BioAssay Ontology (BAO): a semantic description of bioassays and high-throughput screening results

<p>Abstract</p> <p>Background</p> <p>High-throughput screening (HTS) is one of the main strategies to identify novel entry points for the development of small molecule chemical probes and drugs and is now commonly accessible to public sector research. Large amounts of data generated in HTS campaigns are submitted to public repositories such as PubChem, which is growing at an exponential rate. The diversity and quantity of available HTS assays and screening results pose enormous challenges to organizing, standardizing, integrating, and analyzing the datasets and thus to maximize the scientific and ultimately the public health impact of the huge investments made to implement public sector HTS capabilities. Novel approaches to organize, standardize and access HTS data are required to address these challenges.</p> <p>Results</p> <p>We developed the first ontology to describe HTS experiments and screening results using expressive description logic. The BioAssay Ontology (BAO) serves as a foundation for the standardization of HTS assays and data and as a semantic knowledge model. In this paper we show important examples of formalizing HTS domain knowledge and we point out the advantages of this approach. The ontology is available online at the NCBO bioportal <url>http://bioportal.bioontology.org/ontologies/44531</url>.</p> <p>Conclusions</p> <p>After a large manual curation effort, we loaded BAO-mapped data triples into a RDF database store and used a reasoner in several case studies to demonstrate the benefits of formalized domain knowledge representation in BAO. The examples illustrate semantic querying capabilities where BAO enables the retrieval of inferred search results that are relevant to a given query, but are not explicitly defined. BAO thus opens new functionality for annotating, querying, and analyzing HTS datasets and the potential for discovering new knowledge by means of inference.</p

RegenBase: a knowledge base of spinal cord injury biology for translational research.

Spinal cord injury (SCI) research is a data-rich field that aims to identify the biological mechanisms resulting in loss of function and mobility after SCI, as well as develop therapies that promote recovery after injury. SCI experimental methods, data and domain knowledge are locked in the largely unstructured text of scientific publications, making large scale integration with existing bioinformatics resources and subsequent analysis infeasible. The lack of standard reporting for experiment variables and results also makes experiment replicability a significant challenge. To address these challenges, we have developed RegenBase, a knowledge base of SCI biology. RegenBase integrates curated literature-sourced facts and experimental details, raw assay data profiling the effect of compounds on enzyme activity and cell growth, and structured SCI domain knowledge in the form of the first ontology for SCI, using Semantic Web representation languages and frameworks. RegenBase uses consistent identifier schemes and data representations that enable automated linking among RegenBase statements and also to other biological databases and electronic resources. By querying RegenBase, we have identified novel biological hypotheses linking the effects of perturbagens to observed behavioral outcomes after SCI. RegenBase is publicly available for browsing, querying and download.Database URL:http://regenbase.org

Robotic Table Tennis: A Case Study into a High Speed Learning System

We present a deep-dive into a real-world robotic learning system that, in previous work, was shown to be capable of hundreds of table tennis rallies with a human and has the ability to precisely return the ball to desired targets. This system puts together a highly optimized perception subsystem, a high-speed low-latency robot controller, a simulation paradigm that can prevent damage in the real world and also train policies for zero-shot transfer, and automated real world environment resets that enable autonomous training and evaluation on physical robots. We complement a complete system description, including numerous design decisions that are typically not widely disseminated, with a collection of studies that clarify the importance of mitigating various sources of latency, accounting for training and deployment distribution shifts, robustness of the perception system, sensitivity to policy hyper-parameters, and choice of action space. A video demonstrating the components of the system and details of experimental results can be found at https://youtu.be/uFcnWjB42I0.Comment: Published and presented at Robotics: Science and Systems (RSS2023

