Python workflow for the selection and identification of marker peptides-proof-of-principle study with heated milk.

The analysis of almost holistic food profiles has developed considerably over the last years. This has also led to larger amounts of data and the ability to obtain more information about health-beneficial and adverse constituents in food than ever before. Especially in the field of proteomics, software is used for evaluation, and these do not provide specific approaches for unique monitoring questions. An additional and more comprehensive way of evaluation can be done with the programming language Python. It offers broad possibilities by a large ecosystem for mass spectrometric data analysis, but needs to be tailored for specific sets of features, the research questions behind. It also offers the applicability of various machine-learning approaches. The aim of the present study was to develop an algorithm for selecting and identifying potential marker peptides from mass spectrometric data. The workflow is divided into three steps: (I) feature engineering, (II) chemometric data analysis, and (III) feature identification. The first step is the transformation of the mass spectrometric data into a structure, which enables the application of existing data analysis packages in Python. The second step is the data analysis for selecting single features. These features are further processed in the third step, which is the feature identification. The data used exemplarily in this proof-of-principle approach was from a study on the influence of a heat treatment on the milk proteome/peptidome.

Neural Network-Based Study about Correlation Model between TCM Constitution and Physical Examination Indexes Based on 950 Physical Examinees.

Purpose: To establish the correlation model between Traditional Chinese Medicine (TCM) constitution and physical examination indexes by backpropagation neural network (BPNN) technology. A new method for the identification of TCM constitution in clinics is proposed, which is trying to solve the problem like shortage of TCM doctor, complicated process, low efficiency, and unfavorable application in the current TCM constitution identification methods. Methods: The corresponding effective samples were formed by sorting out and classifying the original data which were collected from physical examination indexes and TCM constitution types of 950 physical examinees, who were examined at the affiliated hospital of Chengdu University of TCM. The BPNN algorithm was implemented using the C# programming language and Google's AI library. Then, the training group and the test (validation) group of the effective samples were, respectively, input into the algorithm, to complete the construction and validation of the target model. Results: For all the correlation models built in this paper, the accuracy of the training group and the test group of entire physical examination indexes-constitutional-type network model, respectively, was 88% and 53%, and the error was 0.001. For the other network models, the accuracy of the learning group and the test group and error, respectively, was as follows: liver function (31%, 42%, and 11.7), renal function (41%, 38%, and 6.7), blood routine (56%, 42%, and 2.4), and urine routine (60%, 40%, and 2.6). Conclusions: The more the physical examination indexes are used in training, the more accurate the network model is established to predict TCM constitution. The sample data used in this paper showed that there was a relatively strong correlation between TCM constitution and physical examination indexes. Construction of the correlation model between physical examination indexes and TCM constitution is a kind of study for the integration of Chinese and Western medicine, which provides a new approach for the identification of TCM constitution, and it may be expected to avoid the existing problem of TCM constitution identification at present.

Using collaboration networks to identify authorship dependence in meta-analysis results.

Meta-analytic methods are powerful resources to summarize the existing evidence concerning a given research question and are widely used in many academic fields. Meta-analyzes can also be used to study sources of heterogeneity and bias among results, which should be considered to avoid inaccuracies. Many of these sources can be related to study authorship, as both methodological heterogeneity and researcher bias may lead to deviations in results between different research groups. In this work, we describe a method to objectively attribute study authorship within a given meta-analysis to different research groups by using graph cluster analysis of collaboration networks. We then provide empirical examples of how the research group of origin can impact effect size in distinct types of meta-analyzes, demonstrating how non-independence between within-group results can bias effect size estimates if uncorrected. Finally, we show that multilevel random-effects models using research group as a level of analysis can be a simple tool for correcting for authorship dependence in results.

Quantification of Uncoupled Spin Domains in Spin-Abundant Disordered Solids.

