Computational methods in the analysis of SARS-CoV-2 in mammals: A systematic review of the literature.

SARS-CoV-2 is an enveloped RNA virus that causes severe respiratory illness in humans and animals. It infects cells by binding the Spike protein to the host's angiotensin-converting enzyme 2 (ACE2). The bat is considered the natural host of the virus, and zoonotic transmission is a significant risk and can happen when humans come into close contact with infected animals. Therefore, understanding the interconnection between human, animal, and environmental health is important to prevent and control future coronavirus outbreaks. This work aimed to systematically review the literature to identify characteristics that make mammals suitable virus transmitters and raise the main computational methods used to evaluate SARS-CoV-2 in mammals. Based on this review, it was possible to identify the main factors related to transmissions mentioned in the literature, such as the expression of ACE2 and proximity to humans, in addition to identifying the computational methods used for its study, such as Machine Learning, Molecular Modeling, Computational Simulation, between others. The findings of the work contribute to the prevention and control of future outbreaks, provide information on transmission factors, and highlight the importance of advanced computational methods in the study of infectious diseases that allow a deeper understanding of transmission patterns and can help in the development of more effective control and intervention strategies.

Prediction of enzymatic function with high efficiency and a reduced number of features using genetic algorithm.

The post-genomic era has raised a growing demand for efficient procedures to identify protein functions, which can be accomplished by applying machine learning to the characteristics set extracted from the protein. This approach is feature-based and has been the focus of several works in bioinformatics. In this work, we investigated the characteristics of proteins, representing the primary, secondary, tertiary, and quaternary structures of the protein, that improve the model's quality by applying dimensionality reduction techniques and using the Support Vector Machine classifier for predicting the enzymes' classes. During the investigation, two approaches were evaluated: feature extraction/transformation, which was performed using the statistical technique Factor Analysis, and feature selection methods. For feature selection, we proposed an approach based on a genetic algorithm to face the optimization conflict between the simplicity and reliability of an ideal representation of the characteristics of the enzymes and also compared and employed other methods for this purpose. The best result was accomplished using a feature subset generated by our implementation of a multi-objective genetic algorithm enriched with features that this work identified as relevant to represent the enzymes. This subset representation reduced the dataset by about 87% and reached 85.78% of F-measure performance, improving the overall quality of the model classification. In addition, we verified in this work a subset addressed with only 28 features out of a total of 424 that reached a performance above 80% of F-measure for four of the six evaluated classes, showing that satisfactory classification performance can be achieved with a reduced number of enzymes's characteristics. The datasets and implementations are openly available.

The Use of Convolutional Neural Networks in the Prediction of Invasive Ductal Carcinoma in Histological Images of Breast Cancer.

This paper presents a deep learning approach for automatic detection and visual analysis of Invasive Ductal Carcinoma (IDC) tissue regions. The method proposed in this work is a convolutional neural network (CNN) for visual semantic analysis of tumor regions for diagnostic support. Detection of IDC is a time-consuming and challenging task, mainly because a pathologist needs to examine large tissue regions to identify areas of malignancy. Deep Learning approaches are particularly suitable for dealing with this type of problem, especially when many samples are available for training, ensuring high quality of the learned features by the classifier and, consequently, its generalization capacity. A 3-hidden-layer CNN with data balancing reached both accuracy and F1-Score of 0.85 and outperforming other approaches from the literature. Thus, the proposed method in this article can serve as a support tool for the identification of invasive breast cancer.

Predictions of Academic Performance of Children and Adolescents with ADHD Using the SHAP Approach.

Attention-Deficit/Hyperactivity Disorder (ADHD) is a neuro-developmental disorder characterized by inattention and/or impulsivity-hyperactivity symptoms. Through Machine Learning methods and the SHAP approach, this work aims to discover which features have the most significant impact on the students' performance with ADHD in arithmetic, writing and reading. The SHAP allowed us to deepen the model's understanding and identify the most relevant features for academic performance. The experiments indicated that the Raven_Z IQ test score is the factor with the most significant impact on academic performance in all disciplines. Then, the mother's schooling, being from a private school, and the student's social class were the most frequently highlighted features. In all disciplines, the student having ADHD emerged as an important feature with a negative impact but less relevance than the previous features.

Applying Formal Concept Analysis for the Recognition of Infant Mortality Patterns.