Learnable Knowledge for Autonomous Agents

While computation power has increased and the statistical machine learning methods have made substantial advancement, many problems that would benefit from real-time interpretation have not exploited their combined strengths. For instance, the problem of gathering data from the environment and transforming it into knowledge as well as updating the knowledge as new data become available. Currently, with substantial expressivity and moderate computational cost, high-level languages or first-order predicate logic or model-based machine learning are used for static representation of knowledge, that is used for reasoning and inferring. In this dissertation, we address how an entity dynamically gather knowledge from environmental data and use that for inferring evolving events and dynamically update the current knowledge. We develop theoretical and empirical solutions using Description Logic representation and reasoning, and General Value Functions in Reinforcement Learning. The proposed solutions dynamically extract low-level knowledge from available data and update the high-level knowledge, which is used to predict the evolving future events. We show its applications in three real world domains: 1) RoboCup 3D Soccer Simulation environment, 2) High-throughput screening, and 3) Axon regeneration

PrOntoLearn: Unsupervised Lexico-Semantic Ontology Generation using Probabilistic Methods

An ontology is a formal, explicit specification of a shared conceptualization. Formalizing an ontology for a domain is a tedious and cumbersome process. It is constrained by the knowledge acquisition bottleneck (KAB). There exists a large number of text corpora that can be used for classification in order to create ontologies with the intention to provide better support for the intended parties. In our research we provide a novel unsupervised bottom-up ontology generation method. This method is based on lexico-semantic structures and Bayesian reasoning to expedite the ontology generation process. This process also provides evidence to domain experts to build ontologies based on top-down approaches

RLLib: C++ Library to Predict, Control, and Represent Learnable Knowledge Using On/Off Policy Reinforcement Learning

RLLib is a lightweight C++ template library that implements incremental, standard, and gradient temporal-difference learning algorithms in reinforcement learning. It is an optimized library for robotic applications and embedded devices that operates under fast duty cycles (e.g., \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\le$$\end{document}30 ms). RLLib has been tested and evaluated on RoboCup 3D soccer simulation agents, NAO V4 humanoid robots, and Tiva C series launchpad microcontrollers to predict, control, learn behavior, and represent learnable knowledge

A New Real-Time Algorithm to Extend DL Assertional Formalism to Represent and Deduce Entities in Robotic Soccer

Creating, maintaining, and deducing accurate world knowledge in a dynamic, complex, adversarial, and stochastic environment such as the RoboCup environment is a demanding task. Knowledge should be represented in real-time (i.e., within ms) and deduction from knowledge should be inferred within the same time constraints. We propose an extended assertional formalism for an expressive \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathcal {SROIQ}(\mathcal {D})$$\end{document} Description Logic to represent asserted entities in a lattice structure. This structure can represent temporal-like information. Since the computational complexity of the classes of description logic increases with its expressivity, the problem demands either a restriction in the expressivity or an empirical upper bound on the maximum number of axioms in the knowledge base. We assume that the terminological/relational knowledge changes significantly slower than the assertional knowledge. Henceforth, using a fixed terminological and relational formalisms and the proposed lattice structure, we empirically bound the size of the knowledge bases to find the best trade-off in order to achieve deduction capabilities of an existing description logic reasoner in real-time. The queries deduce instances using the equivalent class expressions defined in the terminology. We have conducted all our experiments in the RoboCup 3D Soccer Simulation League environment and provide justifications of the usefulness of the proposed assertional extension. We show the feasibility of our new approach under real-time constraints and conclude that a modified FaCT++ reasoner empirically outperforms other reasoners within the given class of complexity

Single- and Multi-channel Whistle Recognition with NAO Robots

We propose two real-time sound recognition approaches that are able to distinguish a predefined whistle sound on a NAO robot in various noisy environments. The approaches use one, two, and four microphone channels of a NAO robot. The first approach is based on a frequency/band-pass filter whereas the second approach is based on logistic regression. We conducted experiments in six different settings varying the noise level of both the surrounding environment and the robot itself. The results show that the robot will be able to identify the whistle reliability even in very noisy environments