Early diagnosis of neurodegenerative disorders, such as Alzheimer's Disease (AD), is very important to reduce their effects and to improve both quality and life expectancy of patients. In this context, it is generally agreed that handwriting is one of the first skills altered by the onset of such diseases. For this reason, the analysis of handwriting and the study of its alterations have become of great interest in order to formulate the diagnosis as soon as possible. A fundamental aspect for the use of these techniques is the definition of effective features, which allows the system to distinguish the natural alterations of handwriting due to age, from those caused by neurodegenerative disorders. Starting from these considerations, the aim of our study is to verify whether the combined use of both shape and dynamic features allows a decision support system to improve performance for AD diagnosis. To this purpose, starting from a database of on-line handwriting samples, we generated for each of them an off-line synthetic color image, where the color of each elementary trait encodes, in the three RGB channels, the dynamic information associated with that trait. In order to verify the importance and the specific role played by shape information, we also generated an off-line synthetic binary image for each handwriting sample, where background pixels have white color, while those corresponding to the traits have black color. Finally, we exploited the ability of Convolutional Neural Network (CNN) to automatically extract features on both color and binary images. We carried out a large set of experiments for comparing the results obtained by using on-line features with those obtained by using the off-line features provided by CNN on both color and binary images.

In the last decade, there has been a growing scientific interest in the analysis of DNA microarray datasets, which have been widely used in basic and translational cancer research. The application fields include both the identification of oncological subjects, separating them from the healthy ones, and the classification of different types of cancer. Since DNA microarray experiments typically generate a very large number of features for a limited number of patients, the classification task is very complex and typically requires the application of a feature-selection process to reduce the complexity of the feature space and to identify a subset of distinctive features. In this framework, there are no standard state-of-the-art results generally accepted by the scientific community and, therefore, it is difficult to decide which approach to use for obtaining satisfactory results in the general case. Based on these considerations, the aim of the present work is to provide a large experimental comparison for evaluating the effect of the feature-selection process applied to different classification schemes. For comparison purposes, we considered both ranking-based feature-selection techniques and state-of-the-art feature-selection methods. The experiments provide a broad overview of the results obtainable on standard microarray datasets with different characteristics in terms of both the number of features and the number of patients.

Currently, the age structure of the world population is changing due to declining birth rates and increasing life expectancy. As a result, physicians worldwide have to treat an increasing number of age-related diseases, of which neurological disorders represent a significant part. In this context, there is an urgent need to discover new therapeutic approaches to counteract the effects of neurodegeneration on human health, and computational science can be of pivotal importance for more effective neurodrug discovery. The knowledge of the molecular structure of the receptors and other biomolecules involved in neurological pathogenesis facilitates the design of new molecules as potential drugs to be used in the fight against diseases of high social relevance such as dementia, Alzheimer’s disease (AD) and Parkinson’s disease (PD), to cite only a few. However, the absence of comprehensive guidelines regarding the strengths and weaknesses of alternative approaches creates a fragmented and disconnected field, resulting in missed opportunities to enhance performance and achieve successful applications. This review aims to summarize some of the most innovative strategies based on computational methods used for neurodrug development. In particular, recent applications and the state-of-the-art of molecular docking and artificial intelligence for ligand- and target-based approaches in novel drug design were reviewed, highlighting the crucial role of in silico methods in the context of neurodrug discovery for neurodegenerative diseases.