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Actualment, moltes empreses realitzen tasques repetitives i de baix valor que estan exposades a errors humans i que es poden automatitzar. L'objectiu d'aquest projecte és robotitzar i automatitzar el procés de gestió de comandes dels distribuïdors locals d'una empresa mitjançant la seva plataforma de gestió de comandes. Currently, many companies perform repetitive, low-value tasks that are exposed to human error and can be automated. The objective of this project is to robotize and automate the order management process of a company's local distributors through its order management platform.

Today's software complexity makes developing defect-free software almost impossible. Consequently, developing classifiers to classify software modules into defective and non-defective before software releases have attracted great interest in academia and software industry alike. Although many classifiers have been proposed, no one has been proven superior over others. The major reason is that while a research shows that classifier A is better than classifier B, we can find other research that shows the opposite. These conflicts are usually triggered when researchers report results using their preferable performance evaluation measures such as, recall and precision. Although this approach is valid, it does not examine all possible facets of classifiers performance characteristics. Thus, the performance evaluation might improve or deteriorate if researchers choose other performance measures. As a result, software developers usually struggle to select the most suitable classifier to use in their projects. The goal of this paper is to apply the fuzzy analytical hierarchy process (FAHP) as a popular multicriteria decision-making technique to reliably evaluate classifiers' performance. This evaluation framework incorporates a wider spectrum of performance measures to evaluate classifiers performance rather than relying on selected preferable measures. The results show that this approach will increase software developers' confidence in research outcomes and help them in avoiding false conclusions and infer reasonable boundaries for them. We exploited 22 popular performance measures and 11 software defect classifiers. The analysis was carried out using KNIME data mining platform and 12 software defect data sets provided by the NASA metrics data program (MDP) repository.

Given the pertinence and acceleration of the spread of COVID-19, there is an increased need for the replicability of data models to verify the veracity of models and visualize important data. Most of these visualizations lack reproducibility, credibility, or accuracy, and are static, which makes it difficult to analyze the spread over time. Furthermore, most current visualizations depicting the spread of COVID-19 are at a global or country level, meaning there is a dearth of regional analysis within a country. Keeping these issues in mind, a replicable, efficient, and simple method to generate regional COVID-19 visualizations mapped with time was created by using the KNIME software, an open-source data analytics platform that can create user-friendly applications or workflows. For this analysis, Albania, Sweden, Ukraine, Denmark, Russia, India, and Australia were closely observed. Among the maps generated for the aforementioned countries, it was noticed that regions with a high population or high population density were often the epicenters within their respective country. The regions caused the virus to spread to their neighboring regions: kickstarting the “domino effect”, leading to the infection of another region until the country is overwhelmed with cases—what we call a proximity trend. These dynamic maps are crucial to fighting the pandemic because they can provide insight as to how COVID-19 spreads by providing researchers or officials with an accurate and insightful tool to aid their analysis. By being able to visualize the spread, health and government officials can dive deeper to identify the sources of transmission and attempt to stop or reverse them accordingly., Highlights • Established replicable and efficient workflow to easily generate COVID-19 visualizations • Revealed and analyzed proximity trends that exacerbated spread of COVID-19 • Visualizations have created valuable COVID-19 spatiotemporal insights • Workflow is flexible and can be applied to other COVID-19 studies and variables, such as demographic, socioeconomic, and policy factors

Quantitative measurements and qualitative description of scientific images are both important to describe the complexity of digital image data. While various software solutions for quantitative measurements in images exist, there is a lack of simple tools for the qualitative description of images in common user-oriented image analysis software. To address this issue, we developed a set of Fiji plugins that facilitate the systematic manual annotation of images or image-regions. From a list of user-defined keywords, these plugins generate an easy-to-use graphical interface with buttons or checkboxes for the assignment of single or multiple pre-defined categories to full images or individual regions of interest. In addition to qualitative annotations, any quantitative measurement from the standard Fiji options can also be automatically reported. Besides the interactive user interface, keyboard shortcuts are available to speed-up the annotation process for larger datasets. The annotations are reported in a Fiji result table that can be exported as a pre-formatted csv file, for further analysis with common spreadsheet software or custom automated pipelines. To illustrate possible use case of the annotations, and facilitate the analysis of the generated annotations, we provide examples of such pipelines, including data-visualization solutions in Fiji and KNIME, as well as a complete workflow for training and application of a deep learning model for image classification in KNIME. Ultimately, the plugins enable standardized routine sample evaluation, classification, or ground-truth category annotation of any digital image data compatible with Fiji.

