Machine learning applications in proteomics research: How the past can boost the future

Machine learning is a subdiscipline within artificial intelligence that focuses on algorithms that allow computers to learn solving a (complex) problem from existing data. This ability can be used to generate a solution to a particularly intractable problem, given that enough data are available to train and subsequently evaluate an algorithm on. Since MS-based proteomics has no shortage of complex problems, and since publicly available data are becoming available in ever growing amounts, machine learning is fast becoming a very popular tool in the field. We here therefore present an overview of the different applications of machine learning in proteomics that together cover nearly the entire wet- and dry-lab workflow, and that address key bottlenecks in experiment planning and design, as well as in data processing and analysis.acceptedVersio

Recovery of gene haplotypes from a metagenome

AbstractElucidation of population-level diversity of microbiomes is a significant step towards a complete understanding of the evolutionary, ecological and functional importance of microbial communities. Characterizing this diversity requires the recovery of the exact DNA sequence (haplotype) of each gene isoform from every individual present in the community. To address this, we present Hansel and Gretel: a freely-available data structure and algorithm, providing a software package that reconstructs the most likely haplotypes from metagenomes. We demonstrate recovery of haplotypes from short-read Illumina data for a bovine rumen microbiome, and verify our predictions are 100% accurate with long-read PacBio CCS sequencing. We show that Gretel’s haplotypes can be analyzed to determine a significant difference in mutation rates between core and accessory gene families in an ovine rumen microbiome. All tools, documentation and data for evaluation are open source and available via our repository: https://github.com/samstudio8/gretel</jats:p

kLog: A Language for Logical and Relational Learning with Kernels

We introduce kLog, a novel approach to statistical relational learning. Unlike standard approaches, kLog does not represent a probability distribution directly. It is rather a language to perform kernel-based learning on expressive logical and relational representations. kLog allows users to specify learning problems declaratively. It builds on simple but powerful concepts: learning from interpretations, entity/relationship data modeling, logic programming, and deductive databases. Access by the kernel to the rich representation is mediated by a technique we call graphicalization: the relational representation is first transformed into a graph --- in particular, a grounded entity/relationship diagram. Subsequently, a choice of graph kernel defines the feature space. kLog supports mixed numerical and symbolic data, as well as background knowledge in the form of Prolog or Datalog programs as in inductive logic programming systems. The kLog framework can be applied to tackle the same range of tasks that has made statistical relational learning so popular, including classification, regression, multitask learning, and collective classification. We also report about empirical comparisons, showing that kLog can be either more accurate, or much faster at the same level of accuracy, than Tilde and Alchemy. kLog is GPLv3 licensed and is available at http://klog.dinfo.unifi.it along with tutorials

Cheaper faster drug development validated by the repositioning of drugs against neglected tropical diseases

There is an urgent need to make drug discovery cheaper and faster. This will enable the development of treatments for diseases currently neglected for economic reasons, such as tropical and orphan diseases, and generally increase the supply of new drugs. Here, we report the Robot Scientist 'Eve' designed to make drug discovery more economical. A Robot Scientist is a laboratory automation system that uses artificial intelligence (AI) techniques to discover scientific knowledge through cycles of experimentation. Eve integrates and automates library-screening, hit-confirmation, and lead generation through cycles of quantitative structure activity relationship learning and testing. Using econometric modelling we demonstrate that the use of AI to select compounds economically outperforms standard drug screening. For further efficiency Eve uses a standardized form of assay to compute Boolean functions of compound properties. These assays can be quickly and cheaply engineered using synthetic biology, enabling more targets to be assayed for a given budget. Eve has repositioned several drugs against specific targets in parasites that cause tropical diseases. One validated discovery is that the anti-cancer compound TNP-470 is a potent inhibitor of dihydrofolate reductase from the malaria-causing parasite Plasmodium vivax

Predictive Quantitative Structure-Activity Relationship Models and their use for the Efficient Screening of Molecules (Automatisch leren van structuur-activiteitsrelaties met hoge voorspellende kracht en hun toepassing bij het efficiënt screenen van moleculen)

We explore two avenues where machine learning can help drug discovery: predictive models of in vivo or in vitro effects of molecules (known as Quantitative Structure-Activity Relationship or QSAR models), and the selection of efficient experiments based on such models.In the first part, we present methods to improve the predictive power of graph kernel based molecule classifiers. The bias of existing graph kernels can be improved by augmenting atom-bond graphs with functional groups. This novel representation allows a machine learning algorithm to use both high-level functional and low-level atomic information, without any change to the kernel or learning algorithm. In internal validation tests, we observe consistently higher AUROCs for all tested kernels.We also introduce a novel, efficient graph kernel called the Neighborhood Subgraph Pairwise Distance Kernel. The feature space of this kernel is the space of pairs of topological balls and the interpair distance. Using this kernel, a standard support vector machine outperforms existing methods in the prediction of all investigated target properties: mutagenicity, in vivo toxicity, antiviral activity, and cancer suppression.In the second part, we tackle the problem of efficient experimentation in drug discovery using optimization assisted by a learned surrogate model and we evaluate different experiment selection strategies. The algorithm is extended to accommodate drug discovery needs, such as the selection of many parallel experiments. The algorithm is integrated in an automated drug discovery platform, the robot scientist Eve. It is also applied to the optimization of the design of nanofiltration membranes.status: publishe

How to build a self-driving vehicle

Why are there barely any self-driving cars on the road yet? We look at the building blocks for an autonomous road vehicle. What is the state of the art, and what are the limitations? In Lommel, Flanders Make is building technology for autonomous public transit and specialty vehicles. The prototypes are equipped with various sensors, artificial intelligence, and controllers.status: publishe

Active learning for drug lead discovery

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Augmented molecular graph kernel QSARs

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