Abstract: An approach is provided in which a method, system, and program product analyze, while training a machine learning model, a set of first data transformation operators in a first data preparation pipeline that generates a plurality of constructed features from a set of training data. The method, system, and program product create a plurality of second data preparation pipelines from the first data preparation pipeline, wherein the set of first data transformation operators are converted to a set of second data transformation operators and each assigned to one of the plurality of second data preparation pipelines. The method, system, and program product deploy the plurality of second data preparation pipelines to a runtime system.

Abstract: An embodiment generates an initial set of training data from monitoring data. The initial set of training data is generated by combining outputs from a plurality of pretrained classifiers. The embodiment trains a new classification model using the initial set of training data to identify anomalies in monitoring data. The embodiment performs a multiple-level clustering of the data samples resulting in a plurality of clusters of sub-clusters of data samples, and generates a review list of data samples by selecting a representative data sample from each of the clusters. The embodiment receives an updated data sample from the expert review that includes a revised target classification for at least one of the data samples of the expert review list. The embodiment then trains another replacement classification model using a revised set of training data that includes the updated data sample and associated revised target classification.

Abstract: A method includes generating a plurality of vectors representing words in a plurality of documents about an information technology (IT) system and clustering the plurality of vectors to produce a plurality of clusters. The method also includes identifying a cluster of the plurality of clusters that contains a plurality of clustered vectors, generating a feature based on a plurality of words represented by the plurality of clustered vectors, and training a machine learning model to identify an anomaly in the IT system based on the feature.

Abstract: An embodiment generates an initial set of training data from monitoring data. The initial set of training data is generated by combining outputs from a plurality of pretrained classifiers. The embodiment trains a new classification model using the initial set of training data to identify anomalies in monitoring data. The embodiment performs a multiple-level clustering of the data samples resulting in a plurality of clusters of sub-clusters of data samples, and generates a review list of data samples by selecting a representative data sample from each of the clusters. The embodiment receives an updated data sample from the expert review that includes a revised target classification for at least one of the data samples of the expert review list. The embodiment then trains another replacement classification model using a revised set of training data that includes the updated data sample and associated revised target classification.

Abstract: A system and method for de novo drug discovery using machine learning algorithms. In a preferred embodiment, de novo drug discovery is performed via data enrichment and interpolation/perturbation of molecule models within the latent space, wherein molecules with certain characteristics can be generated and tested in relation to one or more targeted receptors. Filtering methods may be used to determine active novel molecules by filtering out non-active molecules and contain activity predictors to better navigate the molecule-receptor domain. The system may comprise neural networks trained to reconstruct known ligand-receptors pairs and from the reconstruction model interpolate and perturb the model such that novel and unique molecules are discovered. A second preferred embodiment trains a variational autoencoder coupled with a bioactivity model to predict molecules exhibiting a range of desired properties.

Abstract: A method includes generating a plurality of vectors representing words in a plurality of documents about an information technology (IT) system and clustering the plurality of vectors to produce a plurality of clusters. The method also includes identifying a cluster of the plurality of clusters that contains a plurality of clustered vectors, generating a feature based on a plurality of words represented by the plurality of clustered vectors, and training a machine learning model to identify an anomaly in the IT system based on the feature.

Abstract: An approach is provided in which a method, system, and program product analyze, while training a machine learning model, a set of first data transformation operators in a first data preparation pipeline that generates a plurality of constructed features from a set of training data. The method, system, and program product create a plurality of second data preparation pipelines from the first data preparation pipeline, wherein the set of first data transformation operators are converted to a set of second data transformation operators and each assigned to one of the plurality of second data preparation pipelines. The method, system, and program product deploy the plurality of second data preparation pipelines to a runtime system.

Abstract: A virtual change database system that supports iterative and parallel database application development is disclosed. The system stores a common set of base physical data and a plurality of sets of virtual changes. Each set of virtual changes is associated with a database object. A database application may access a database object in the database by using the virtual version of the object to extract the object's data content from the common base physical data. The database system present a first query response to (i) a first application based on the set of base physical data and (ii) a first set of virtual changes for a particular database object, while also presenting a second query response to a second application based on the set of base physical data and a second, different set of virtual changes for the particular database object.

Abstract: A system and method for biomarker-outcome prediction and medical literature exploration which utilizes a data platform to analyze, optimize, and explore the knowledge contained in or derived from clinical trials. The system utilizes a knowledge graph and data analysis engine capabilities of the data platform. The knowledge graph may be used to link biomarkers with molecules, proteins, and genetic data to provide insight into the relationship between biomarkers, outcomes, and adverse events. The system uses natural language processing techniques on a large corpus of medical literature to perform advanced text mining to identify biomarkers associated with adverse events and to curate a comprehensive profile of biomarker-outcome associations. These associations may then be ranked to identify the most-common biomarker-outcome association pairs.

Abstract: A system and method that predicts whether a given protein-ligand pair is active or inactive and outputs a pose score classifying the propriety of the pose. A 3D bioactivity platform comprising a 3D bioactivity module and data platform scrapes empirical lab-based data that a docking simulator uses to generate a dataset from which a 3D-CNN model is trained. The model then may receive new protein-ligand pairs and determine a classification for the bioactivity and pose propriety of that protein-ligand pair. Furthermore, gradients relating to the binding affinity in the 3D model of the molecule may be used to generate profiles from which new protein targets may be determined.

Abstract: A system and method that predicts whether a given protein-ligand pair is active or inactive, the ground-truth protein-ligand complex crystalline-structure similarity, and an associated bioactivity value. The system and method further produce 3-D visualizations of previously unknown protein-ligand pairs that show directly the importance assigned to protein-ligand interactions, the positive/negative-ness of the saliencies, and magnitude. Furthermore, the system and method make enhancements in the art by accurately predicting protein-ligand pair bioactivity from decoupled models, removing the need for docking simulations, as well as restricting attention of the machine learning between protein and ligand atoms only.