Deep learning is developing as an important technology to perform various tasks in cheminformatics. In particular, graph convolutional neural networks (GCNs) have been reported to perform well in many types of prediction tasks related to molecules. Although GCN exhibits considerable potential in various applications, appropriate utilization of this resource for obtaining reasonable and reliable prediction results requires thorough understanding of GCN and programming. To leverage the power of GCN to benefit various users from chemists to cheminformaticians, an open-source GCN tool, kGCN, is introduced. To support the users with various levels of programming skills, kGCN includes three interfaces: a graphical user interface (GUI) employing KNIME for users with limited programming skills such as chemists, as well as command-line and Python library interfaces for users with advanced programming skills such as cheminformaticians. To support the three steps required for building a prediction model, i.e., pre-processing, model tuning, and interpretation of results, kGCN includes functions of typical pre-processing, Bayesian optimization for automatic model tuning, and visualization of the atomic contribution to prediction for interpretation of results. kGCN supports three types of approaches, single-task, multi-task, and multi-modal predictions. The prediction of compound-protein interaction for four matrixmetalloproteases, MMP-3, -9, -12 and -13, in the inhibition assays is performed as a representative case study using kGCN. Additionally, kGCN provides the visualization of atomic contributions to the prediction. Such visualization is useful for the validation of the prediction models and the design of molecules based on the prediction model, realizing “explainable AI” for understanding the factors affecting AI prediction. kGCN is available at https://github.com/clinfo.

Display Omitted False positive removal after gene recognition and normalization.Integration of existing annotations, acronym resolution and classification.Based on well established technologies in pharmaceutical industry.Improvement in precision while keeping recall high.We propose to use existing gene taggers to create corpora for common problematic cases. BackgroundGene name recognition and normalization is, together with detection of other named entities, a crucial step in biomedical text mining and the underlying basis for development of more advanced techniques like extraction of complex events. While the current state of the art solutions achieve highly promising results on average, performance can drop significantly for specific genes with highly ambiguous synonyms. Depending on the topic of interest, this can cause the need for extensive manual curation of such text mining results. Our goal was to enhance this curation step based on tools widely used in pharmaceutical industry utilizing the text processing and classification capabilities of the Konstanz Information Miner (KNIME) along with publicly available sources. ResultsF-score achieved on gene specific test corpora for highly ambiguous genes could be improved from values close to zero, due to very low precision, to values >0.9 for several cases. Interestingly the presented approach even resulted in an increased F-score for genes showing already good results in initial gene name normalization. For most test cases, we could significantly improve precision, while retaining a high recall. ConclusionsWe could show that KNIME can be used to assist in manual curation of text mining results containing high numbers of false positive hits. Our results also indicate that it could be beneficial for future development in the field of gene name normalization to create gene specific training corpora based on incorrectly identified genes common to current state of the art algorithms.

Context. Software data collection precedes analysis which, in turn, requires data science related skills. Software defect prediction is hardly used in industrial projects as a quality assurance and cost reduction mean. Objectives. There are many studies and several tools which help in various data analysis tasks but there is still neither an open source tool nor standardized approach. Results. We developed Defect Prediction for software systems (DePress), which is an extensible software measurement, and data integration framework which can be used for prediction purposes (e.g. defect prediction, effort prediction) and software changes analysis (e.g. release notes, bug statistics, commits quality). DePress is based on the KNIME project and allows building workflows in a graphic, end-user friendly manner. Conclusions. We present main concepts, as well as the development state of the DePress framework. The results show that DePress can be used in Open Source, as well as in industrial project analysis.

