bioRxiv Science⌕ Search

bioRxiv · 10.1101/2020.12.17.423357

League of Brazilian Bioinformatics: a competition framework to promote scientific training

Abstract

Backgroundthe scientific training to become a bioinformatician includes multidisciplinary abilities, which increase the challenges to professional development. Competition frameworkin order to improve and promote the ongoing training of the Brazilian bioinformatics community, we organize a national competition, with the main goal to develop human resources and abilities in Computational Biology at the national level. The competition framework was designed in three phases: 1) a one-day challenge composed of 60 multiple-choice questions covering Biology, Computer Science, and Bioinformatics knowledge; 2) five Computational Biology challenges to be solved in three days; and 3) development of an original project evaluated during the 15th X-meeting. Resultsthe first edition of the League of Brazilian Bioinformatics (LBB) counted 168 competitors and 59 groups, distributed into undergraduate students (14.4%), graduate students (12.6% master and 16.8%, Ph.D.), and other professional fields. The first phase selected 46 teams to proceed in the competition, while the second phase selected the three top-performing teams. Conclusionduring the competition, we were able to stimulate teamwork in the main areas of Bioinformatics, with the engagement of all research-level competitors. Furthermore, we identified opportunities to deliver and offer better training to the community and we intend to apply the acquired experience in the second edition of the LBB, which will occur in 2021. Supplementary informationSupplementary data are available at Bioinformatics

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Carvalho, L. M., Coimbra, N., Camargo Neves, M. R., Fonseca, N., Costa, M., Horacio, E., Riyuzo, R., Aburjaile, F., Nagamatsu, S.. 2020-12-20. League of Brazilian Bioinformatics: a competition framework to promote scientific training. https://doi.org/10.1101/2020.12.17.423357

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Evaluating Large Language Models as Tools to Navigate Researchers in Rapidly Evolving Research Landscapes: A Case Study in Cancer Drug Response Prediction

Large Language Models (LLMs) have emerged as promising tools for assisting researchers in automating and accelerating the synthesis of literature reviews. However, their reliability is a significant concern due to issues like factual inaccuracies and hallucinations. The key question is whether LLMs can reliably provide comprehensive, up-to-date overviews and analyses. This study evaluates the performance of three leading LLMs (OpenAI's ChatGPT, Google's Gemini, and DeepSeek) on the complex task of generating a comprehensive survey paper on deep learning for cancer Drug Response Prediction (DRP). By testing both standard and Deep Research (DR) / Deep Think (DT) modes of LLMs with prompts of varying detail, this paper assesses key academic dimensions, including reference management, content quality, and analytical depth. Key findings reveal that while DR modes of LLMs significantly improve reliability by eliminating hallucinations, performance variations exist across models and prompts. A trade-off between reference quantity and integration quality was observed, and even the best-performing models lacked the analytical depth of human experts, often requiring extensive human supervision. The study concludes that LLMs currently serve as powerful assistive tools but still cannot replace the critical validation and synthesis provided by human researchers. Choosing the best LLM to use depends on the task in hand, while several strategies can be implemented to improve the produced output.

scientific communication and education↗

Welfare concerns for mounted load carrying by working donkeys in Pakistan

Working donkeys (Equus asinus) are vital to peoples livelihoods. They are essential for carrying goods, however globally, overloading is one of the primary welfare concerns of working donkeys. We studied mounted load carrying by donkeys and associated factors in Pakistan. A cross-sectional study of donkey owners (n = 332) was conducted, and interviews were undertaken based on a questionnaire. Owners estimated that the median weight of their donkeys was 110kg (interquartile range (IQR) 100-120kg), and that they carried a median mounted load of 81.5kg (IQR 63-99kg). We found that 87.4% of donkeys carried a load above 50% of their bodyweight ratio (BWR), the median BWR carried was 77.1% (IQR 54.5-90.7%), and 25.3% of donkeys carried above 90% BWR. Donkeys that were loaded at more than 50% BWR were more likely to sit, compared to donkeys loaded with less weight (p=0.01). Donkeys working in peri-urban and urban areas were more likely to carry a greater BWR than donkeys working in rural areas (P<0.001), as were those carrying construction materials or bricks, compared to agricultural materials (p=0.004). Age (p=0.03) and breed (p=0.01) were also associated with carrying a higher weight. Overloading based on current recommendations (50% BWR) was common, with the majority (87.4%) of donkeys reported to carry more than the recommended 50% limit. This survey provides evidence of on-the-ground working practices and factors associated with mounted load carrying, which is critical for developing evidence-based recommendations for loading, in order to improve the welfare of working donkeys.

scientific communication and education↗