Estimating the chance of success in IVF treatment using a ranking algorithm

In medicine, estimating the chance of success for treatment is important in deciding whether to begin the treatment or not. This paper focuses on the domain of in vitro fertilization (IVF), where estimating the outcome of a treatment is very crucial in the decision to proceed with treatment for both the clinicians and the infertile couples. IVF treatment is a stressful and costly process. It is very stressful for couples who want to have a baby. If an initial evaluation indicates a low pregnancy rate, decision of the couple may change not to start the IVF treatment. The aim of this study is twofold, firstly, to develop a technique that can be used to estimate the chance of success for a couple who wants to have a baby and secondly, to determine the attributes and their particular values affecting the outcome in IVF treatment. We propose a new technique, called success estimation using a ranking algorithm (SERA), for estimating the success of a treatment using a ranking-based algorithm. The particular ranking algorithm used here is RIMARC. The performance of the new algorithm is compared with two well-known algorithms that assign class probabilities to query instances. The algorithms used in the comparison are Naïve Bayes Classifier and Random Forest. The comparison is done in terms of area under the ROC curve, accuracy and execution time, using tenfold stratified cross-validation. The results indicate that the proposed SERA algorithm has a potential to be used successfully to estimate the probability of success in medical treatment. © 2015, The Author(s)

Not to be sniffed at: repurposing existing RNA-seq data to examine changes to human nasal microbiome composition in COVID-19 infection, compared to influenza and healthy controls

Objective: The human microbiome is essential in maintaining healthy physiology; compositional changes have been implicated in numerous physical and mental diseases. Thus far, COVID-19 microbiome research has focused primarily upon gut and lung bacterial communities. However, the early stages of COVID-19 infection and immune response occur in the nasal epithelium. Therefore, investigating nasal microbiome changes in early-stage COVID-19 may yield key insights into the immune system mechanisms involved in progression from mild/no symptoms to systemic organ failure/death, why this occurs in certain individuals, and how it may be prevented with early warning.Patients and Methods: Here we repurposed existing RNA-seq data to characterise the human nasal microbiome in COVID-19 infected samples and compared the taxonomic profile to healthy control and influenza-infected control samples, to identify COVID-19 specific nasal microbiome changes and attempt to rationalise these in the context of what is already known regarding mechanisms of the immune response to COVID-19.Results: We demonstrate that existing RNA-seq reads from human nasal swabs can be repurposed to characterize the human nasal microbiome robustly and accurately in health, early-stage COVID-19, and influenza. We observe that nasal microbiome composition (presence and abundance of phyla, genera, and species) significantly differs between health and disease, and between COVID-19 and influenza.Conclusions: Our observed healthy nasal microbial profiles match the findings of previous research, demonstrating that repurposing existing RNA-seq data is as accurate as targeted methods for taxonomic classification. We also observed many differential changes in the nasal microbiome profile to be disease specific. This will be key to enabling the potential for differential diagnosis based upon nasal microbiome profiles in the future

Annotations for Rule-Based Models

The chapter reviews the syntax to store machine-readable annotations and describes the mapping between rule-based modelling entities (e.g., agents and rules) and these annotations. In particular, we review an annotation framework and the associated guidelines for annotating rule-based models of molecular interactions, encoded in the commonly used Kappa and BioNetGen languages, and present prototypes that can be used to extract and query the annotations. An ontology is used to annotate models and facilitate their description

SBOL-OWL: An ontological approach for formal and semantic representation of synthetic biology information

Standard representation of data is key for the reproducibility of designs in synthetic biology. The Synthetic Biology Open Language (SBOL) has already emerged as a data standard to represent information about genetic circuits, and it is based on capturing data using graphs. The language provides the syntax using a free text document that is accessible to humans only. This paper describes SBOL-OWL, an ontology for a machine understandable definition of SBOL. This ontology acts as a semantic layer for genetic circuit designs. As a result, computational tools can understand the meaning of design entities in addition to parsing structured SBOL data. SBOL-OWL not only describes how genetic circuits can be constructed computationally, it also facilitates the use of several existing Semantic Web tools for synthetic biology. This paper demonstrates some of these features, for example, to validate designs and check for inconsistencies. Through the use of SBOL-OWL, queries can be simplified and become more intuitive. Moreover, existing reasoners can be used to infer information about genetic circuit designs that cannot be directly retrieved using existing querying mechanisms. This ontological representation of the SBOL standard provides a new perspective to the verification, representation, and querying of information about genetic circuits and is important to incorporate complex design information via the integration of biological ontologies

SBOL visual 2 ontology

Standardizing the visual representation of genetic parts and circuits is essential for unambiguously creating and interpreting genetic designs. To this end, an increasing number of tools are adopting well-defined glyphs from the Synthetic Biology Open Language (SBOL) Visual standard to represent various genetic parts and their relationships. However, the implementation and maintenance of the relationships between biological elements or concepts and their associated glyphs has up to now been left up to tool developers. We address this need with the SBOL Visual 2 Ontology, a machine-accessible resource that provides rules for mapping from genetic parts, molecules, and interactions between them, to agreed SBOL Visual glyphs. This resource, together with a web service, can be used as a library to simplify the development of visualization tools, as a stand-alone resource to computationally search for suitable glyphs, and to help facilitate integration with existing biological ontologies and standards in synthetic biology