Abstract

Abstract
One important problem in Bioinformatics is the discovery of new interactions between cellular lines and chemical compounds. In silico methods for cell-line screening are fundamental to optimize cost and time in the drug discovery processes. In order to build these methods, we need to computationally represent cell lines. Current methods for modeling cell line interactions rely on comparing genetic expression profiles. However, these profiles are usually unknown. In this work, we present a method to characterize and represent cell lines by text processing the related scientific literature. We collect abstracts of scientific papers about cellular lines from Cellosaurus and PubMed. These documents are then represented as TF-IDF vectors. We build a data set for classification with the document vectors having the cell line identifier as the target class. We then apply a multiclass SVM classification method. We use Support Vector Domain Description to describe and characterize each cell line with its corresponding hyperplane obtained with a one-vs-rest training. We evaluated several configurations of classifiers, using micro-averaged precision as metric to choose the best classifier, and were able to differentiate cellular lines from a set of 200+.

Abstract

Abstract
The proliferation of electronic health (e-Health) initiatives in Brazil over the last 2 decades has resulted in a considerable fragmentation within health information technology (IT), with a strong political interference. The problem regarding this issue became twofold: 1) there are considerable flaws regarding interoperability and security involving patient data; and 2) it is difficult even for an experienced company to enter the Brazilian health IT market. In this article, the authors aim to assess the current state of IT interoperability and security in hospitals in Brazil and evaluate the best business strategy for an IT company to enter this difficult but very promising health IT market. A face-to-face questionnaire was conducted among 11 hospital units to assess their current status regarding IT interoperability and security aspects. Global Brazilian socio-economic data was also collected, and helped to not only identify areas of investment regarding health IT security and interoperability, but also to derive a business strategy, composed out of recommendations listed in the paper.

Abstract
Complex data management on healthcare institutions makes very hard to identify illegitimate accesses which is a serious issue. We propose to develop a system to detect accesses with suspicious behavior for further investigation. We modeled use cases (UC) and sequence diagrams (SD) showing the data flow between users and systems. The algorithms represented by activity diagrams apply rules based on professionals' routines, use data from an audit trail (AT) and classify accesses as suspicious or normal. The algorithms were evaluated between 23rd and 31st July 2019. The results were analyzed using absolute and relative frequencies and dispersion measures. Access classification was in accordance to rules applied. "Check time of activity" UC had 64,78% of suspicious classifications, being 55% of activity period shorter and 9,78% longer than expected, "Check days of activity" presented 2,27% of suspicious access and "EHR read access" 79%, the highest percentage of suspicious accesses. The results show the first picture of HIS accesses. Deeper analysis to evaluate algorithms sensibility and specificity should be done. Lack of more detailed information about professionals' routines and systems. and low quality of systems logs are some limitations. Although we believe this is an important step in this field.

Abstract
The evolutionary growth of information technology has enabled us with platforms that eases access to a wide range of electronic services. Typically, access to these services requires users to authenticate their identity, which involves the release, dissemination and processing of personal data by third parties such as service and identity providers. The involvement of these and other entities in managing and processing personal identifiable data has continued to raise concerns on privacy of personal information. Identity management systems (IdMs) emerged as a promising solution to address major access control and privacy issues, however most research works are focused on securing service providers (SPs) and the services provided, with little emphases on users privacy. In order to optimise users privacy and ensure that personal information are used only for intended purposes, there is need for authorisation systems that controls who may access what and under what conditions. However, for adoption data owners perspective must not be neglected. To address these issues, this paper introduces the concept of IdM and access control framework which operates with RESTful based services. The proposal provides a new level of abstraction and logic in access management, while giving data owner a decisive control over access to personal data using smartphone. The framework utilises Attribute based access control (ABAC) method to authenticate and authorise users, Open ID Connect (OIDC) protocol for data owner authorisation and Public-key cryptography to achieve perfect forward secrecy communication. The solution enables data owner to attain the responsibility of granting or denying access to their data, from a secured communication with an identity provider using a digitally signed token.