The development of the advanced web shop based on purchase history

The goal of thesis is to develop a typical web shop application with some additional functionality. This functionality enables web shop customers to browse products in a more efficient way and thus makes shop more profitable. For this purpose, we developed a specific mechanism that handles product presentation in customer adapted way. First we describe technologies used for development. Programing language C# is presented shortly as well as some other frameworks (ASP.net, Entity framework,), libraries (LINQ) and other web technologies (HTML, CSS, AJAX). For storing and manipulating data a database with tables in MS SQL database is created. Furthermore we take a look at requirements, idea and logic of solution. We present solution design and present how specific functionality behaves in case of different user types. We present a solution analysis where a comparison with other similar solutions and user tests are shown. Finally we discuss problems during the development and possibilities about the future improvements

Papyrus - A large scale curated dataset aimed at bioactivity predictions

With the recent rapid growth of publicly available ligand-protein bioactivity data, there is a trove of viable data that can be used to train machine learning algorithms. However, not all data is equal in terms of size and quality, and a significant portion of researcher’s time is needed to adapt the data to their needs. On top of that, finding the right data for a research question can often be a challenge on its own. As an answer to that, we have constructed the Papyrus dataset (DOI: 10.4121/16896406), comprised of around 60 million datapoints. This dataset contains multiple large publicly available datasets such as ChEMBL and ExCAPE-DB combined with several smaller datasets containing high quality data. The aggregated data has been standardised and normalised in a manner that is suitable for machine learning. We show how data can be filtered in a variety of ways, and also perform some baseline quantitative structure-activity relationship analyses and proteochemometrics modeling. Our ambition is this pruned data collection constitutes a benchmark set that can be used for constructing predictive models, while also providing a solid baseline for related research

Identifying Novel Inhibitors for Hepatic Organic Ani-on Transporting Polypeptides by Machine-learning based Virtual Screening

Integration of statistical learning methods with structure-based modeling approaches is a contemporary strategy to identify novel lead compounds in drug discovery. Hepatic organic anion transporting polypeptides (OATP1B1, OATP1B3, and OATP2B1) are classical off-targets and it is well recognized that their ability to interfere with a wide range of chemically unrelated drugs, environmental chemicals, or food additives can lead to unwanted adverse effects like liver toxicity, drug-drug or drug-food interactions. Therefore, the identification of novel (tool) compounds for hepatic OATPs by virtual screening approaches and subsequent experimental validation is a major asset for elucidating structure-function relationships of (related) transporters: they enhance our understanding about molecular determinants and structural aspects of hepatic OATPs driving ligand binding and selectivity. In the present study, we performed a consensus virtual screening approach by using different types of machine learning models (proteochemometric models, conformal prediction models, and XGBoost models for hepatic OATPs), followed by molecular docking of preselected hits using previously established structural models for hepatic OATPs. Screening the diverse REAL drug-like set (Enamine) shows a comparable hit rate for OATP1B1 (36% actives) and OATP1B3 (32% actives), while the hit rate for OATP2B1 was even higher (66% actives). Percentage inhibition values for 44 selected compounds were subsequently determined using dedicated in vitro assays, and guided the priori-tization of several highly potent novel hepatic OATP inhibitors: six (strong) OATP2B1 inhibitors (IC50 values ranging from 0.04 to 6 μM), three OATP1B1 inhibitors (2.69 to 10 μM), and five OATP1B3 inhibitors (1.53 to 10 μM) inhibitors, were identified. Strikingly, two novel OATP2B1 inhibitors were uncovered (C7, H5) which show high affinity (IC50 values: 40 nM and 390 nM) comparable to the recently described estrone-based inhibitor (IC50 = 41 nM). A molecularly detailed explanation for the observed differences in ligand binding to the three transporters is given by means of structural comparison of the detected binding sites and docking poses

Beyond the Hype: Deep Neural Networks Outperform Established Methods Using A ChEMBL Bioactivity Benchmark Set

