Machine learning (ML) methods offer opportunities for gaining insights into the intricate workings of complex biological systems, and their applications are increasingly prominent in the analysis of omics data to facilitate tasks, such as the identification of novel biomarkers and predictive modeling of phenotypes. For scientists and domain experts, leveraging user-friendly ML pipelines can be incredibly valuable, enabling them to run sophisticated, robust, and interpretable models without requiring in-depth expertise in coding or algorithmic optimization. By streamlining the process of model development and training, researchers can devote their time and energies to the critical tasks of biological interpretation and validation, thereby maximizing the scientific impact of ML-driven insights. Here, we present an entirely automated open-source explainable AI tool, AutoXAI4Omics, that performs classification and regression tasks from omics and tabular numerical data. AutoXAI4Omics accelerates scientific discovery by automating processes and decisions made by AI experts, e.g. selection of the best feature set, hyper-tuning of different ML algorithms and selection of the best ML model for a specific task and dataset. Prior to ML analysis AutoXAI4Omics incorporates feature filtering options that are tailored to specific omic data types. Moreover, the insights into the predictions that are provided by the tool through explainability analysis highlight associations between omic feature values and the targets under investigation, e.g. predicted phenotypes, facilitating the identification of novel actionable insights. AutoXAI4Omics is available at: https://github.com/IBM/AutoXAI4Omics.

The increasing interest of researchers in service oriented architecture (SOA) for wireless sensor networks (WSNs) is opening new unexplored venues in the field of WSNs. In service oriented systems, services are configured and composed of various other services and thus perform complex tasks. In such composite services, the geospatial locations of services and their coverage is of vital importance as they signify the geospatial relevance of the service to the area of interest to the user. In this paper, we present a service-oriented system for WSNs that is capable of performing service configuration under geospatial and relevancy constraints. We present and evaluate “Cost Based Model (CBM)” and “Gain Based Model (GBM)” approaches to capture the relevancy of services hosted on WSN nodes in composite service configuration. The system is resilient to failures and can operate in manual or autonomous recovery modes. The system supports three service configuration methods namely, distributed, centralized and hybrid. Furthermore, we present a novel emulation mechanism for testing the performance of our proposed relevancy models and show that our system efficiently configures services.

There is a significant challenge in designing, optimizing, deploying and managing complex sensor networks over heterogeneous communications infrastructures. The ITA Sensor Fabric addresses these challenges in the areas of sensor identification and discovery, sensor access and control, and sensor data consumability, by extending the message bus model commonly found in commercial IT infrastructures out to the edge of the network. In this paper we take the message bus model further into a semantically rich, model-based design and analysis approach that considers the sensor network and its contained services as a Service Oriented Architecture. We present an application of a hierarchic schema for nested service definitions together with an initial ontology that describes the assets and services deployed in a sensor network infrastructure.