Fast training of self organizing maps for the visual exploration of molecular compounds

Visual exploration of scientific data in life science area is a growing research field due to the large amount of available data. The Kohonen’s Self Organizing Map (SOM) is a widely used tool for visualization of multidimensional data. In this paper we present a fast learning algorithm for SOMs that uses a simulated annealing method to adapt the learning parameters. The algorithm has been adopted in a data analysis framework for the generation of similarity maps. Such maps provide an effective tool for the visual exploration of large and multi-dimensional input spaces. The approach has been applied to data generated during the High Throughput Screening of molecular compounds; the generated maps allow a visual exploration of molecules with similar topological properties. The experimental analysis on real world data from the National Cancer Institute shows the speed up of the proposed SOM training process in comparison to a traditional approach. The resulting visual landscape groups molecules with similar chemical properties in densely connected regions.

Understanding the seriousness of corporate crime: Some lessons for the new 'corporate manslaughter' offence

The measurement of public attitudes towards the criminal law has become an important area of research in recent years because of the perceived desirability of ensuring that the legal system reflects broader societal values. In particular, studies into public perceptions of crime seriousness have attempted to measure the degree of concordance that exists between law and public opinion in the organization and enforcement of criminal offences. These understandings of perceived crime seriousness are particularly important in relation to high-profile issues where public confidence in the law is central to the legal agenda, such as the enforcement of work-related fatality cases. A need to respond to public concern over this issue was cited as a primary justification for the introduction of the Corporate Manslaughter and Corporate Homicide Act 2007. This article will suggest that, although literature looking at the perceived seriousness of corporate crime and, particularly, health and safety offences is limited in volume and generalist in scope, important lessons can be gleaned from existing literature, and pressing questions are raised that demand further empirical investigation.

The automatic identification and prioritisation of criminal networks from police crime data

The identification of criminal networks is not a routine exploratory process within the current practice of the law enforcement authorities; rather it is triggered by specific evidence of criminal activity being investigated. A network is identified when a criminal comes to notice and any associates who could also be potentially implicated would need to be identified if only to be eliminated from the enquiries as suspects or witnesses as well as to prevent and/or detect crime. However, an identified network may not be the one causing most harm in a given area.. This paper identifies a methodology to identify all of the criminal networks that are present within a Law Enforcement Area, and, prioritises those that are causing most harm to the community. Each crime is allocated a score based on its crime type and how recently the crime was committed; the network score, which can be used as decision support to help prioritise it for law enforcement purposes, is the sum of the individual crime scores.

Reduction of invasive tests in chagasic patients with a modified self-organizing map

The applicability of AI methods to the Chagas' disease diagnosis is carried out by the use of Kohonen's self-organizing feature maps. Electrodiagnosis indicators calculated from ECG records are used as features in input vectors to train the network. Cross-validation results are used to modify the maps, providing an outstanding improvement to the interpretation of the resulting output. As a result, the map might be used to reduce the need for invasive explorations in chronic Chagas' disease.