This dataset contains the (standardized) data used in the experiments, alongside the scripts used to perform Deep Neural Nets (DNN_Scripts), and the other machine learning methods in both Pipeline Pilot (PP_protocols) and Python/Scikit-Learn (PY_scripts

Successive Statistical and Structure-Based Modeling to Identify Chemically Novel Kinase Inhibitors

Kinases are frequently studied in the context of anticancer drugs. Their involvement in cell responses, such as proliferation, differentiation, and apoptosis, makes them interesting subjects in multitarget drug design. In this study, a workflow is presented that models the bioactivity spectra for two panels of kinases: (1) inhibition of RET, BRAF, SRC, and S6K, while avoiding inhibition of MKNK1, TTK, ERK8, PDK1, and PAK3, and (2) inhibition of AURKA, PAK1, FGFR1, and LKB1, while avoiding inhibition of PAK3, TAK1, and PIK3CA. Both statistical and structure-based models were included, which were thoroughly benchmarked and optimized. A virtual screening was performed to test the workflow for one of the main targets, RET kinase. This resulted in 5 novel and chemically dissimilar RET inhibitors with remaining RET activity of <60% (at a concentration of 10 mu M) and similarities with known RET inhibitors from 0.18 to 0.29 (Tanimoto, ECFP6). The four more potent inhibitors were assessed in a concentration range and proved to be modestly active with a pIC(50) value of 5.1 for the most active compound. The experimental validation of inhibitors for RET strongly indicates that the multitarget workflow is able to detect novel inhibitors for kinases, and hence, this workflow can potentially be applied in polypharmacology modeling. We conclude that this approach can identify new chemical matter for existing targets. Moreover, this workflow can easily be applied to other targets as well

Proteochemometric modeling identifies chemically diverse norepinephrine transporter inhibitors - Protocol Files

This repository contains the Pipeline Pilot protocols and aggregated ChEMBL dataset, as described in the manuscript "Proteochemometric modeling identifies chemically diverse norepinephrine transporter inhibitors"</p

Proteochemometric modeling identifies chemically diverse norepinephrine transporter inhibitors - Protocol Files

This repository contains the Pipeline Pilot protocols and aggregated ChEMBL dataset, as described in the manuscript "Proteochemometric modeling identifies chemically diverse norepinephrine transporter inhibitors"</p

Perceived behavioral problems of school aged children in rural Nepal:a qualitative study

BACKGROUND: Studies on child behavioral problems from low and middle income countries are scarce, even more so in Nepal. This paper explores parents’, family members’ and teachers’ perceptions of child behavioral problems, strategies used and recommendations to deal with this problem. METHOD: In this study, 72 free list interviews and 30 Key Informant Interviews (KII) were conducted with community members of Chitwan district in Nepal. RESULT: The result suggest that addictive behavior, not paying attention to studies, getting angry over small issues, fighting back, disobedience, and stealing were the most commonly identified behavioral related problems of children, with these problems seen as interrelated and interdependent. Results indicate that community members view the family, community and school environments as being the causes of child behavioral problems, with serious impacts upon children’s personal growth, family harmony and social cohesion. The strategies reported by parents and teachers to manage child behavioral problems were talking, listening, consoling, advising and physical punishment (used as a last resort). CONCLUSIONS: As perceived by children and other community dwellers, children in rural Nepalese communities have several behavioral related problems. The findings suggest that multi-level community-based interventions targeting peers, parents, teachers and community leaders could be a feasible approach to address the identified problems

Proteochemometric modeling identifies chemically diverse norepinephrine transporter inhibitors - Protocol Files

This repository contains the Pipeline Pilot protocols and aggregated ChEMBL dataset, as described in the manuscript "Proteochemometric modeling identifies chemically diverse norepinephrine transporter inhibitors"</p

Auto-segmentation of the brachial plexus assessed with TaCTICS – A software platform for rapid multiple-metric quantitative evaluation of contours

Auto-segmentation of the brachial plexus assessed with TaCTICS – A software platform for rapid multiple-metric quantitative evaluation of contour