Deep Learning for Metagenomic Data: using 2D Embeddings and Convolutional Neural Networks

Deep learning (DL) techniques have had unprecedented success when applied to images, waveforms, and texts to cite a few. In general, when the sample size (N) is much greater than the number of features (d), DL outperforms previous machine learning (ML) techniques, often through the use of convolution neural networks (CNNs). However, in many bioinformatics ML tasks, we encounter the opposite situation where d is greater than N. In these situations, applying DL techniques (such as feed-forward networks) would lead to severe overfitting. Thus, sparse ML techniques (such as LASSO e.g.) usually yield the best results on these tasks. In this paper, we show how to apply CNNs on data which do not have originally an image structure (in particular on metagenomic data). Our first contribution is to show how to map metagenomic data in a meaningful way to 1D or 2D images. Based on this representation, we then apply a CNN, with the aim of predicting various diseases. The proposed approach is applied on six different datasets including in total over 1000 samples from various diseases. This approach could be a promising one for prediction tasks in the bioinformatics field.Comment: Accepted at NIPS 2017 Workshop on Machine Learning for Health (https://ml4health.github.io/2017/); In Proceedings of the NIPS ML4H 2017 Workshop in Long Beach, CA, USA

Heterogeneity in Kv2 Channel Expression Shapes Action Potential Characteristics and Firing Patterns in CA1 versus CA2 Hippocampal Pyramidal Neurons.

The CA1 region of the hippocampus plays a critical role in spatial and contextual memory, and has well-established circuitry, function and plasticity. In contrast, the properties of the flanking CA2 pyramidal neurons (PNs), important for social memory, and lacking CA1-like plasticity, remain relatively understudied. In particular, little is known regarding the expression of voltage-gated K+ (Kv) channels and the contribution of these channels to the distinct properties of intrinsic excitability, action potential (AP) waveform, firing patterns and neurotransmission between CA1 and CA2 PNs. In the present study, we used multiplex fluorescence immunolabeling of mouse brain sections, and whole-cell recordings in acute mouse brain slices, to define the role of heterogeneous expression of Kv2 family Kv channels in CA1 versus CA2 pyramidal cell excitability. Our results show that the somatodendritic delayed rectifier Kv channel subunits Kv2.1, Kv2.2, and their auxiliary subunit AMIGO-1 have region-specific differences in expression in PNs, with the highest expression levels in CA1, a sharp decrease at the CA1-CA2 boundary, and significantly reduced levels in CA2 neurons. PNs in CA1 exhibit a robust contribution of Guangxitoxin-1E-sensitive Kv2-based delayed rectifier current to AP shape and after-hyperpolarization potential (AHP) relative to that seen in CA2 PNs. Our results indicate that robust Kv2 channel expression confers a distinct pattern of intrinsic excitability to CA1 PNs, potentially contributing to their different roles in hippocampal network function

Rounding Methods for Discrete Linear Classification (Extended Version)

Learning discrete linear classifiers is known as a difficult challenge. In this paper, this learning task is cast as combinatorial optimization problem: given a training sample formed by positive and negative feature vectors in the Euclidean space, the goal is to find a discrete linear function that minimizes the cumulative hinge loss of the sample. Since this problem is NP-hard, we examine two simple rounding algorithms that discretize the fractional solution of the problem. Generalization bounds are derived for several classes of binary-weighted linear functions, by analyzing the Rademacher complexity of these classes and by establishing approximation bounds for our rounding algorithms. Our methods are evaluated on both synthetic and real-world data

Game Theory Models for Multi-Robot Patrolling of Infraestructures

Abstract This work is focused on the problem of performing multi‐robot patrolling for infrastructure security applications in order to protect a known environment at critical facilities. Thus, given a set of robots and a set of points of interest, the patrolling task consists of constantly visiting these points at irregular time intervals for security purposes. Current existing solutions for these types of applications are predictable and inflexible. Moreover, most of the previous centralized and deterministic solutions and only few efforts have been made to integrate dynamic methods. Therefore, the development of new dynamic and decentralized collaborative approaches in order to solve the aforementioned problem by implementing learning models from Game Theory. The model selected in this work that includes belief‐based and reinforcement models as special cases is called Experience‐Weighted Attraction. The problem has been defined using concepts of Graph Theory to represent the environment in order to work with such Game Theory techniques. Finally, the proposed methods have been evaluated experimentally by using a patrolling simulator. The results obtained have been compared with previous availabl

The Power of Swap Deals in Distributed Resource Allocation

International audienceIn the simple resource allocation setting consisting in assigning exactly one resource per agent, the top trading cycle procedure stands out as being the undisputed method of choice. It remains however a centralized procedure which may not well suited in the context of multiagent systems, where distributed coordination may be problematic. In this paper, we investigate the power of dynamics based on rational bilateral deals (swaps) in such settings. While they may induce a high efficiency loss, we provide several new elements that temper this fact: (i) we identify a natural domain where convergence to a Pareto-optimal allocation can be guaranteed, (ii) we show that the worst-case loss of welfare is as good as it can be under the assumption of individual rationality, (iii) we provide a number of experimental results, showing that such dynamics often provide good outcomes, especially in light of their simplicity, and (iv) we prove the NP-hardness of deciding whether an allocation maximizing utilitarian or egalitarian welfare is reachable

