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MassBank of America (MoNA), is an auto curating repository for storing, comparing and querying mass spectra of chemical compounds. It is metadata centric and it was designed to allow easy integration into other tools by utilize its REST based application programming interface. At the current time it contains over 200k predicted and 40k unique experimental mass spectra and their associated metadata. The predicted spectra were obtained by utilizing the lipid blast library and the experimental spectra were acquired from 30 different facilities all over the world, including several major research facilities in the United States and Japan. MoNA is utilizing the InChI Key as unique identifier for chemicals and is designed for easy scalability and expendability. This is realized by utilizing common applications like nginx, grails, AngularJS, postgresSQL and tomcat. MoNA is currently integrated in applications like MSDial, BinBase, MZMine and the statistics package R. This was accomplished by utilizing its REST based API, which is also utilized by its main AngularJS based web interface. We consider MoNA to be highly useful for crosslinking mass spectra in publications, identification of unknowns and integration in data acquisition software. This package provides you with access to the REST api of the main MoNA installation.

Performs a grid search of parameter pairs for the a classifier (Y-axis, default is LinearRegression with the "Ridge" parameter) and the PLSFilter (X-axis, "# of Components") and chooses the best pair found for the actual predicting. The initial grid is worked on with 2-fold CV to determine the values of the parameter pairs for the selected type of evaluation (e.g., accuracy). The best point in the grid is then taken and a 10-fold CV is performed with the adjacent parameter pairs. If a better pair is found, then this will act as new center and another 10-fold CV will be performed (kind of hill-climbing). This process is repeated until no better pair is found or the best pair is on the border of the grid. In case the best pair is on the border, one can let GridSearch automatically extend the grid and continue the search. Check out the properties 'gridIsExtendable' (option '-extend-grid') and 'maxGridExtensions' (option '-max-grid-extensions <num>'). GridSearch can handle doubles, integers (values are just cast to int) and booleans (0 is false, otherwise true). float, char and long are supported as well. The best filter/classifier setup can be accessed after the buildClassifier call via the getBestFilter/getBestClassifier methods. Note on the implementation: after the data has been passed through the filter, a default NumericCleaner filter is applied to the data in order to avoid numbers that are getting too small and might produce NaNs in other schemes.

This is an implementation of the Neural Gas algorithm on distance data (Relational Neural Gas) for unsupervised clustering. We recommend that you use the functions provided by the RelationalNeuralGas class for your purposes. All other classes and functions are utilities which are used by this central class. In particular, you can use RelationalNeuralGas.train() to obtain a RNGModel (i.e. a clustering of your data), and subsequently you can use RelationalNeuralGas.getAssignments() to obtain the resulting cluster assignments, and RelationalNeuralGas.classify() to cluster new points which are not part of the training data set. The underlying scientific work is summarized nicely in the dissertation "Topographic Mapping of Dissimilarity Datasets" by Alexander Hasenfuss (2009). The basic properties of an Relational Neural Gas algorithm are the following: 1.) It is relational: The data is represented only in terms of a pairwise distance matrix. 2.) It is a clustering method: The algorithm provides a clustering model, that is: After calculation, each data point should be assigned to a cluster (for this package here we only consider hard clustering, that is: each data point is assigned to exactly one cluster). 3.) It is a vector quantization method: Each cluster corresponds to a prototype, which is in the center of the cluster and data points are assigned to the cluster if and only if they are closest to this particular prototype. 4.) It is rank-based: The updates of the prototypes depend only on the distance ranking, not on the absolute value of the distances.

A platform for discovery and communication between various parts of a distributed service or application. At its minimum, the platform is a library which can be used to serialize and deserialize application constructs into payloads which it can then send to and receive from remote endpoints. For now it supports TCP and HTTP communication protocols with WebSockets coming soon. It supports raw byte streams via TCP, as well as application/json via JAX-RS as serialization protocols. It offers plain or SSL/TLS communication for security of your data in transit. For now it is only available in Java, with bindings for other languages coming soon. The communication pattern is request-reply, with fail fast semantics, and with service consumers (clients) knowing beforehand the coordinates of the service providers (servers). The TCP communication protocol offers blocking/unblocking/asynchronous flavors whereas the rest are only blocking for now. Coming soon, the platform will provide a Discovery Service to facilitate the registration of the service providers (servers) and service consumers (clients). In this case, the callers don't need to resolve or even balance their calls towards various endpoints, since the Platform handles this tasks. In this case, the pub/sub communication pattern will also be available (coming soon) with at-most-once delivery guarantees. We intend to provide a Security Service for managing the keys/and certificates of various services as well as Metrics and Logging services (coming soon).