Modelling molecular interaction pathways using a two-stage identification algorithm

In systems biology, molecular interactions are typically modelled using white-box methods, usually based on mass action kinetics. Unfortunately, problems with dimensionality can arise when the number of molecular species in the system is very large, which makes the system modelling and behavior simulation extremely difficult or computationally too expensive. As an alternative, this paper investigates the identification of two molecular interaction pathways using a black-box approach. This type of method creates a simple linear-in-the-parameters model using regression of data, where the output of the model at any time is a function of previous system states of interest. One of the main objectives in building black-box models is to produce an optimal sparse nonlinear one to effectively represent the system behavior. In this paper, it is achieved by applying an efficient iterative approach, where the terms in the regression model are selected and refined using a forward and backward subset selection algorithm. The method is applied to model identification for the MAPK signal transduction pathway and the Brusselator using noisy data of different sizes. Simulation results confirm the efficacy of the black-box modelling method which offers an alternative to the computationally expensive conventional approach

Artificial intelligence for management of patients with intracranial neoplasms

Although rare, intracranial neoplasms are associated with high morbidity and often poor prognosis. After a brief epidemiologic introduction, artificial intelligence (AI) applications in the fields of neurosurgery and neuro-oncology are reviewed, with a focus on machine learning (ML). AI can play an important role in the diagnostic process of brain tumors, through imaging-related applications, from segmentation to prediction of clinical features and patient outcome. AI decision support systems are promising to aid the physician in defining the best treatment options, based on predicted outcomes. Important technological advances have provided neurosurgeons with innovative equipment that can assist in surgical resection of brain lesions: while neuronavigation is now standard for most procedures, new systems can help differentiate neoplastic tissue from normal brain parenchyma and robotics has found specific applications. Assessment of prognosis through ML algorithms has been shown to be at least as accurate as normally used prognostic indices and the opinion of clinical experts. Although extremely promising, AI applications in neurosurgical practice still present several limitations—from quantity and quality of training data, to concerns of ethical implications—highlighting the need for continued research in this growing field. This chapter provides an overview of the applications AI and ML in the habitual steps of clinical management of a patient with an intracranial neoplasm, discussing the present and future AI tools available to assist diagnosis, treatment, and prognosis