Reply to "Ten simple rules for getting published"

Ten simple rules for providing bioinformatics support within a hospital

Bioinformatics has become a key aspect of the biomedical research programmes of many hospitals' scientific centres, and the establishment of bioinformatics facilities within hospitals has become a common practice worldwide. Bioinformaticians working in these facilities provide computational biology support to medical doctors and principal investigators who are daily dealing with data of patients to analyze. These bioinformatics analysts, although pivotal, usually do not receive formal training for this job. We therefore propose these ten simple rules to guide these bioinformaticians in their work: ten pieces of advice on how to provide bioinformatics support to medical doctors in hospitals. We believe these simple rules can help bioinformatics facility analysts in producing better scientific results and work in a serene and fruitful environment.

The Matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for assessing binary classification

Binary classification is a common task for which machine learning and computational statistics are used, and the area under the receiver operating characteristic curve (ROC AUC) has become the common standard metric to evaluate binary classifications in most scientific fields. The ROC curve has true positive rate (also called sensitivity or recall) on the y axis and false positive rate on the x axis, and the ROC AUC can range from 0 (worst result) to 1 (perfect result). The ROC AUC, however, has several flaws and drawbacks. This score is generated including predictions that obtained insufficient sensitivity and specificity, and moreover it does not say anything about positive predictive value (also known as precision) nor negative predictive value (NPV) obtained by the classifier, therefore potentially generating inflated overoptimistic results. Since it is common to include ROC AUC alone without precision and negative predictive value, a researcher might erroneously conclude that their classification was successful. Furthermore, a given point in the ROC space does not identify a single confusion matrix nor a group of matrices sharing the same MCC value. Indeed, a given (sensitivity, specificity) pair can cover a broad MCC range, which casts doubts on the reliability of ROC AUC as a performance measure. In contrast, the Matthews correlation coefficient (MCC) generates a high score in its [Formula: see text] interval only if the classifier scored a high value for all the four basic rates of the confusion matrix: sensitivity, specificity, precision, and negative predictive value. A high MCC (for example, MCC [Formula: see text] 0.9), moreover, always corresponds to a high ROC AUC, and not vice versa. In this short study, we explain why the Matthews correlation coefficient should replace the ROC AUC as standard statistic in all the scientific studies involving a binary classification, in all scientific fields.

Nine quick tips for pathway enrichment analysis

Pathway enrichment analysis (PEA) is a computational biology method that identifies biological functions that are overrepresented in a group of genes more than would be expected by chance and ranks these functions by relevance. The relative abundance of genes pertinent to specific pathways is measured through statistical methods, and associated functional pathways are retrieved from online bioinformatics databases. In the last decade, along with the spread of the internet, higher availability of computational resources made PEA software tools easy to access and to use for bioinformatics practitioners worldwide. Although it became easier to use these tools, it also became easier to make mistakes that could generate inflated or misleading results, especially for beginners and inexperienced computational biologists. With this article, we propose nine quick tips to avoid common mistakes and to out a complete, sound, thorough PEA, which can produce relevant and robust results. We describe our nine guidelines in a simple way, so that they can be understood and used by anyone, including students and beginners. Some tips explain what to do before starting a PEA, others are suggestions of how to correctly generate meaningful results, and some final guidelines indicate some useful steps to properly interpret PEA results. Our nine tips can help users perform better pathway enrichment analyses and eventually contribute to a better understanding of current biology.

Acceptance rates for manuscripts submitted to veterinary peer-reviewed journals in 2012

REASONS FOR PERFORMING STUDY

Relatively few journals publish their annual acceptance rate, although this figure is of scientific and academic interest.

OBJECTIVES

To determine the acceptance rate for manuscripts submitted to veterinary peer-reviewed journals during 2012 and to determine the proportions of submitted manuscripts that were accepted without revision, accepted after revision or rejected.

STUDY DESIGN

Self-reporting email questionnaire

METHODS

Editors of 118 peer-reviewed journals listed in the Web of Science in the subject category veterinary sciences were invited by email to submit data pertinent to manuscripts submitted to their journal in 2012.

RESULTS

Data were received from 30 (26%) journals. Mean ± s.d. acceptance rate was 47 ± 15%. On average 3 ± 5% submitted manuscripts were accepted without revision, 44% ± 15% manuscripts were accepted after revision, 4 ± 4% manuscripts were withdrawn by authors, 46 ± 17% manuscripts were rejected and 3 ± 5% manuscripts were still pending at the end of the study period.

CONCLUSIONS

With so few manuscripts accepted without revision, prospective authors must expect to expend time and effort revising and resubmitting their manuscripts for publication. Although authors are frequently able to correct manuscript flaws identified by reviewers, the knowledge that less than half submitted manuscripts are accepted might help stimulate prospective authors to try to submit better quality manuscripts.

Desafios e perspectivas para a publicação de artigos - uma reflexão a partir de autores e editores

Publicar é uma exigência para qualquer profissional que almeje estar em sintonia com o mundo da ciência. Igualmente, o consumo e a produção de conhecimento são atividades inerentes ao exercício profissional. Propomo-nos nesse artigo fazer uma reflexão sobre alguns aspectos desta temática, destacando aqueles que possam contribuir para uma eficaz investida a periódicos. São considerados aspectos fundamentais na sistematização dessa tarefa a fim de proporcionar que mais profissionais se dediquem a ela com eficiência. São trazidos para enriquecer a reflexão conhecimentos e estratégias divulgadas na literatura atual, nas quais encontramos depoimentos tanto de autores como de editores de periódicos da enfermagem e de outras áreas. Para tanto estruturamos este artigo nos seguintes passos: gerar e consolidar uma idéia, definir parcerias, focar no leitor, escolher o periódico, organizar o artigo, e observar aspectos éticos e envio à publicação.