Bruno Grisci

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• Doctor of Philosophy
• Professor at Federal University of Rio Grande do Sul

Microarrays are still one of the major techniques employed to study cancer biology. However, the identification of expression patterns from microarray datasets is still a significant challenge to overcome. In this work, a new approach using Neuroevolution, a machine learning field that combines neural networks and evolutionary computation, provides...

The employment of machine learning (ML) approaches to extract gene expression information from microarray studies has increased in the past years, specially on cancer-related works. However, despite this continuous interest in applying ML in cancer biomedical research, there are no curated repositories focused only on providing quality data sets ex...

The lack of interpretability of neural networks is partially why they are not adopted in a wider variety of applications. Many works focus on explaining their predictions, but few take tabular data into consideration, which led to a small adoption even though this data is of high academic and business interest. We present relevance aggregation, an...

Evolutionary Developmental Biology (Evo-Devo) is an ever-expanding field that aims to understand how development was modulated by the evolutionary process. In this sense, "omic" studies emerged as a powerful ally to unravel the molecular mechanisms underlying development. In this scenario, bioinformatics tools become necessary to analyze the growin...

Motivation The conformational space of small molecules can be vast and difficult to assess. Molecular dynamics simulations of free ligands in solution have been applied to predict conformational populations, but their characterization is often based on clustering algorithms or manual efforts. Results Here, we introduce ConfID, an analytical tool...

The amount of gathered data is increasing at unprecedented rates for machine learning applications such as natural language processing, computer vision, and bioinformatics. This increase implies a higher number of samples and features; thus, some problems regarding highly dimensional data arise. The curse of dimensionality, small samples, noisy or...

Feature selection algorithms are frequently employed in preprocessing machine learning pipelines applied to biological data to identify relevant features. The use of feature selection in gene expression studies began at the end of the 1990s with the analysis of human cancer microarray datasets. Since then, gene expression technology has been perfec...

Nowadays, data has become an invaluable asset to entities and companies, and keeping it secure represents a major challenge. Data centers are responsible for storing data provided by software applications. Nevertheless, the number of vulnerabilities has been increasing every day. Managing such vulnerabilities is essential for building a reliable an...

The Coronavirus pandemic caused by the novel SARS-CoV-2 has significantly impacted human health and the economy, especially in countries struggling with financial resources for medical testing and treatment, such as Brazil's case, the third most affected country by the pandemic. In this scenario, machine learning techniques have been heavily employ...

Machine learning is a huge field of study in computer science and statistics dedicated to the execution of computational tasks through algorithms that do not require explicit instructions but instead rely on learning patterns from data samples to automate inferences. A large portion of the work involved in a machine learning project is to define th...

Chalcones and flavonoids constitute a large family of plant secondary metabolites that has been explored as a potential source of novel pharmaceutical products. While the simulation of these compounds by molecular dynamics (MD) can be a valuable strategy to assess their conformational properties and so further develop their role in drug discovery,...

The analysis of microarrays has the potential to identify and predict diseases predisposition, such as cancer, opening a new path to better diagnosis and improved treatments. Additionally, microarrays can help to find genetic biomarkers, which are genes whose expressions are related to a specific disease stage or condition. But due to the huge numb...

The identification of lead compounds usually includes a step of chemical diversity generation. Its rationale may be supported by both qualitative (SAR) and quantitative (QSAR) approaches, offering models of the putative ligand-receptor interactions. In both scenarios, our understanding of which interactions functional groups can perform is mostly b...

Tertiary protein structure prediction is one of the most challenging problems in structural bioinformatics. Despite the advances in algorithm development and computational strategies, predicting the folded structure of a protein only from its amino acid sequence remains as an unsolved problem. We present a new computational approach to predict the...

Educational robotics is the use of robots to make projects in school. Students build and program robots to perform various tasks, an activity that requires and develops cognitive skills, group work and project organization abilities, while serving as a first contact for students with the engineering areas. Even though there are several competitions...