Infant mortality (IM), an index that corresponds to the number of deaths among children up to one year, is an important social indicator of a region. It generally reflects the conditions of socioeconomic development - in addition, the access and quality of resources available for maternal and child health care. Monitoring its magnitude, thus, can help in the definition of public policies for its confrontation. The main causes of IM can be also associated with biological, behavioral, and public health issues. In this work, implication and association rules based on Formal Concept Analysis are used to recognize patterns in births occurring in the state of Amapá (located in the Brazilian Amazon), where the index of infant mortality is more severe.

Characterizing Infant Mortality Using Data Mining - A Case Study in Two Brazilian States - Santa Catarina and Amapá.

Infant mortality is characterized by the death of young children under the age of one, and it is an issue affecting millions of children in the world. The objective of this article is to employ concepts of knowledge discovery in databases, specifically of machine learning in the data mining phase, to characterize infant mortality in two states of Brazil: Santa Catarina, with the lowest infant mortality rate of the country's states, and Amapá, with the highest. The classifiers C4.5, JRip, Random Forest, SVM, and Multilayer Perceptron were used, and a brief comparison of the results obtained by the classifiers in both states is made. In addition, the dataset preprocessing is detailed, which includes attribute selection and class balancing. The results show that the features APGAR5, WEIGHT, and CONGENITAL ANOMALY stood out the most from the rules generated by the tree-based classifiers.

Interpreting the Human Longevity Profile Through Triadic Rules - A Case Study Based on the ELSA-UK Longitudinal Study.

Human aging is a complex process with several factors interacting. One of the ways to identify patterns about human aging is longitudinal population studies. In this work, we identified longevity profiles through a process of knowledge discovery. After identifying the profiles, we apply triadic rules which allow extracting rules of implication with conditions. These rules can be used to identify related factors, in the various waves, of longitudinal studies, which can better explain the conditions that favor longevity profiles.The results show that the triadic analysis is efficient to allow the analysis of the temporal evolution of clinical or environmental conditions that favor certain profiles when databases of longitudinal studies are considered.

How Is the Quality of Information in a Cancer Hospital Regarding the Completeness and Consistency? A Case Study of the Hospital Cancer Registry of the Brazilian Hospital Bom Pastor.

This study aimed to analyze the measurement of information quality in the Information System of the Brazilian Hospital in terms of data completeness and consistency. The quality of information is a fundamental factor in evaluating the performance of information systems. Its use is a reasonable condition in the management of the health service and attention to the care of patients. The research had a retrospective, descriptive character exploratory with quantitative analysis of the data. The study had some limitations, we observed incomplete information regarding some studied variables; this is because the primary source of information in the cancer registry is the patient's medical record. Therefore, the patient's medical records are relevant and should contain the entire health history, from birth to death. In addition, they support research, the management of health services.

Assessment of the efficacy of the utilisation of conventional and electric toothbrushes by the older adults.

OBJECTIVE: To evaluate the efficacy of electric and conventional toothbrushes for a group of elderly individuals. BACKGROUND: Although the electric toothbrush has been recommended for elderly individuals, there had previously never been a study regarding its efficacy. MATERIAL AND METHODS: Sixty independent elders of both genders with different oral conditions from the Center Adult Day Vitória, Espírito Santo, Brazil, were randomly divided into two groups of 30 individuals. One group received the Oral B CrossAction Power electric toothbrush, whereas the other received a conventional Bitufo Class 32 soft toothbrush to perform oral hygiene. The bacterial plaque index (O'Leary Plaque Index) and DMFT index were assessed as a measure of oral hygiene and oral health. The data were analysed using the Shapiro-Wilk, Mann-Whitney and Wilcoxon tests. RESULTS: The results of the efficacy of the Oral B Cross Action Power electric toothbrush demonstrated that on the 7th and 15th days, the bacterial plaque indexes were 24.91 ± 12.81 and 22.11 ± 14.46, respectively, which corresponds to a 50.24% removal of bacterial plaque on the 7th and 55.83% on the 15th days. Although the electric toothbrush removed more bacterial plaque than the conventional toothbrush, the difference was not statistically significant. CONCLUSION: Both the conventional and the electric toothbrushes were effective in removing bacterial plaque within the elderly group. More studies are necessary to test the efficacy of electric toothbrushes in relation to conventional toothbrushes for elderly patients.

A Fast Parallel K-Modes Algorithm for Clustering Nucleotide Sequences to Predict Translation Initiation Sites.

