Biomedical subjects

Alessio Micheli

Publications and source records attributed to Alessio Micheli.

4 recordsLinked to original sources

PubMed2006

Can individual repair kinetics of UVC-induced DNA damage in human lymphocytes be assessed through the comet assay?

The suitability of the comet assay for quantifying DNA repair capacity at individual level was studied following the kinetics of nucleotide excision repair (NER) in human lymphocytes from four healthy donors, at various time steps after a single dose of UVC. A significant increase of DNA migration was seen as soon as 20 min after UV exposure, reaching the peak within 60-90 min. Afterwards, a rapid decline was observed, approaching the basal level at 180-240 min. The increase could be ascribed to excision activity, while the reduction to gap filling and rejoining, as demonstrated by the effects of phase-specific inhibitors, novobiocin and aphidicolin. Therefore, the comet assay should allow following the biphasic kinetics of NER. Wide inter-individual differences were observed, although repeated tests on the same donor cells revealed a large experimental variation. To quantitatively compare the individual patterns, a mathematical model was developed that adequately fitted the experimental results and estimated appropriate descriptors for each phase and for each donor. A second approach was also used to directly compare the distributions of damaged cells and to assess the differences between donors and between experiments visualizing them as reciprocal distances on a two-dimensional space computed with a principal component analysis (PCA). The results confirmed the inter-individual differences, but also the strong influence of experimental factors of the comet assay. The two approaches provided the means of accurately comparing DNA repair kinetics at individual level, taking also into account the experimental variability which poses serious doubts on the suitability of the comet assay. Nevertheless, since this methodology allows a detailed analysis of repair kinetics and it is potentially very useful for identifying individual with reduced repair capacity, further efforts have to be addressed to improve the reproducibility of the comet assay.

Adult↗

PubMed2004

Contextual processing of structured data by recursive cascade correlation.

This paper propose a first approach to deal with contextual information in structured domains by recursive neural networks. The proposed model, i.e., contextual recursive cascade correlation (CRCC), a generalization of the recursive cascade correlation (RCC) model, is able to partially remove the causality assumption by exploiting contextual information stored in frozen units. We formally characterize the properties of CRCC showing that it is able to compute contextual transductions and also some causal supersource transductions that RCC cannot compute. Experimental results on controlled sequences and on a real-world task involving chemical structures confirm the computational limitations of RCC, while assessing the efficiency and efficacy of CRCC in dealing both with pure causal and contextual prediction tasks. Moreover, results obtained for the real-world task show the superiority of the proposed approach versus RCC when exploring a task for which it is not known whether the structural causality assumption holds.

Algorithms↗

PubMedDate not supplied

Recursive self-organizing network models.

Self-organizing models constitute valuable tools for data visualization, clustering, and data mining. Here, we focus on extensions of basic vector-based models by recursive computation in such a way that sequential and tree-structured data can be processed directly. The aim of this article is to give a unified review of important models recently proposed in literature, to investigate fundamental mathematical properties of these models, and to compare the approaches by experiments. We first review several models proposed in literature from a unifying perspective, thereby making use of an underlying general framework which also includes supervised recurrent and recursive models as special cases. We shortly discuss how the models can be related to different neuron lattices. Then, we investigate theoretical properties of the models in detail: we explicitly formalize how structures are internally stored in different context models and which similarity measures are induced by the recursive mapping onto the structures. We assess the representational capabilities of the models, and we shortly discuss the issues of topology preservation and noise tolerance. The models are compared in an experiment with time series data. Finally, we add an experiment for one context model for tree-structured data to demonstrate the capability to process complex structures.

Artifacts↗

PubMedDate not supplied

Predicting physical-chemical properties of compounds from molecular structures by recursive neural networks.

In this paper, we report on the potential of a recently developed neural network for structures applied to the prediction of physical chemical properties of compounds. The proposed recursive neural network (RecNN) model is able to directly take as input a structured representation of the molecule and to model a direct and adaptive relationship between the molecular structure and target property. Therefore, it combines in a learning system the flexibility and general advantages of a neural network model with the representational power of a structured domain. As a result, a completely new approach to quantitative structure-activity relationship/quantitative structure-property relationship (QSPR/QSAR) analysis is obtained. An original representation of the molecular structures has been developed accounting for both the occurrence of specific atoms/groups and the topological relationships among them. Gibbs free energy of solvation in water, Delta(solv)G degrees , has been chosen as a benchmark for the model. The different approaches proposed in the literature for the prediction of this property have been reconsidered from a general perspective. The advantages of RecNN as a suitable tool for the automatization of fundamental parts of the QSPR/QSAR analysis have been highlighted. The RecNN model has been applied to the analysis of the Delta(solv)G degrees in water of 138 monofunctional acyclic organic compounds and tested on an external data set of 33 compounds. As a result of the statistical analysis, we obtained, for the predictive accuracy estimated on the test set, correlation coefficient R = 0.9985, standard deviation S = 0.68 kJ mol(-1), and mean absolute error MAE = 0.46 kJ mol(-1). The inherent ability of RecNN to abstract chemical knowledge through the adaptive learning process has been investigated by principal components analysis of the internal representations computed by the network. It has been found that the model recognizes the chemical compounds on the basis of a nontrivial combination of their chemical structure and target property.

Journal Article↗