Rama: a machine learning approach for ribosomal protein prediction in plants.

Ribosomal proteins (RPs) play a fundamental role within all type of cells, as they are major components of ribosomes, which are essential for translation of mRNAs. Furthermore, these proteins are involved in various physiological and pathological processes. The intrinsic biological relevance of RPs motivated advanced studies for the identification of unrevealed RPs. In this work, we propose a new computational method, termed Rama, for the prediction of RPs, based on machine learning techniques, with a particular interest in plants. To perform an effective classification, Rama uses a set of fundamental attributes of the amino acid side chains and applies a two-step procedure to classify proteins with unknown function as RPs. The evaluation of the resultant predictive models showed that Rama could achieve mean sensitivity, precision, and specificity of 0.91, 0.91, and 0.82, respectively. Furthermore, a list of proteins that have no annotation in Phytozome v.10, and are annotated as RPs in Phytozome v.12, were correctly classified by our models. Additional computational experiments have also shown that Rama presents high accuracy to differentiate ribosomal proteins from RNA-binding proteins. Finally, two novel proteins of Arabidopsis thaliana were validated in biological experiments. Rama is freely available at http://inctipp.bioagro.ufv.br:8080/Rama .

MUMAL2: Improving sensitivity in shotgun proteomics using cost sensitive artificial neural networks and a threshold selector algorithm.

BACKGROUND: This work presents a machine learning strategy to increase sensitivity in tandem mass spectrometry (MS/MS) data analysis for peptide/protein identification. MS/MS yields thousands of spectra in a single run which are then interpreted by software. Most of these computer programs use a protein database to match peptide sequences to the observed spectra. The peptide-spectrum matches (PSMs) must also be assessed by computational tools since manual evaluation is not practicable. The target-decoy database strategy is largely used for error estimation in PSM assessment. However, in general, that strategy does not account for sensitivity. RESULTS: In a previous study, we proposed the method MUMAL that applies an artificial neural network to effectively generate a model to classify PSMs using decoy hits with increased sensitivity. Nevertheless, the present approach shows that the sensitivity can be further improved with the use of a cost matrix associated with the learning algorithm. We also demonstrate that using a threshold selector algorithm for probability adjustment leads to more coherent probability values assigned to the PSMs. Our new approach, termed MUMAL2, provides a two-fold contribution to shotgun proteomics. First, the increase in the number of correctly interpreted spectra in the peptide level augments the chance of identifying more proteins. Second, the more appropriate PSM probability values that are produced by the threshold selector algorithm impact the protein inference stage performed by programs that take probabilities into account, such as ProteinProphet. Our experiments demonstrate that MUMAL2 reached around 15% of improvement in sensitivity compared to the best current method. Furthermore, the area under the ROC curve obtained was 0.93, demonstrating that the probabilities generated by our model are in fact appropriate. Finally, Venn diagrams comparing MUMAL2 with the best current method show that the number of exclusive peptides found by our method was nearly 4-fold higher, which directly impacts the proteome coverage. CONCLUSIONS: The inclusion of a cost matrix and a probability threshold selector algorithm to the learning task further improves the target-decoy database analysis for identifying peptides, which optimally contributes to the challenging task of protein level identification, resulting in a powerful computational tool for shotgun proteomics.

Mirnacle: machine learning with SMOTE and random forest for improving selectivity in pre-miRNA ab initio prediction.

BACKGROUND: MicroRNAs (miRNAs) are key gene expression regulators in plants and animals. Therefore, miRNAs are involved in several biological processes, making the study of these molecules one of the most relevant topics of molecular biology nowadays. However, characterizing miRNAs in vivo is still a complex task. As a consequence, in silico methods have been developed to predict miRNA loci. A common ab initio strategy to find miRNAs in genomic data is to search for sequences that can fold into the typical hairpin structure of miRNA precursors (pre-miRNAs). The current ab initio approaches, however, have selectivity issues, i.e., a high number of false positives is reported, which can lead to laborious and costly attempts to provide biological validation. This study presents an extension of the ab initio method miRNAFold, with the aim of improving selectivity through machine learning techniques, namely, random forest combined with the SMOTE procedure that copes with imbalance datasets. RESULTS: By comparing our method, termed Mirnacle, with other important approaches in the literature, we demonstrate that Mirnacle substantially improves selectivity without compromising sensitivity. For the three datasets used in our experiments, our method achieved at least 97% of sensitivity and could deliver a two-fold, 20-fold, and 6-fold increase in selectivity, respectively, compared with the best results of current computational tools. CONCLUSIONS: The extension of miRNAFold by the introduction of machine learning techniques, significantly increases selectivity in pre-miRNA ab initio prediction, which optimally contributes to advanced studies on miRNAs, as the need of biological validations is diminished. Hopefully, new research, such as studies of severe diseases caused by miRNA malfunction, will benefit from the proposed computational tool.