Materials often contain minor heterogeneous phases that are difficult to characterize yet nonetheless significantly influence important properties. Here we describe a solid-state NMR strategy for quantifying minor heterogenous sample regions containing dilute, essentially uncoupled nuclei in materials where the remaining nuclei experience heteronuclear dipolar couplings. NMR signals from the coupled nuclei are dephased while NMR signals from the uncoupled nuclei can be amplified by one or two orders of magnitude using Carr-Meiboom-Purcell-Gill (CPMG) acquisition. The signal amplification by CPMG can be estimated allowing the concentration of the uncoupled spin regions to be determined even when direct observation of the uncoupled spin NMR signal in a single pulse experiment would require an impractically long duration of signal averaging. We use this method to quantify residual graphitic carbon using 13C CPMG NMR in poly(carbon monofluoride) samples synthesized by direct fluorination of carbon from various sources. Our detection limit for graphitic carbon in these materials is better than 0.05 mol%. The accuracy of the method is discussed and comparisons to other methods are drawn.

MUBD-DecoyMaker 2.0: A Python GUI Application to Generate Maximal Unbiased Benchmarking Data Sets for Virtual Drug Screening.

Ligand enrichment assessment based on benchmarking data sets has become a necessity for the rational selection of the best-suited approach for prospective data mining of drug-like molecules. Up to now, a variety of benchmarking data sets had been generated and frequently used. Among them, MUBD-HDACs from our prior research efforts was regarded as one of five state-of-the-art benchmarks in 2017 by Frontiers in Pharmacology. This benchmarking set was generated by one of our unique de-biasing algorithms. It also rendered quite a few other cases of successful applications in recent years, thus is expected to have more impact in modern drug discovery. To make our algorithm amenable to more users, we developed a Python GUI application called MUBD-DecoyMaker 2.0. Moreover, it has two new additional functional modules, i. e. "Detect 2D Bias" and "Quality Control". This new GUI version had been proved to be easy to use while generate benchmarking data sets of the same quality. MUBD-DecoyMaker 2.0 is freely available at https://github.com/jwxia2014/MUBD-DecoyMaker2.0, along with its manual and testcase.

Gumpy: a Python toolbox suitable for hybrid brain-computer interfaces.

OBJECTIVE: The objective of this work is to present gumpy, a new free and open source Python toolbox designed for hybrid brain-computer interface (BCI). APPROACH: Gumpy provides state-of-the-art algorithms and includes a rich selection of signal processing methods that have been employed by the BCI community over the last 20 years. In addition, a wide range of classification methods that span from classical machine learning algorithms to deep neural network models are provided. Gumpy can be used for both EEG and EMG biosignal analysis, visualization, real-time streaming and decoding. RESULTS: The usage of the toolbox was demonstrated through two different offline example studies, namely movement prediction from EEG motor imagery, and the decoding of natural grasp movements with the applied finger forces from surface EMG (sEMG) signals. Additionally, gumpy was used for real-time control of a robot arm using steady-state visually evoked potentials (SSVEP) as well as for real-time prosthetic hand control using sEMG. Overall, obtained results with the gumpy toolbox are comparable or better than previously reported results on the same datasets. SIGNIFICANCE: Gumpy is a free and open source software, which allows end-users to perform online hybrid BCIs and provides different techniques for processing and decoding of EEG and EMG signals. More importantly, the achieved results reveal that gumpy's deep learning toolbox can match or outperform the state-of-the-art in terms of accuracy. This can therefore enable BCI researchers to develop more robust decoding algorithms using novel techniques and hence chart a route ahead for new BCI improvements.

clusterExperiment and RSEC: A Bioconductor package and framework for clustering of single-cell and other large gene expression datasets.

