As software applications continue to become more complex and attractive to cyber-attackers, enhancing resilience against cyber threats becomes essential. Aiming to provide more robust solutions, different approaches were proposed for vulnerability detection in different stages of the application life-cycle. This article explores three main approaches to application security: Static Application Security Testing (SAST), Dynamic Application Security Testing (DAST), and Software Composition Analysis (SCA). The analysis conducted in this work is focused on open-source solutions while considering commercial solutions to show contrast in the approaches taken and to better illustrate the different options available. It proposes a baseline comparison model to help evaluate and select the best solutions, using comparison criteria that are based on community standards. This work also identifies future opportunities for application security, highlighting some of the key challenges that still need to be addressed in order to fully protect against emerging threats, and proposes a workflow that combines the identified tools to be used for vulnerability assessments.

A vast number of microarray datasets have been produced as a way to identify differentially expressed genes and gene expression signatures. A better understanding of these biological processes can help in the diagnosis and prognosis of diseases, as well as in the therapeutic response to drugs. However, most of the available datasets are composed of a reduced number of samples, leading to low statistical, predictive and generalization power. One way to overcome this problem is by merging several microarray datasets into a single dataset, which is typically a challenging task. Statistical methods or supervised machine learning algorithms are usually used to determine gene expression signatures. Nevertheless, statistical methods require an arbitrary threshold to be defined, and supervised machine learning methods can be ineffective when applied to high-dimensional datasets like microarrays. We propose a methodology to identify gene expression signatures by merging microarray datasets. This methodology uses statistical methods to obtain several sets of differentially expressed genes and uses supervised machine learning algorithms to select the gene expression signature. This methodology was validated using two distinct research applications: one using heart failure and the other using autism spectrum disorder microarray datasets. For the first, we obtained a gene expression signature composed of 117 genes, with a classification accuracy of approximately 98%. For the second use case, we obtained a gene expression signature composed of 79 genes, with a classification accuracy of approximately 82%. This methodology was implemented in R language and is available, under the MIT licence, at https://github.com/bioinformatics-ua/MicroGES.

One of the features of Electronic Health Records (EHR) is to store the patient clinical data. Despite the efforts to structure all this data, clinical reports and notes containing essential information about each patient are still stored in free text. Some of this information refers to the family's health history and may be highly relevance for diagnosis and prognosis. We proposed two methodologies to unify this knowledge and extract family history information from clinical notes using rule-based techniques in natural language processing (NLP). With these methods, we intend to collect the family members mentioned in the text as well as associations to diseases and living status. The proposed methods were evaluated into two stages, demonstrating F-scores of 0.72 and 0.74 for the discovery of family members and observations, and 0.62 and 0.52 for the detection of the family relations with the observations, and their living status. Our methodologies raised new strategies to automatically annotate large amounts of EHRs, facilitating the detection of comorbidities within family relations.