Predicting the location of the translation initiation sites (TIS) is an important problem of molecular biology. In this field, the computational cost for balancing non-TIS sequences is substantial and demands high-performance computing. In this article, we present an optimized version of the K-modes algorithm to cluster TIS sequences and a comparison with the standard K-means clustering. The adapted algorithm uses simple instructions and fewer computational resources to deliver a significant speedup without compromising the sequence clustering results. We also implemented two optimized parallel versions of the algorithm, one for graphics processing units (GPUs) and the other one for general-purpose multicore processors. In our experiments, the GPU K-modes's performance was up to 203 times faster than the respective sequential version for processing Arabidopsis thaliana sequence.

Transductive learning as an alternative to translation initiation site identification.

BACKGROUND: The correct protein coding region identification is an important and latent problem in the molecular biology field. This problem becomes a challenge due to the lack of deep knowledge about the biological systems and unfamiliarity of conservative characteristics in the messenger RNA (mRNA). Therefore, it is fundamental to research for computational methods aiming to help the patterns discovery for identification of the Translation Initiation Sites (TIS). In the field of Bioinformatics, machine learning methods have been widely applied based on the inductive inference, as Inductive Support Vector Machine (ISVM). On the other hand, not so much attention has been given to transductive inference-based machine learning methods such as Transductive Support Vector Machine (TSVM). The transductive inference performs well for problems in which the amount of unlabeled sequences is considerably greater than the labeled ones. Similarly, the problem of predicting the TIS may take advantage of transductive methods due to the fact that the amount of new sequences grows rapidly with the progress of Genome Project that allows the study of new organisms. Consequently, this work aims to investigate the transductive learning towards TIS identification and compare the results with those obtained in inductive method. RESULTS: The transductive inference presents better results both in F-measure and in sensitivity in comparison with the inductive method for predicting the TIS. Additionally, it presents the least failure rate for identifying the TIS, presenting a smaller number of False Negatives (FN) than the ISVM. The ISVM and TSVM methods were validated with the molecules from the most representative organisms contained in the RefSeq database: Rattus norvegicus, Mus musculus, Homo sapiens, Drosophila melanogaster and Arabidopsis thaliana. The transductive method presented F-measure and sensitivity higher than 90% and also higher than the results obtained with ISVM. The ISVM and TSVM approaches were implemented in the TransduTIS tool, TransduTIS-I and TransduTIS-T respectively, available in a web interface. These approaches were compared with the TISHunter, TIS Miner, NetStart tools, presenting satisfactory results. CONCLUSIONS: In relation to precision, the results are similar for the ISVM and TSVM classifiers. However, the results show that the application of TSVM approach ensured an improvement, specially for F-measure and sensitivity. Moreover, it was possible to identify a potential for the application of TSVM, which is for organisms in the initial study phase with few identified sequences in the databases.

Improvement in the prediction of the translation initiation site through balancing methods, inclusion of acquired knowledge and addition of features to sequences of mRNA.

BACKGROUND: The accurate prediction of the initiation of translation in sequences of mRNA is an important activity for genome annotation. However, obtaining an accurate prediction is not always a simple task and can be modeled as a problem of classification between positive sequences (protein codifiers) and negative sequences (non-codifiers). The problem is highly imbalanced because each molecule of mRNA has a unique translation initiation site and various others that are not initiators. Therefore, this study focuses on the problem from the perspective of balancing classes and we present an undersampling balancing method, M-clus, which is based on clustering. The method also adds features to sequences and improves the performance of the classifier through the inclusion of knowledge obtained by the model, called InAKnow. RESULTS: Through this methodology, the measures of performance used (accuracy, sensitivity, specificity and adjusted accuracy) are greater than 93% for the Mus musculus and Rattus norvegicus organisms, and varied between 72.97% and 97.43% for the other organisms evaluated: Arabidopsis thaliana, Caenorhabditis elegans, Drosophila melanogaster, Homo sapiens, Nasonia vitripennis. The precision increases significantly by 39% and 22.9% for Mus musculus and Rattus norvegicus, respectively, when the knowledge obtained by the model is included. For the other organisms, the precision increases by between 37.10% and 59.49%. The inclusion of certain features during training, for example, the presence of ATG in the upstream region of the Translation Initiation Site, improves the rate of sensitivity by approximately 7%. Using the M-Clus balancing method generates a significant increase in the rate of sensitivity from 51.39% to 91.55% (Mus musculus) and from 47.45% to 88.09% (Rattus norvegicus). CONCLUSIONS: In order to solve the problem of TIS prediction, the results indicate that the methodology proposed in this work is adequate, particularly when using the concept of acquired knowledge which increased the accuracy in all databases evaluated.