Background A current focus of biofilm research is the chemical interaction between microorganisms within the biofilms. Prerequisites for this research are bioassay systems which integrate reliable tools for the planning of experiments with robot-assisted measurements and with rapid data processing. Here, data structures that are both human- and machine readable may be particularly useful. Results In this report, we present several simplification and robotisation options for an assay of bacteria-induced biofilm formation by the freshwater diatom Achnanthidium minutissimum. We also tested several proof-of-concept robotisation methods for pipetting, as well as for measuring the biofilm absorbance directly in the multi-well plates. Furthermore, we exemplify the implementation of an improved data processing workflow for this assay using the Konstanz Information Miner (KNIME), a free and open source data analysis environment. The workflow integrates experiment planning files and absorbance read-out data, towards their automated processing for analysis. Conclusions Our workflow lead to a substantial reduction of the measurement and data processing workload, while still reproducing previously obtained results in the A. minutissimum biofilm assay. The methods, scripts and files we designed are described here, offering adaptable options for other medium-throughput biofilm screenings. Electronic supplementary material The online version of this article (doi:10.1186/s12866-016-0676-9) contains supplementary material, which is available to authorized users.

Background Reproducibility is one of the tenets of the scientific method. Scientific experiments often comprise complex data flows, selection of adequate parameters, and analysis and visualization of intermediate and end results. Breaking down the complexity of such experiments into the joint collaboration of small, repeatable, well defined tasks, each with well defined inputs, parameters, and outputs, offers the immediate benefit of identifying bottlenecks, pinpoint sections which could benefit from parallelization, among others. Workflows rest upon the notion of splitting complex work into the joint effort of several manageable tasks. There are several engines that give users the ability to design and execute workflows. Each engine was created to address certain problems of a specific community, therefore each one has its advantages and shortcomings. Furthermore, not all features of all workflow engines are royalty-free —an aspect that could potentially drive away members of the scientific community. Results We have developed a set of tools that enables the scientific community to benefit from workflow interoperability. We developed a platform-free structured representation of parameters, inputs, outputs of command-line tools in so-called Common Tool Descriptor documents. We have also overcome the shortcomings and combined the features of two royalty-free workflow engines with a substantial user community: the Konstanz Information Miner, an engine which we see as a formidable workflow editor, and the Grid and User Support Environment, a web-based framework able to interact with several high-performance computing resources. We have thus created a free and highly accessible way to design workflows on a desktop computer and execute them on high-performance computing resources. Conclusions Our work will not only reduce time spent on designing scientific workflows, but also make executing workflows on remote high-performance computing resources more accessible to technically inexperienced users. We strongly believe that our efforts not only decrease the turnaround time to obtain scientific results but also have a positive impact on reproducibility, thus elevating the quality of obtained scientific results.

Automation is universal in today's society, from operating equipment such as machinery, in factory processes, to self-parking automobile systems. While these examples show the efficiency and effectiveness of automated mechanical processes, automated procedures that support the chemical risk assessment process are still in their infancy. Future human safety assessments will rely increasingly on the use of automated models, such as physiologically based kinetic (PBK) and dynamic models and the virtual cell based assay (VCBA). These biologically-based models will be coupled with chemistry-based prediction models that also automate the generation of key input parameters such as physicochemical properties. The development of automated software tools is an important step in harmonising and expediting the chemical safety assessment process. In this study, we illustrate how the KNIME Analytics Platform can be used to provide a user-friendly graphical interface for these biokinetic models, such as PBK models and VCBA, which simulates the fate of chemicals in vivo within the body and in vitro test systems respectively., Highlights • The VCBA is a mathematical model that simulates in vitro fate of chemicals and the corresponding cellular effect. • The VCBA has been implemented in an open access web-based KNIME platform for ease of use. • KNIME Analytics Platform can be used to provide a user-friendly graphical interface for biokinetic models.

