One aspect of personalized medicine is aiming at identifying specific targets for therapy considering the gene expression profile of each patient individually. The real-world implementation of this approach is better achieved by user-friendly bioinformatics systems for healthcare professionals. In this report, we present an online platform that endows users with an interface designed using MEAN stack supported by a Galaxy pipeline. This pipeline targets connection hubs in the subnetworks formed by the interactions between the proteins of genes that are up-regulated in tumors. This strategy has been proved to be suitable for the inhibition of tumor growth and metastasis in vitro. Therefore, Perl and Python scripts were enclosed in Galaxy for translating RNA-seq data into protein targets suitable for the chemotherapy of solid tumors. Consequently, we validated the process of target diagnosis by (i) reference to subnetwork entropy, (ii) the critical value of density probability of differential gene expression, and (iii) the inhibition of the most relevant targets according to TCGA and GDC data. Finally, the most relevant targets identified by the pipeline are stored in MongoDB and can be accessed through the aforementioned internet portal designed to be compatible with mobile or small devices through Angular libraries.

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Introduction: Contributions to medicine may come from different areas; and most areas are full of researchers wanting to support. Physists may help with theory, such as for nuclear medicine. Engineers with machineries, such as dialysis machine. Mathematicians with models, such as pharmacokinetics. And computer scientists with codes such as bioinformatics. Method: We have used TensorFlow.js for modeling using neural networks biomedical datasets from Kaggle. We have modeled three datasets: diabetes detection, surgery complications, and heart failure. We have used Angular coded in TypeScript for the implementation of the models. Using TensorFlow.js, we have built Multilayer Perceptrons (MPLs) for modelling our datasets. We have employed the training and the validation curves to make sure the model learnt, and we have used accuracy as a measure of goodness of each model. Results and discussion: We have built a couple of examples using TensorFlow.js as machine learning platform. Even though python and R are dominant at the moment, JavaScript and derivatives are growing fast, offering basically the same performance, and some extra features associated with JavaScript. Kaggle, the public platform from where we downloaded our datasets, offers a huge amount of datasets for biomedical cases, thus, the reader can easily test what we have discussed, using the same codes, with minor chances, on any case they may be interested in. We were able to find 92% of accuracy for diabetes detection, 100% for surgery complications, and 70% for heart failure. The possibilities are unlimited, and we believe that it is a nice option for researchers aiming at web applications, especially, focused on medicine.