Clustering of genes and/or samples is a common task in gene expression analysis. The goals in clustering can vary, but an important scenario is that of finding biologically meaningful subtypes within the samples. This is an application that is particularly appropriate when there are large numbers of samples, as in many human disease studies. With the increasing popularity of single-cell transcriptome sequencing (RNA-Seq), many more controlled experiments on model organisms are similarly creating large gene expression datasets with the goal of detecting previously unknown heterogeneity within cells. It is common in the detection of novel subtypes to run many clustering algorithms, as well as rely on subsampling and ensemble methods to improve robustness. We introduce a Bioconductor R package, clusterExperiment, that implements a general and flexible strategy we entitle Resampling-based Sequential Ensemble Clustering (RSEC). RSEC enables the user to easily create multiple, competing clusterings of the data based on different techniques and associated tuning parameters, including easy integration of resampling and sequential clustering, and then provides methods for consolidating the multiple clusterings into a final consensus clustering. The package is modular and allows the user to separately apply the individual components of the RSEC procedure, i.e., apply multiple clustering algorithms, create a consensus clustering or choose tuning parameters, and merge clusters. Additionally, clusterExperiment provides a variety of visualization tools for the clustering process, as well as methods for the identification of possible cluster signatures or biomarkers. The R package clusterExperiment is publicly available through the Bioconductor Project, with a detailed manual (vignette) as well as well documented help pages for each function.

Model selection for clustering of pharmacokinetic responses.

BACKGROUND AND OBJECTIVE: Pharmacokinetics comprises the study of drug absorption, distribution, metabolism and excretion over time. Clinical pharmacokinetics, focusing on therapeutic management, offers important insights towards personalised medicine through the study of efficacy and toxicity of drug therapies. This study is hampered by subject's high variability in drug blood concentration, when starting a therapy with the same drug dosage. Clustering of pharmacokinetics responses has been addressed recently as a way to stratify subjects and provide different drug doses for each stratum. This clustering method, however, is not able to automatically determine the correct number of clusters, using an user-defined parameter for collapsing clusters that are closer than a given heuristic threshold. We aim to use information-theoretical approaches to address parameter-free model selection. METHODS: We propose two model selection criteria for clustering pharmacokinetics responses, founded on the Minimum Description Length and on the Normalised Maximum Likelihood. RESULTS: Experimental results show the ability of model selection schemes to unveil the correct number of clusters underlying the mixture of pharmacokinetics responses. CONCLUSIONS: In this work we were able to devise two model selection criteria to determine the number of clusters in a mixture of pharmacokinetics curves, advancing over previous works. A cost-efficient parallel implementation in Java of the proposed method is publicly available for the community.

Data-driven knowledge acquisition, validation, and transformation into HL7 Arden Syntax.

OBJECTIVE: The objective of this study is to help a team of physicians and knowledge engineers acquire clinical knowledge from existing practices datasets for treatment of head and neck cancer, to validate the knowledge against published guidelines, to create refined rules, and to incorporate these rules into clinical workflow for clinical decision support. METHODS AND MATERIALS: A team of physicians (clinical domain experts) and knowledge engineers adapt an approach for modeling existing treatment practices into final executable clinical models. For initial work, the oral cavity is selected as the candidate target area for the creation of rules covering a treatment plan for cancer. The final executable model is presented in HL7 Arden Syntax, which helps the clinical knowledge be shared among organizations. We use a data-driven knowledge acquisition approach based on analysis of real patient datasets to generate a predictive model (PM). The PM is converted into a refined-clinical knowledge model (R-CKM), which follows a rigorous validation process. The validation process uses a clinical knowledge model (CKM), which provides the basis for defining underlying validation criteria. The R-CKM is converted into a set of medical logic modules (MLMs) and is evaluated using real patient data from a hospital information system. RESULTS: We selected the oral cavity as the intended site for derivation of all related clinical rules for possible associated treatment plans. A team of physicians analyzed the National Comprehensive Cancer Network (NCCN) guidelines for the oral cavity and created a common CKM. Among the decision tree algorithms, chi-squared automatic interaction detection (CHAID) was applied to a refined dataset of 1229 patients to generate the PM. The PM was tested on a disjoint dataset of 739 patients, which gives 59.0% accuracy. Using a rigorous validation process, the R-CKM was created from the PM as the final model, after conforming to the CKM. The R-CKM was converted into four candidate MLMs, and was used to evaluate real data from 739 patients, yielding efficient performance with 53.0% accuracy. CONCLUSION: Data-driven knowledge acquisition and validation against published guidelines were used to help a team of physicians and knowledge engineers create executable clinical knowledge. The advantages of the R-CKM are twofold: it reflects real practices and conforms to standard guidelines, while providing optimal accuracy comparable to that of a PM. The proposed approach yields better insight into the steps of knowledge acquisition and enhances collaboration efforts of the team of physicians and knowledge engineers.