MS-based proteomics and metabolomics are rapidly evolving research fields driven by the development of novel instruments, experimental approaches, and analysis methods. Monolithic analysis tools perform well on single tasks but lack the flexibility to cope with the constantly changing requirements and experimental setups. Workflow systems, which combine small processing tools into complex analysis pipelines, allow custom-tailored and flexible data-processing workflows that can be published or shared with collaborators. In this article, we present the integration of established tools for computational MS from the open-source software framework OpenMS into the workflow engine Konstanz Information Miner (KNIME) for the analysis of large datasets and production of high-quality visualizations. We provide example workflows to demonstrate combined data processing and visualization for three diverse tasks in computational MS: isobaric mass tag based quantitation in complex experimental setups, label-free quantitation and identification of metabolites, and quality control for proteomics experiments. (VLID)2215121

Open-source software is especially appealing to academics, since it permits implementing novel methods into existing code with little effort, while allowing full disclosure of the underlying science; the lack of license fees and the ease of dissemination via public repositories are additional plusses. However, open-source has also been growing popular within large companies, which have recognized the value of code sharing to increase the pool of users (and therefore testers and reviewers), as well as to gather new ideas and contributions. In the pharmaceutical field, an outstanding example is represented by the RDKit [1], a BSD-licensed C++ cheminformatics toolkit with Python, Java, and C# bindings, originally developed at Rational Discovery and currently used and being actively developed within the Novartis Institutes for BioMedical Research, which contributes in-house code enhancements back to the open-source version. To enable effective and widespread adoption and usage of open-source tools, it is helpful to make them available as plugins to well-recognized platforms. Herein we describe the integration of Open3DTOOLS (namely, Open3DQSAR [2] and Open3DALIGN [3]) into the RDKit and KNIME [4]. This task required the preliminary implementation and validation of the MMFF94(s) force field, upon which the Open3DTOOLS are based, in the C++ layer of the RDKit, followed by the extension of the RDKit API to enable molecular alignment, MIF computation and 3D-QSAR model building. Additionally, KNIME nodes were set up to allow access to Open3DTOOLS functionality within KNIME workflows. We also present some test cases which illustrate the potential of these RDKit and KNIME extensions in the context of virtual screening and CADD.

Background The localization of objects of interest is a key initial step in most image analysis workflows. For biomedical image data, classical image-segmentation methods like thresholding or edge detection are typically used. While those methods perform well for labelled objects, they are reaching a limit when samples are poorly contrasted with the background, or when only parts of larger structures should be detected. Furthermore, the development of such pipelines requires substantial engineering of analysis workflows and often results in case-specific solutions. Therefore, we propose a new straightforward and generic approach for object-localization by template matching that utilizes multiple template images to improve the detection capacity. Results We provide a new implementation of template matching that offers higher detection capacity than single template approach, by enabling the detection of multiple template images. To provide an easy-to-use method for the automatic localization of objects of interest in microscopy images, we implemented multi-template matching as a Fiji plugin, a KNIME workflow and a python package. We demonstrate its application for the localization of entire, partial and multiple biological objects in zebrafish and medaka high-content screening datasets. The Fiji plugin can be installed by activating the Multi-Template-Matching and IJ-OpenCV update sites. The KNIME workflow is available on nodepit and KNIME Hub. Source codes and documentations are available on GitHub (https://github.com/multi-template-matching). Conclusion The novel multi-template matching is a simple yet powerful object-localization algorithm, that requires no data-pre-processing or annotation. Our implementation can be used out-of-the-box by non-expert users for any type of 2D-image. It is compatible with a large variety of applications including, for instance, analysis of large-scale datasets originating from automated microscopy, detection and tracking of objects in time-lapse assays, or as a general image-analysis step in any custom processing pipelines. Using different templates corresponding to distinct object categories, the tool can also be used for classification of the detected regions.