Da ciência à e-ciência: paradigmas da descoberta do conhecimento

Gradualmente, a computação está deixando de ser apenas uma “ferramenta de apoio” a novas pesquisas para se tornar parte fundamental das ciências com que interage e de seus métodos científicos. A sinergia entre ciência da computação e as outras áreas do conhecimento criou um novo modo de se fazer ciência – a e-science (ou e-ciência) – que unifica teoria, experimentos e simulação, ao mesmo tempo em que lida com uma quantidade enorme de informação. O uso de computação em nuvem tem o potencial de permitir que pesquisas antes restritas àqueles com acesso a supercomputadores possam ser realizadas por qualquer pesquisador. Este artigo apresenta uma breve descrição da evolução dos paradigmas do modo de se fazer ciência (do empirismo ao panorama atual da e-science) e aborda o potencial da computação em nuvem como ferramenta catalisadora de pesquisa transformativa.Computer Science is gradually evolving from a mere “supporting tool” for research in other fields and turning into an intrinsic part of the very methods of the sciences with which it interacts. The synergy between Computer Science and other fields of knowledge created a novel way of doing science – called eScience – which unifies theory, experiments, and simulations, enabling researchers to deal with huge amounts of information. The use of cloud computing has the potential to allow any researcher to conduct works previously restricted to those with access to supercomputers. This article presents a brief history of the evolution of scientific paradigms (from empiricism to the current landscape of eScience) and discusses the potential of cloud computing as a tool capable of catalyzing transformative research

Features in HIV genotypes associated with failure in the computational prediction of patients' response to antiretroviral treatment

HIV acts by attacking the immune system and gradually destroying the TCD4+ defense cells. Without adequate treatment, the carriers develop the most severe form of the infection, AIDS, when the patient can be afflicted by opportunistic diseases that inevitably lead to death. Fortunately, with the advent of the highly active antiretroviral therapy (HAART), the mortality of people with HIV is decreasing. However, mutations can occur in the genotype of the virus, generating drug-resistant phenotypes. Computational methods have been used to predict whether a given strain is drug-resistant, and to which drugs this resistance occurs, thereby increasing the chances of success of the prescribed treatment regimen. However, these methods are not always accurate in their task. In this context, by applying Feature Selection methods and estimating Decision Tree models, we investigated patterns in Protease and Reverse Transcriptase enzyme sequences, as well as in patients’ clinical data, which can lead to correct or incorrect computational prediction. As a result, we identified 21 features that are highly informative, 11 which tend to lead the methods to error, and eight that present both behaviors simultaneously, being able to predict the patient's response to therapy and at the same time may lead the predictor's methods to failure

Data-intensive analysis of HIV mutations

Background\ud In this study, clustering was performed using a bitmap representation of HIV reverse transcriptase and protease sequences, to produce an unsupervised classification of HIV sequences. The classification will aid our understanding of the interactions between mutations and drug resistance. 10,229 HIV genomic sequences from the protease and reverse transcriptase regions of the pol gene and antiretroviral resistant related mutations represented in an 82-dimensional binary vector space were analyzed.\ud \ud \ud Results\ud A new cluster representation was proposed using an image inspired by microarray data, such that the rows in the image represented the protein sequences from the genotype data and the columns represented presence or absence of mutations in each protein position.The visualization of the clusters showed that some mutations frequently occur together and are probably related to an epistatic phenomenon.\ud \ud \ud Conclusion\ud We described a methodology based on the application of a pattern recognition algorithm using binary data to suggest clusters of mutations that can easily be discriminated by cluster viewing schemes.FAPESP [11/50761-2]CNPqCapesPRP-US