Self-Anamnesis with a Conversational User Interface: Concept and Usability Study.

OBJECTIVE: Self-anamnesis is a procedure in which a patient answers questions about the personal medical history without interacting directly with a doctor or medical assistant. If collected digitally, the anamnesis data can be shared among the health care team. In this article, we introduce a concept for digital anamnesis collection and assess the applicability of a conversational user interface (CUI) for realizing a mobile self-anamnesis application. MATERIALS AND METHODS: We implemented our concept for self-anamnesis for the concrete field of music therapy. We collected requirements with respect to the application from music therapists and by reviewing the literature. A rule-based approach was chosen for realizing the anamnesis conversation between the system and the user. The Artificial Intelligence Markup Language was exploited for encapsulating the questions and responses of the system. For studying the quality of the system and analyzing performance, humanity, effect, and accessibility of the system, we performed a usability test with 22 persons. RESULTS: The current version of the self-anamnesis application is equipped with 63 questions on the music biography of a patient that are asked subsequently to the user by means of a chatbot conversation. The usability study showed that a CUI is a practical way for collecting anamnesis data. Users felt engaged of answering the questions and liked the human characteristics of the chatbot. They suggested to extend the conversation capabilities of the chatbot so that the system can react appropriately, in particular when the user is not feeling well. CONCLUSIONS: We could demonstrate the applicability of a CUI for collecting anamnesis data. In contrast to digital anamnesis questionnaires, the application of a CUI provides several benefits: the user can be encouraged to complete all queries and can ask clarifying questions in case something is unclear.

HitWalker2: visual analytics for precision medicine and beyond.

UNLABELLED: The lack of visualization frameworks to guide interpretation and facilitate discovery is a potential bottleneck for precision medicine, systems genetics and other studies. To address this we have developed an interactive, reproducible, web-based prioritization approach that builds on our earlier work. HitWalker2 is highly flexible and can utilize many data types and prioritization methods based upon available data and desired questions, allowing it to be utilized in a diverse range of studies such as cancer, infectious disease and psychiatric disorders. AVAILABILITY AND IMPLEMENTATION: Source code is freely available at https://github.com/biodev/HitWalker2 and implemented using Python/Django, Neo4j and Javascript (D3.js and jQuery). We support major open source browsers (e.g. Firefox and Chromium/Chrome). CONTACT: wilmotb@ohsu.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Additional information/instructions are available at https://github.com/biodev/HitWalker2/wiki.

PancreApp: An Innovative Approach to Computational Individualization of Nutritional Therapy in Chronic Gastrointestinal Disorders.

Medical nutrition therapy has a pivotal role in the management of chronic gastrointestinal disorders, like chronic pancreatitis, inflammatory bowel diseases (Lesniowski-Crohn's disease and ulcerative colitis) or irritable bowel syndrome. The aim of this study is to develop, deploy and evaluate an interactive application for Windows and Android operating systems, which could serve as a digital diet diary and as an analysis and a prediction tool both for the patient and the doctor. The software is gathering details about patients' diet and associated fettle in order to estimate fettle change after future meals, specifically for an individual patient. In this paper we have described the process of idea development and application design, feasibility assessment using a phone survey, a preliminary evaluation on 6 healthy individuals and early results of a clinical trial, which is still an ongoing study. Results suggest that applied approximative approach (Shepard's method of 6-dimensional metric interpolation) has a potential to predict the fettle accurately; as shown in leave-one-out cross-validation (LOOCV).

