Motivation: Ontologies are widely used in biology for data annotation, integration, and analysis. In addition to formally structured axioms, ontologies contain meta-data in the form of annotation axioms which provide valuable pieces of information that characterize ontology classes. Annotations commonly used in ontologies include class labels, descriptions, or synonyms. Despite being a rich source of semantic information, the ontology meta-data are generally unexploited by ontology-based analysis methods such as semantic similarity measures. Results: We propose a novel method, OPA2Vec, to generate vector representations of biological entities in ontologies by combining formal ontology axioms and annotation axioms from the ontology meta-data. We apply a Word2Vec model that has been pre-trained on PubMed abstracts to produce feature vectors from our collected data. We validate our method in two different ways: first, we use the obtained vector representations of proteins as a similarity measure to predict protein-protein interaction (PPI) on two different datasets. Second, we evaluate our method on predicting gene-disease associations based on phenotype similarity by generating vector representations of genes and diseases using a phenotype ontology, and applying the obtained vectors to predict gene-disease associations. These two experiments are just an illustration of the possible applications of our method. OPA2Vec can be used to produce vector representations of any biomedical entity given any type of biomedical ontology. Availability: https://github.com/bio-ontology-research-group/opa2vec Contact: robert.hoehndorf@kaust.edu.sa and xin.gao@kaust.edu.sa.

Autonomously training interpretable control strategies, called policies, using pre-existing plant trajectory data is of great interest in industrial applications. Fuzzy controllers have been used in industry for decades as interpretable and efficient system controllers. In this study, we introduce a fuzzy genetic programming (GP) approach called fuzzy GP reinforcement learning (FGPRL) that can select the relevant state features, determine the size of the required fuzzy rule set, and automatically adjust all the controller parameters simultaneously. Each GP individual's fitness is computed using model-based batch reinforcement learning (RL), which first trains a model using available system samples and subsequently performs Monte Carlo rollouts to predict each policy candidate's performance. We compare FGPRL to an extended version of a related method called fuzzy particle swarm reinforcement learning (FPSRL), which uses swarm intelligence to tune the fuzzy policy parameters. Experiments using an industrial benchmark show that FGPRL is able to autonomously learn interpretable fuzzy policies with high control performance.

The demands of UQ in computer prediction, a problem we believe to be NP-Hard, cannot be met on our current HPC technology path. We see that cognitive computing, defined through the technology convergence of AI, Big Data and HPC is an essential next step. With vendor technology decisions being made now and in the next few years in AI and HPC, it is urgent that broad classes of HW and SW are explored to best leverage commercial technology roadmaps. To that end, we are using precision medicine data as a force multiplier and accelerator. This rich, complex, unstructured, heterogeneous, curated, massive data is likely the richest class of data to work on today and brings with it unique partnerships that buys down risk in exploring the many splintered paths forward each with their own tough challenges and also shares costs.

This paper introduces a new property of estimators of the strength of statistical association, which helps characterize how well an estimator will perform in scenarios where dependencies between continuous and discrete random variables need to be rank ordered. The new property, termed the estimator response curve, is easily computable and provides a marginal distribution agnostic way to assess an estimator's performance. It overcomes notable drawbacks of current metrics of assessment, including statistical power, bias, and consistency. We utilize the estimator response curve to test various measures of the strength of association that satisfy the data processing inequality (DPI), and show that the CIM estimator's performance compares favorably to kNN, vME, AP, and H_{MI} estimators of mutual information. The estimators which were identified to be suboptimal, according to the estimator response curve, perform worse than the more optimal estimators when tested with real-world data from four different areas of science, all with varying dimensionalities and sizes.

Clustering is a complex process in finding the relevant hidden patterns in unlabeled datasets, broadly known as unsupervised learning. Support vector clustering algorithm is a well-known clustering algorithm based on support vector machines and Gaussian kernels. In this paper, we have investigated the support vector clustering algorithm in quantum paradigm. We have developed a quantum algorithm which is based on quantum support vector machine and the quantum kernel (Gaussian kernel and polynomial kernel) formulation. The investigation exhibits approximately exponential speed up in the quantum version with respect to the classical counterpart.

Roundup Here's your roundup of machine-learning news from this week, beyond what we've already covered. Axon AI Ethics board A group of civil rights groups and technology researchers has written a letter to Axon, a company that uses AI to analyze video footage aimed at law enforcement. Axon recently announced it had set up an AI ethics board to guide its products and services. In response, the letter urges the company to not develop real-time facial recognition for police body cameras to prevent misidentifying civilians as criminals, to ethically reviewing all its other products, and to reach out to "survivors of law enforcement harm and violence" for advice. You can read the letter here.

A House of Lords Committee report today warns that even though the UK has what it takes to become a world leader in the development of artificial intelligence, such new technologies should not come at the price of data rights or privacy of individuals, families or communities. In the wake of the Cambridge Analytica scandal, Internet users from across the world have rightly questioned the sanctity of their personal data, how much data is being used by companies, who they are shared with, and how much data is being used to create individual profiles that can be targeted via social media. Similar is the case with artificial intelligence technologies that are being increasingly adopted by companies from across the world. Some of the cutting-edge technologies being used commercially include recognising voices, understanding people's behavioural patterns, their likes and dislikes, their working hours, their driving routes, the devices they use etc. These technologies are then incorporated into'smart devices' that behave as digital assistants - playing their owners' favourite songs, synchronising their emails, adjusting their schedules etc. However, these technologies utilise a well-known concept, machine learning, and such learning requires the collection of vast amounts of data, or in other words, personally identifiable and sensitive data belonging to individuals.

Many of the world's leading aircraft makers are in Germany for the ILA Berlin Air Show, with the focus this year on drone technology. Its development is helping spark a revolution in sustainable flight.

Blockchain technology has been the new kid on the "block" for some time now and is especially hot when enabling new currencies with the rollercoaster rides that these have recently taken. However, blockchains are not limited to crypto-currencies but are widely being adopted by other industries as well, especially the automotive industry. According to Frost and Sullivan, 10–15% of connected vehicle transactions are expected to be on blockchain by 2025. Toyota is exploring blockchain technology in collaboration with MIT Media Lab to develop a new mobility ecosystem that could accelerate the development of autonomous driving technology, particularly with secure data sharing, car/ride share transactions and usage-based insurance. German supplier ZF and IBM announced in 2017 that they were jointly developing Car eWallet, a payment technology targeting future mobility services.

The Alan Turing Institute and Rolls-Royce are among several UK-based organisations whose artificial intelligence (AI) initiatives have been highlighted by the Department for Digital, Culture, Media and Sport (DCMS). Read about how blockchain's inherent security makes it tamper-proof, and perfect for keeping and sharing records for transactions in many scenarios. You forgot to provide an Email Address. This email address doesn't appear to be valid. This email address is already registered.