Discovering hidden structures in molecular data using a Bayesian partition model approach

Advancements in the analysis techniques have led to a rapid accumulation of biological data in databases. Such data often are in the form of sequences of observations, examples including DNA sequences and amino acid sequences of proteins. The scale and quality of the data give promises of answering various biologically relevant questions in more detail than what has been possible before. For example, one may wish to identify areas in an amino acid sequence, which are important for the function of the corresponding protein, or investigate how characteristics on the level of DNA sequence affect the adaptation of a bacterial species to its environment. Many of the interesting questions are intimately associated with the understanding of the evolutionary relationships among the items under consideration. The aim of this work is to develop novel statistical models and computational techniques to meet with the challenge of deriving meaning from the increasing amounts of data. Our main concern is on modeling the evolutionary relationships based on the observed molecular data. We operate within a Bayesian statistical framework, which allows a probabilistic quantification of the uncertainties related to a particular solution. As the basis of our modeling approach we utilize a partition model, which is used to describe the structure of data by appropriately dividing the data items into clusters of related items. Generalizations and modifications of the partition model are developed and applied to various problems. Large-scale data sets provide also a computational challenge. The models used to describe the data must be realistic enough to capture the essential features of the current modeling task but, at the same time, simple enough to make it possible to carry out the inference in practice. The partition model fulfills these two requirements. The problem-specific features can be taken into account by modifying the prior probability distributions of the model parameters. The computational efficiency stems from the ability to integrate out the parameters of the partition model analytically, which enables the use of efficient stochastic search algorithms.

Information Technology Development Needs in Community Pharmacies : A Strategic Approach

In the context of health care, information technology (IT) has an important role in the operational infrastructure, ranging from business management to patient care. An essential part of the system is medication management in inpatient and outpatient care. Community pharmacists strategy has been to extend practice responsibilities beyond dispensing towards patient care services. Few studies have evaluated the strategic development of IT systems to support this vision. The objectives of this study were to assess and compare independent Finnish community pharmacy owners and staff pharmacists priorities concerning the content and structure of the next generation of community pharmacy IT systems, to explore international experts visions and strategic views on IT development needs in relation to services provided in community pharmacies, to identify IT innovations facilitating patient care services and to evaluate their development and implementation processes, and to assess community pharmacists readiness to adopt innovations. This study applied both qualitative and quantitative methods. A qualitative personal interview of 14 experts in community pharmacy services and related IT from eight countries and a national survey of Finnish community pharmacy owners (mail survey, response rate 53%, n=308), and of a representative sample of staff pharmacists (online survey, response rate 22%, n=373) were conducted. Finnish independent community pharmacy owners gave priority to logistical functions but also to those related to medication information and patient care. The managers and staff pharmacists have different views of the importance of IT features, reflecting their different professional duties in the community pharmacy. This indicates the need for involving different occupation groups in planning the new IT systems for community pharmacies. A majority of the international experts shared the vision of community pharmacy adopting a patient care orientation; supported by IT-based documentation, new technological solutions, access to information, and shared patient data. Community pharmacy IT innovations were rare, which is paradoxical because owners and staff pharmacists perception of their innovativeness was seen as being high. Community pharmacy IT systems development processes usually had not undergone systematic needs assessment research beforehand or evaluation after the implementation and were most often coordinated by national governments without subsequent commercialization. Specifically, community pharmacy IT developments lack research, organization, leadership and user involvement in the process. Those responsible for IT development in the community pharmacy sector should create long-term IT development strategies that are in line with community pharmacy service development strategies. This could provide systematic guidance for future projects to ensure that potential innovations are based on a sufficient understanding of pharmacy practice problems that they are intended to solve, and to encourage strong leadership in research, development of innovations so that community pharmacists potential innovativeness is used, and that professional needs and strategic priorities will be considered even if the development process is led by those outside the profession.