Wyrm: A Brain-Computer Interface Toolbox in Python.

In the last years Python has gained more and more traction in the scientific community. Projects like NumPy, SciPy, and Matplotlib have created a strong foundation for scientific computing in Python and machine learning packages like scikit-learn or packages for data analysis like Pandas are building on top of it. In this paper we present Wyrm ( https://github.com/bbci/wyrm ), an open source BCI toolbox in Python. Wyrm is applicable to a broad range of neuroscientific problems. It can be used as a toolbox for analysis and visualization of neurophysiological data and in real-time settings, like an online BCI application. In order to prevent software defects, Wyrm makes extensive use of unit testing. We will explain the key aspects of Wyrm's software architecture and design decisions for its data structure, and demonstrate and validate the use of our toolbox by presenting our approach to the classification tasks of two different data sets from the BCI Competition III. Furthermore, we will give a brief analysis of the data sets using our toolbox, and demonstrate how we implemented an online experiment using Wyrm. With Wyrm we add the final piece to our ongoing effort to provide a complete, free and open source BCI system in Python.

Role of OpenEHR as an open source solution for the regional modelling of patient data in obstetrics.

This work investigates, whether openEHR with its reference model, archetypes and templates is suitable for the digital representation of demographic as well as clinical data. Moreover, it elaborates openEHR as a tool for modelling Hospital Information Systems on a regional level based on a national logical infrastructure. OpenEHR is a dual model approach developed for the modelling of Hospital Information Systems enabling semantic interoperability. A holistic solution to this represents the use of dual model based Electronic Healthcare Record systems. Modelling data in the field of obstetrics is a challenge, since different regions demand locally specific information for the process of treatment. Smaller health units in developing countries like Brazil or Malaysia, which until recently handled automatable processes like the storage of sensitive patient data in paper form, start organizational reconstruction processes. This archetype proof-of-concept investigation has tried out some elements of the openEHR methodology in cooperation with a health unit in Colombo, Brazil. Two legal forms provided by the Brazilian Ministry of Health have been analyzed and classified into demographic and clinical data. LinkEHR-Ed editor was used to read, edit and create archetypes. Results show that 33 clinical and demographic concepts, which are necessary to cover data demanded by the Unified National Health System, were identified. Out of the concepts 61% were reused and 39% modified to cover domain requirements. The detailed process of reuse, modification and creation of archetypes is shown. We conclude that, although a major part of demographic and clinical patient data were already represented by existing archetypes, a significant part required major modifications. In this study openEHR proved to be a highly suitable tool in the modelling of complex health data. In combination with LinkEHR-Ed software it offers user-friendly and highly applicable tools, although the complexity built by the vast specifications requires expert networks to define generally excepted clinical models. Finally, this project has pointed out main benefits enclosing high coverage of obstetrics data on the Clinical Knowledge Manager, simple modelling, and wide network and support using openEHR. Moreover, barriers described are enclosing the allocation of clinical content to respective archetypes, as well as stagnant adaption of changes on the Clinical Knowledge Manager leading to redundant efforts in data contribution that need to be addressed in future works.

PyGaze: an open-source, cross-platform toolbox for minimal-effort programming of eyetracking experiments.

The PyGaze toolbox is an open-source software package for Python, a high-level programming language. It is designed for creating eyetracking experiments in Python syntax with the least possible effort, and it offers programming ease and script readability without constraining functionality and flexibility. PyGaze can be used for visual and auditory stimulus presentation; for response collection via keyboard, mouse, joystick, and other external hardware; and for the online detection of eye movements using a custom algorithm. A wide range of eyetrackers of different brands (EyeLink, SMI, and Tobii systems) are supported. The novelty of PyGaze lies in providing an easy-to-use layer on top of the many different software libraries that are required for implementing eyetracking experiments. Essentially, PyGaze is a software bridge for eyetracking research.