The complement system: importance in clinical practice / O sistema complemento: importância para a prática clínica

The complement system is a fundamental component of the host immune response. In addition to its effector activity against pathogens, it possesses functions such as opsonization and phagocytosis, removal of immune complexes and activation of the inflammatory process. The knowledge of the complement system is important in the investigation of numerous diseases that can be observed in cases of deficiencies in cascade proteins, their receptors, or regulatory proteins. Clinical and experimental evidence demonstrate the association between the complement system and several inflammatory conditions, as well as a greater susceptibility to infection among patients with complement system dysfunction. Thus, the purpose of this paper is to describe the three complement system pathways - the activation and effector mechanisms and their biochemical characteristics - and correlate them to certain clinical conditions.

NICeSim: an open-source simulator based on machine learning techniques to support medical research on prenatal and perinatal care decision making.

OBJECTIVE: This paper describes NICeSim, an open-source simulator that uses machine learning (ML) techniques to aid health professionals to better understand the treatment and prognosis of premature newborns. METHODS: The application was developed and tested using data collected in a Brazilian hospital. The available data were used to feed an ML pipeline that was designed to create a simulator capable of predicting the outcome (death probability) for newborns admitted to neonatal intensive care units. However, unlike previous scoring systems, our computational tool is not intended to be used at the patients bedside, although it is possible. Our primary goal is to deliver a computational system to aid medical research in understanding the correlation of key variables with the studied outcome so that new standards can be established for future clinical decisions. In the implemented simulation environment, the values of key attributes can be changed using a user-friendly interface, where the impact of each change on the outcome is immediately reported, allowing a quantitative analysis, in addition to a qualitative investigation, and delivering a totally interactive computational tool that facilitates hypothesis construction and testing. RESULTS: Our statistical experiments showed that the resulting model for death prediction could achieve an accuracy of 86.7% and an area under the receiver operating characteristic curve of 0.84 for the positive class. Using this model, three physicians and a neonatal nutritionist performed simulations with key variables correlated with chance of death. The results indicated important tendencies for the effect of each variable and the combination of variables on prognosis. We could also observe values of gestational age and birth weight for which a low Apgar score and the occurrence of respiratory distress syndrome (RDS) could be less or more severe. For instance, we have noticed that for a newborn with 2000 g or more the occurrence of RDS is far less problematic than for neonates weighing less. CONCLUSIONS: The significant accuracy demonstrated by our predictive model shows that NICeSim might be used for hypothesis testing to minimize in vivo experiments. We observed that the model delivers predictions that are in very good agreement with the literature, demonstrating that NICeSim might be an important tool for supporting decision making in medical practice. Other very important characteristics of NICeSim are its flexibility and dynamism. NICeSim is flexible because it allows the inclusion and deletion of variables according to the requirements of a particular study. It is also dynamic because it trains a just-in-time model. Therefore, the system is improved as data from new patients become available. Finally, NICeSim can be extended in a cooperative manner because it is an open-source system.

As redes neurais artificiais e o ensino da medicina / Artificial neural networks and medical education

As transformações da prática médica nos últimos anos - sobretudo com a incorporação de novas tecnologias da informação - apontam a necessidade de ampliar as discussões sobre o processo ensino-aprendizagem na educação médica. A utilização de novas tecnologias computacionais no ensino médico tem demonstrado inúmeras vantagens no processo de aquisição de habilidades para a identificação e a resolução de problemas, o que estimula a criatividade, o senso crítico, a curiosidade e o espírito científico. Nesse contexto, ganham destaque as Redes Neurais Artificiais (RNA) - sistemas computacionais cuja estrutura matemática é inspirada no funcionamento do cérebro humano -, as quais têm sido úteis no processo ensino-aprendizagem e na avaliação de estudantes de Medicina. Com base nessas ponderações, o escopo da presente comunicação é revisar aspectos da aplicação das RNA na educação médica.

The transformations that medical practice has undergone in recent years - especially with the incorporation of new information technologies - point to the need to broaden discussions on the teaching-learning process in medical education. The use of new computer technologies in medical education has shown many advantages in the process of acquiring skills in problem solving, which encourages creativity, critical thinking, curiosity and scientific spirit. In this context, it is important to highlight artificial neural networks (ANN) - computer systems with a mathematical structure inspired by the human brain - which proved to be useful in the evaluation process and the acquisition of knowledge among medical students. The purpose of this communication is to review aspects of the application of ANN in medical education.