Abstract: A system and method for computationally tractable prediction of protein-ligand interactions and their bioactivity. According to an embodiment, the system and method comprise two machine learning processing streams and concatenating their outputs. One of the machine learning streams is trained using information about ligands and their bioactivity interactions with proteins. The other machine learning stream is trained using information about proteins and their bioactivity interactions with ligands. After the machine learning algorithms for each stream have been trained, they can be used to predict the bioactivity of a given protein-ligand pair by inputting a specified ligand into the ligand processing stream and a specified protein into the protein processing stream. The machine learning algorithms of each stream predict possible protein-ligand bioactivity interactions based on the training data.

Abstract: A system and method for an automated pharmaceutical research data platform comprising a data curation platform which searches for and ingests a plurality of unstructured, heterogenous medical data sources, extracts relevant information from the ingested data sources, and creates a massive, custom-built and intricately related knowledge graph using the extracted data, and a data analysis engine which receives data queries from a user interface, conducts analyses in response to queries, and returns results based on the analyses. The system hosts a suite of modules and tools, integrated with the custom knowledge graph and accessible via the user interface, which may provide a plurality of functions such as statistical and graphical analysis, similarity based searching, and edge prediction among others.

Abstract: A method for training a computer-implemented machine learning model for detecting irregularities in medical images, the method including: identifying at least one predetermined type of body region (14) depicted in a medical image (10), said body region (14) having a depicted irregularity (12); defining a plurality of image segments (20) each including at least part of the depicted body region (14), wherein a resolution of the image segments (20) is maintained or not reduced by more than 20% compared to the medical image (10); and using said image segments (20) to train a machine learning model to detect similar irregularities (12) in other medical images (10). Further, the invention relates to a use and to systems for training a computer-implemented machine learning model for detecting irregularities in medical images.

Abstract: The present invention provides a power controller, a power supply system and device and control method thereof. The power command value is adjusted according to the state of the power supply system and the characteristic of the secondary battery. When the converting power of the power converter is larger than or equal to the power command value, or when the charging current is smaller than the minimum charging current, the power controller performs the power adjusting mode for providing the first preset output current value. The power converter adjusts the converting power according to the first preset output current value. When the charging current is larger than or equal to the maximum charging current, the power controller performs the charging control mode for providing the second preset output current value. The power converter adjusts the output current according to the second preset output current value.

Abstract: A capacitive sensing structure includes a first sensing electrode located in a first layer for sensing a first capacitance and producing a first sense signal indicative of the sensed first capacitance. A transmit electrode is located in the first layer and positioned surrounding 90%+ of a perimeter of the first sensing electrode. A second sensing electrode is located in the first layer and positioned surrounding 90%+ of a perimeter of the transmit electrode, the second sensing electrode to sense a second capacitance and produce a second sense signal indicative of the sensed second capacitance. Controller circuitry receives the first and second sense signals, compares a change in the sensed first capacitance to a change in the sensed second capacitance, and produces an output signal indicative of a user touch based upon the comparison between the change in the sensed first capacitance and the change in the sensed second capacitance.

Abstract: A method for training a computer-implemented machine learning model for detecting irregularities in medical images, the method including: identifying at least one predetermined type of body region (14) depicted in a medical image (10), said body region (14) having a depicted irregularity (12); defining a plurality of image segments (20) each including at least part of the depicted body region (14), wherein a resolution of the image segments (20) is maintained or not reduced by more than 20% compared to the medical image (10); and using said image segments (20) to train a machine learning model to detect similar irregularities (12) in other medical images (10). Further, the invention relates to a use and to systems for training a computer-implemented machine learning model for detecting irregularities in medical images.

Abstract: A capacitive sensing structure includes a first sensing electrode located in a first layer for sensing a first capacitance and producing a first sense signal indicative of the sensed first capacitance. A transmit electrode is located in the first layer and positioned surrounding 90%+ of a perimeter of the first sensing electrode. A second sensing electrode is located in the first layer and positioned surrounding 90%+ of a perimeter of the transmit electrode, the second sensing electrode to sense a second capacitance and produce a second sense signal indicative of the sensed second capacitance. Controller circuitry receives the first and second sense signals, compares a change in the sensed first capacitance to a change in the sensed second capacitance, and produces an output signal indicative of a user touch based upon the comparison between the change in the sensed first capacitance and the change in the sensed second capacitance.

Abstract: Disclosed is a touch sensor and method for detecting a touch in a capacitive touchscreen application, wherein the touch sensor is capable of distinguishing between a finger hovering above the touch sensor and a touch from a stylus having a small contact surface area without having to adjust the sensitivity of the touch sensor. The touch sensor includes a first sensing electrode, a transmit electrode, and a second sensing electrode, wherein the second sensing electrode is positioned substantially around the perimeter of the inner circuitry (i.e., transmit electrode and first sensing electrode). A touch is detected by sensing changes in a first capacitance between the transmit electrode and first sensing electrode and a second capacitance between the transmit electrode and second sensing electrode. The changes in the first and second capacitances are compared to determine whether the changes in the capacitances are due to a finger hover or a touch.

Abstract: A virtual change database system that supports iterative and parallel database application development is disclosed. The system stores a common set of base physical data and a plurality of sets of virtual changes. Each set of virtual changes is associated with a database object. A database application may access a database object in the database by using the virtual version of the object to extract the object's data content from the common base physical data. The database system present a first query response to (i) a first application based on the set of base physical data and (ii) a first set of virtual changes for a particular database object, while also presenting a second query response to a second application based on the set of base physical data and a second, different set of virtual changes for the particular database object.