Reasoning based quality assurance of medical ontologies: a case study.

The World Health Organisation is using OWL as a key technology to develop ICD-11 - the next version of the well-known International Classification of Diseases. Besides providing better opportunities for data integration and linkages to other well-known ontologies such as SNOMED-CT, one of the main promises of using OWL is that it will enable various forms of automated error checking. In this paper we investigate how automated OWL reasoning, along with a Justification Finding Service can be used as a Quality Assurance technique for the development of large and complex ontologies such as ICD-11. Using the International Classification of Traditional Medicine (ICTM) - Chapter 24 of ICD-11 - as a case study, and an expert panel of knowledge engineers, we reveal the kinds of problems that can occur, how they can be detected, and how they can be fixed. Specifically, we found that a logically inconsistent version of the ICTM ontology could be repaired using justifications (minimal entailing subsets of an ontology). Although over 600 justifications for the inconsistency were initially computed, we found that there were three main manageable patterns or categories of justifications involving TBox and ABox axioms. These categories represented meaningful domain errors to an expert panel of ICTM project knowledge engineers, who were able to use them to successfully determine the axioms that needed to be revised in order to fix the problem. All members of the expert panel agreed that the approach was useful for debugging and ensuring the quality of ICTM.

Android-based access to holistic emergency care record.

This paper is concerned with the development of an Emergency Medical Services (EMS) system which interfaces with a Holistic Emergency Care Record (HECR) that aims at managing emergency care holistically by supporting EMS processes and is accessible by Android-enabled mobile devices.

Ontology-oriented diagnostic system for traditional Chinese medicine based on relation refinement.

Although Chinese medicine treatments have become popular recently, the complicated Chinese medical knowledge has made it difficult to be applied in computer-aided diagnostics. The ability to model and use the knowledge becomes an important issue. In this paper, we define the diagnosis in Traditional Chinese Medicine (TCM) as discovering the fuzzy relations between symptoms and syndromes. An Ontology-oriented Diagnosis System (ODS) is created to address the knowledge-based diagnosis based on a well-defined ontology of syndromes. The ontology transforms the implicit relationships among syndromes into a machine-interpretable model. The clinical data used for feature selection is collected from a national TCM research institute in China, which serves as a training source for syndrome differentiation. The ODS analyzes the clinical cases to obtain a statistical mapping relation between each syndrome and associated symptom set, before rechecking the completeness of related symptoms via ontology refinement. Our diagnostic system provides an online web interface to interact with users, so that users can perform self-diagnosis. We tested 12 common clinical cases on the diagnosis system, and it turned out that, given the agree metric, the system achieved better diagnostic accuracy compared to nonontology method-92% of the results fit perfectly with the experts' expectations.

A4S: a user-friendly graphical tool for pharmacokinetic and pharmacodynamic (PK/PD) simulation.

Effective communication of PK/PD principles and results in a biomedical research environment remains a significant challenge which can result in lack of buy-in and engagement from scientists outside the modeling and simulation communities. In our view, one of the barriers in this area is a lack of user-friendly tools which allow "non experts" to use PK/PD models without the need to develop technical skills and expertise in advanced mathematical principles and specialist software. The costs of commercial software may also prevent large-scale distribution. One attempt to address this issue internally in our research organizations has resulted in the development of the A4S ("Accelera for Sandwich") software, which is a simple-to-use, menu-drive Matlab-based PK/PD simulator targeted at biomedical researchers with little PK/PD experience.

Evolution in clinical knowledge management strategy at Intermountain Healthcare.

In this manuscript, we present an overview of the clinical knowledge management strategy at Intermountain Healthcare in support of our electronic medical record systems. Intermountain first initiated efforts in developing a centralized enterprise knowledge repository in 2001. Applications developed, areas of emphasis served, and key areas of focus are presented. We also detail historical and current areas of emphasis, in response to business needs.