Multiagent resource allocation with k-additive utility functions

We briefly review previous work on the welfare engineering framework where autonomous software agents negotiate on the allocation of a number of discrete resources, and point out connections to combinatorial optimisation problems, including combinatorial auctions, that shed light on the computational complexity of the framework. We give particular consideration to scenarios where the preferences of agents are modelled in terms of k-additive utility functions, i.e. scenarios where synergies between different resources are restricted to bundles of at most k items. Key words: negotiation, representation of utility functions, social welfare, combinatorial optimisation, bidding languages for combinatorial auctions

Expressive Power of Weighted Propositional Formulas for Cardinal Preference Modelling

As proposed in various places, a set of propositional formulas, each associated with a numerical weight, can be used to model the preferences of an agent in combinatorial domains. If the range of possible choices can be represented by the set of possible assignments of propositional symbols to truth values, then the utility of an assignment is given by the sum of the weights of the formulas it satisfies. Our aim in this paper is twofold: (1) to establish correspondences between certain types of weighted formulas and well-known classes of utility functions (such as monotonic, concave or k-additive functions); and (2) to obtain results on the comparative succinctness of different types of weighted formulas for representing the same class of utility functions

Allocation in Practice

How do we allocate scarcere sources? How do we fairly allocate costs? These are two pressing challenges facing society today. I discuss two recent projects at NICTA concerning resource and cost allocation. In the first, we have been working with FoodBank Local, a social startup working in collaboration with food bank charities around the world to optimise the logistics of collecting and distributing donated food. Before we can distribute this food, we must decide how to allocate it to different charities and food kitchens. This gives rise to a fair division problem with several new dimensions, rarely considered in the literature. In the second, we have been looking at cost allocation within the distribution network of a large multinational company. This also has several new dimensions rarely considered in the literature.Comment: To appear in Proc. of 37th edition of the German Conference on Artificial Intelligence (KI 2014), Springer LNC

Work Semantics. In Search of an Alternative Conceptual Matrix for Labour and Social Historians

The idea for the project presented in this volume began with an encounter and a discovery. When we – a medievalist and a sinologist – first met in autumn 2017, we realised that although we came from different disciplines and worked on different regions and time periods, we were struggling with the same problem: As historians working on slaving practices in the Venetian empire (14th–16th centuries) respectively servitude in late imperial China (15th–19th centuries), we were both spending much of our time explaining the contextual differences and similarities between the social configurations we were studying to the broader community of social, labour, and global historians. We both felt that our objects of study did not fit well within the much-debated subfield of “free and unfree labour”, and that the postcolonial critiques and the so-called global turn in history did not solve the conceptual problem we were facing. Integrating a medieval or Chinese case study into a conference panel or a special journal issue on household service or slavery helped to enlarge the horizon of the historiographical debates on the history of unfree labour relations, but the umbrella terms of these subfields of study and the limited conceptual referencesavailable did little to help us understand and properly convey the social taxonomies shaping the power relations we were studying.The idea for the project presented in this volume began with an encounter and a discovery. When we – a medievalist and a sinologist – first met in autumn 2017, we realised that although we came from different disciplines and worked on different regions and time periods, we were struggling with the same problem: As historians working on slaving practices in the Venetian empire (14th–16th centuries) respectively servitude in late imperial China (15th–19th centuries), we were both spending much of our time explaining the contextual differences and similarities between the social configurations we were studying to the broader community of social, labour, and global historians. We both felt that our objects of study did not fit well within the much-debated subfield of “free and unfree labour”, and that the postcolonial critiques and the so-called global turn in history did not solve the conceptual problem we were facing. Integrating a medieval or Chinese case study into a conference panel or a special journal issue on household service or slavery helped to enlarge the horizon of the historiographical debates on the history of unfree labour relations, but the umbrella terms of these subfields of study and the limited conceptual referencesavailable did little to help us understand and properly convey the social taxonomies shaping the power relations we were studying

Expression and immunogenicity of the mycobacterial Ag85B/ESAT-6 antigens produced in transgenic plants by elastin-like peptide fusion strategy.

International audienceThis study explored a novel system combining plant-based production and the elastin-like peptide (ELP) fusion strategy to produce vaccinal antigens against tuberculosis. Transgenic tobacco plants expressing the mycobacterial antigens Ag85B and ESAT-6 fused to ELP (TBAg-ELP) were generated. Purified TBAg-ELP was obtained by the highly efficient, cost-effective, inverse transition cycling (ICT) method and tested in mice. Furthermore, safety and immunogenicity of the crude tobacco leaf extracts were assessed in piglets. Antibodies recognizing mycobacterial antigens were produced in mice and piglets. A T-cell immune response able to recognize the native mycobacterial antigens was detected in mice. These findings showed that the native Ag85B and ESAT-6 mycobacterial B- and T-cell epitopes were conserved in the plant-expressed TBAg-ELP. This study presents the first results of an efficient plant-expression system, relying on the elastin-like peptide fusion strategy, to produce a safe and immunogenic mycobacterial Ag85B-ESAT-6 fusion protein as a potential vaccine candidate against tuberculosis