Jaap Heringa, Ph.D.
Director & Professor of Bioinformatics, IBIVU VU University Amsterdam, The Netherlands
Modeling strategy based on Petri-nets
In my talk I will introduce a formal modeling strategy based on Petri-nets, which are a convenient means of modeling biological processes. I will illustrate the capabilities of Petri-nets as reasoning vehicles using two examples: Haematopoietic stem cell differentiation in mice, and vulval development in C. elegance. The first system was modeled using a Boolean implementation, and the second using a coarse-grained multi-cellular Petri-net model. Concepts such as the model state space, attractor states, and reasoning to adapt the model to the biological reality will be discussed.
Srisairam Achuthan, Ph.D.
Senior Scientific Programmer, Research Informatics Division, Department of Information Sciences, City of Hope, CA, USA
Applying Machine learning for Automated Identification of Patient Cohorts
Srisairam Achuthan, Mike Chang, Ajay Shah, Joyce Niland
Patient cohorts for a clinical study are typically identified based on specific selection criteria. In most cases considerable time and effort are spent in finding the most relevant criteria that could potentially lead to a successful study. For complex diseases, this process can be more difficult and error prone since relevant features may not be easily identifiable. Additionally, the information captured in clinical notes is in non-coded text format. Our goal is to discover patterns within the coded and non-coded fields and thereby reveal complex relationships between clinical characteristics across different patients that would be difficult to accomplish manually. Towards this, we have applied machine learning techniques such as artificial neural networks and decision trees to determine patients sharing similar characteristics from available medical records. For this proof of concept study, we used coded and non-coded (i.e., clinical notes) patient data from a clinical database. Coded clinical information such as diagnoses, labs, medications and demographics recorded within the database were pooled together with non-coded information from clinical notes including, smoking status, life style (active / inactive) status derived from clinical notes. The non-coded textual information was identified and interpreted using a Natural Language Processing (NLP) tool I2E from Linguamatics.
Arathy R and Binoy B Nair
PC based heart sound monitoring system
Heart diseases caused by disorders of the heart and blood vessels, are world’s largest killers. Early detection and monitoring of heart abnormalities is essential for diagnosis and effective treatment of heart diseases. Severalmethodologies are used for screening and diagnosing heart diseases. They are auscultation, electrocardiogram (ECG), echo-cardiogram, ultrasound etc. The effectiveness and applicability of all these diagnostic methods are highly dependent on the equipment cost and size as well as skill of the physician. This paper presents the design and development of a low cost portable wireless/tubeless digital stethoscope which can be used by the physician for monitoring the patient from a distance. The stethoscope system interfaces to a PC and is also capable of analyzing the heart sounds and identifying abnormalities in the heart sound and its classification. Storage of heart sound for later analysis is also possible.This advanced functionality increases the physician’s diagnostic capability, and such a PCG is not still available in most hospitals. Acoustic stethoscope can be changed into a digital stethoscope by inserting an electric capacity microphone into its diaphragm (Wang, Chen and Samjin, 2009).
Bodo Eickhoff, Ph.D.
Senior Vice-President, Head of Sales and Marketing for Roche Applied Science, Germany
New paths for treatment of complex diseases: target combinatorial drug therapy
Several types of diseases show a complex pathogenesis and require targeted as well as combinatorial drug treatment. A classical example, Tuberculosis, was thought for decades to be managable by triple therapy, however now requiring new therapeutic approaches due to multi drug resistant strains. HIV and AIDS can only be kept under control by combinations of specific, virus-protein targeted drugs, requiring constant monitoring of resistance patterns and modulation of drug combinations during life-long therapy. As a third example, Cancer in all its different variations, requires detailled molecular understanding to enable targeted therapy. New technologies provide more and in depths molecular insights into pathomechanisms and resulting treatment options. However, is there an alternative way to approach complex diseases by holistic models? Can restoring of apoptosis-capabilities of transformed cells be an example of such an alternative path? How do we in future adress major unresolved topics like increasing drug resistance in bacterial infections, lack of anti-viral drugs, treatment of parasite diseases like Malaria, and newly emerging infectious diseases in research and fast translation of these results into diagnosis and treatment?