12 Result(s)
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Chapter and Conference Paper
Weighting Scheme Methods for Enhanced Genomic Annotation Prediction
Functional genomic annotation data banks, which store the associations between genes (or a gene products) and terms of controlled vocabularies describing their features, are paramount in computational biology....
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Chapter and Conference Paper
Extended Spearman and Kendall Coefficients for Gene Annotation List Correlation
Gene annotations are a key concept in bioinformatics and computational methods able to predict them are a fundamental contribution to the field. Several machine learning algorithms are available in this domain...
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Article
Open AccessComputational algorithms to predict Gene Ontology annotations
Gene function annotations, which are associations between a gene and a term of a controlled vocabulary describing gene functional features, are of paramount importance in modern biology. Datasets of these anno...
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Chapter and Conference Paper
Validation Pipeline for Computational Prediction of Genomics Annotations
Controlled biomolecular annotations are key concepts in computational genomics and proteomics, since they can describe the functional features of genes and their products in both a simple and computational way...
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Article
Open AccessTen quick tips for machine learning in computational biology
Machine learning has become a pivotal tool for many projects in computational biology, bioinformatics, and health informatics. Nevertheless, beginners and biomedical researchers often do not have enough experi...
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Article
Open AccessData analytics and clinical feature ranking of medical records of patients with sepsis
Sepsis is a life-threatening clinical condition that happens when the patient’s body has an excessive reaction to an infection, and should be treated in one hour. Due to the urgency of sepsis, doctors and phys...
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Article
Open AccessThe Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation
Evaluating binary classifications is a pivotal task in statistics and machine learning, because it can influence decisions in multiple areas, including for example prognosis or therapies of patients in critica...
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Article
Open AccessTowards a potential pan-cancer prognostic signature for gene expression based on probesets and ensemble machine learning
Cancer is one of the leading causes of death worldwide and can be caused by environmental aspects (for example, exposure to asbestos), by human behavior (such as smoking), or by genetic factors. To understand ...
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Article
Open AccessThe 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 ...
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Article
Open AccessTen 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 pract...
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Article
Open AccessSignature literature review reveals AHCY, DPYSL3, and NME1 as the most recurrent prognostic genes for neuroblastoma
Neuroblastoma is a childhood neurological tumor which affects hundreds of thousands of children worldwide, and information about its prognosis can be pivotal for patients, their families, and clinicians. One o...
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Article
Open AccessClinical Feature Ranking Based on Ensemble Machine Learning Reveals Top Survival Factors for Glioblastoma Multiforme
Glioblastoma multiforme (GM) is a malignant tumor of the central nervous system considered to be highly aggressive and often carrying a terrible survival prognosis. An accurate prognosis is therefore pivotal f...
