Abstract: A neural network module is provided. It comprises an input layer comprising one or more input nodes configured to receive gene expression data. It also has a rule base layer comprising one or more rule nodes and an output layer comprising one or more output nodes configured to output one or more conditions. It also comprises an adaptive component configured to extract one or more rules from the rule base layer representing relationships between the gene expression data and the one or more conditions. Methods and systems using the module are disclosed as well as specific profiles utilising the system.

Abstract: Embodiments of this invention include oligonucleotides associated with one or mutations associated with familial thrombocytopenia and methods suitable for their detection. Embodiements of this invention include isolated peptides and/or proteins comprising a serine residue at position 42 of the HCS protein. This invention also includes antibodies directed at nucleic acids, peptides or proteins comprising at least a portion of HCS having a serine reside at positon 42. Other embodiements include devices, kits and methods for detecting markers associated with familial thrombocytopenia associated with the HCS42g-s mutation, or to therapies for HCS42g-s using antisense oligonucleotides.

Abstract: Embodiments of this invention provide improved medical decision support systems and methods for using such systems to simultaneously consider two or more different types of information along with estimates of accuracies of the information to produce a combined predictor. Such predictors have greater accuracy compared to use of the individual types of information alone. Increased accuracy can increase the likelihood of correct diagnosis and/or evaluation of clinical condition or outcome, and can decrease the frequency of false negative results, including misdiagnosis. Embodiments of medical decision support systems can include EFuNN, Bayesian or other statistical estimators to produce a combined predictor. The systems can be used to extract relationship rules between sets of genes and clinical variables common for patients of a group, thus making a personalized gene-based treatment possible. Such systems are incorporated into computer-based devices and are run using suitable computer programs.