable advantage compared to the semiempirical calculations and could be avoided.

Animals↗