Modelos de tomada de decisão em bioética clínica: apontamentos para a abordagem computacional / Models of decision making in clinical bioethics: notes for a computational approach

A bioética tem se tornado, nas últimas décadas, um tema de importância central para a prática clínica, por fornecer ferramentas teóricas para a tomada de decisão do profissional de saúde. A questão que se propõe diz respeito a como saber se a decisão é a mais apropriada, já que uma decisão na esfera clínica - quer se esteja atuando na atenção primária, secundária ou terciária - deve, necessariamente, ser acertada tanto do ponto de vista técnico, quanto do ponto de vista ético. A literatura tem apresentado diferentes modelos para a tomada de decisão no campo de análise da bioética clínica. Com base nessas ponderações, objetiva-se, no presente ensaio, apresentar apontamentos sobre (i) a tomada de decisão na área de bioética clínica e (ii) as possibilidades de abordagem computacional das decisões bioéticas...

Bioethics has become over the recent decades a central question to clinical practice, due to the fact that it provides theoretical tools for decision making in health care. The issue that arises concerns how to know whether the decision made is the most appropriate, considering that a clinic decision - whether working in primary, secondary, or tertiary care - must be accurate from both the technical and the ethical point of views. As a result, different models for decision making in clinical bioethics have been presented in the literature. Based on these considerations, the objective of this article is to point important issues about (i) decision making in the field of clinical bioethics and (ii) the possibilities of computational approaches to assist such decisions...

La bioética se ha convertido, en las últimas décadas, en un tema de gran importancia en la práctica clínica, proporcionando herramientas teóricas para la toma de decisiones de los profesionales de la salud. La pregunta que se plantea es cómo saber si la decisión es la más apropiada, puesto que una decisión en el ámbito clínico - si se está trabajando en la atención primaria, secundaria o terciaria - debe necesariamente ser correcta desde el punto de vista técnico, como el punto de vista ético. La literatura ha presentado diferentes modelos para la toma de decisiones en el ámbito del análisis de la bioética clínica. Sobre la base de estas consideraciones, el objetivo en el siguiente texto es presentar puntos sobre (i) la toma de decisiones en el ámbito de la bioética clínica y (ii) las posibilidades de un enfoque computacional de las decisiones bioéticas...

Linfócitos T CD4+CD25+ e a regulação do sistema imunológico: perspectivas para o entendimento fisiopatológico da sepse / CD4+CD25+ T lymphocytes and regulation of the immune system: perspectives for a pathophysiological understanding of sepsis

A resposta inflamatória sistêmica representa o evento patogênico central da sepse, subjazendo às manifestações clínicas e aos achados laboratoriais presentes nos enfermos. Inúmeras pesquisas têm demonstrado que os linfócitos T CD4+CD25+ - também conhecidos como células T reguladoras (Treg) - participam dos processos de desenvolvimento da sepse, em virtude de sua capacidade de suprimir a resposta imune. Com base nessas ideias, propôs-se, no presente artigo, a discussão do papel dos linfócitos Treg na sepse, com base na revisão da literatura com estratégia de busca definida (LILACS, PubMed e SciELO), tendo em vista duas abordagens principais: a participação dessas células nos processos de inflamação e imunidade, e as perspectivas de investigação fisiopatológica computacional da condição mórbida.

The systemic inflammatory response represents the core pathogenic event of sepsis, underlying clinical manifestations and laboratory findings in patients. Numerous studies have shown that CD4+CD25+ T lymphocytes, also known as regulatory T lymphocytes (Treg), participate in the development of sepsis due to their ability to suppress the immune response. The present article discusses the role of Treg lymphocytes in sepsis based on a specific search strategy (Latin American and Caribbean Health Sciences / Literatura Latino-americana e do Caribe em Ciências da Saúde - LILACS, PubMed, and Scientific Electronic Library Online - SciELO) focusing on two main topics: the participation of Treg cells in inflammation and immunity as well as perspectives in the computational physiological investigation of sepsis.

CD4+CD25+ T lymphocytes and regulation of the immune system: perspectives for a pathophysiological understanding of sepsis. / Linfócitos T CD4+CD25+ e a regulação do sistema imunológico: perspectivas para o entendimento fisiopatológico da sepse.

The systemic inflammatory response represents the core pathogenic event of sepsis, underlying clinical manifestations and laboratory findings in patients. Numerous studies have shown that CD4+CD25+ T lymphocytes, also known as regulatory T lymphocytes (Treg), participate in the development of sepsis due to their ability to suppress the immune response. The present article discusses the role of Treg lymphocytes in sepsis based on a specific search strategy (Latin American and Caribbean Health Sciences / Literatura Latino-americana e do Caribe em Ciências da Saúde - LILACS, PubMed, and Scientific Electronic Library Online - SciELO) focusing on two main topics: the participation of Treg cells in inflammation and immunity as well as perspectives in the computational physiological investigation of sepsis.