Comprehensive evaluation of deep and graph learning on drug-drug interactions prediction

Recent advances and achievements of artificial intelligence (AI) as well as deep and graph learning models have established their usefulness in biomedical applications, especially in drug-drug interactions (DDIs). DDIs refer to a change in the effect of one drug to the presence of another drug in the human body, which plays an essential role in drug discovery and clinical research. DDIs prediction through traditional clinical trials and experiments is an expensive and time-consuming process. To correctly apply the advanced AI and deep learning, the developer and user meet various challenges such as the availability and encoding of data resources, and the design of computational methods. This review summarizes chemical structure based, network based, NLP based and hybrid methods, providing an updated and accessible guide to the broad researchers and development community with different domain knowledge. We introduce widely-used molecular representation and describe the theoretical frameworks of graph neural network models for representing molecular structures. We present the advantages and disadvantages of deep and graph learning methods by performing comparative experiments. We discuss the potential technical challenges and highlight future directions of deep and graph learning models for accelerating DDIs prediction.Comment: Accepted by Briefings in Bioinformatic

The robot's vista space : a computational 3D scene analysis

Swadzba A. The robot's vista space : a computational 3D scene analysis. Bielefeld (Germany): Bielefeld University; 2011.The space that can be explored quickly from a fixed view point without locomotion is known as the vista space. In indoor environments single rooms and room parts follow this definition. The vista space plays an important role in situations with agent-agent interaction as it is the directly surrounding environment in which the interaction takes place. A collaborative interaction of the partners in and with the environment requires that both partners know where they are, what spatial structures they are talking about, and what scene elements they are going to manipulate. This thesis focuses on the analysis of a robot's vista space. Mechanisms for extracting relevant spatial information are developed which enable the robot to recognize in which place it is, to detect the scene elements the human partner is talking about, and to segment scene structures the human is changing. These abilities are addressed by the proposed holistic, aligned, and articulated modeling approach. For a smooth human-robot interaction, the computed models should be aligned to the partner's representations. Therefore, the design of the computational models is based on the combination of psychological results from studies on human scene perception with basic physical properties of the perceived scene and the perception itself. The holistic modeling realizes a categorization of room percepts based on the observed 3D spatial layout. Room layouts have room type specific features and fMRI studies have shown that some of the human brain areas being active in scene recognition are sensitive to the 3D geometry of a room. With the aligned modeling, the robot is able to extract the hierarchical scene representation underlying a scene description given by a human tutor. Furthermore, it is able to ground the inferred scene elements in its own visual perception of the scene. This modeling follows the assumption that cognition and language schematize the world in the same way. This is visible in the fact that a scene depiction mainly consists of relations between an object and its supporting structure or between objects located on the same supporting structure. Last, the articulated modeling equips the robot with a methodology for articulated scene part extraction and fast background learning under short and disturbed observation conditions typical for human-robot interaction scenarios. Articulated scene parts are detected model-less by observing scene changes caused by their manipulation. Change detection and background learning are closely coupled because change is defined phenomenologically as variation of structure. This means that change detection involves a comparison of currently visible structures with a representation in memory. In range sensing this comparison can be nicely implement as subtraction of these two representations. The three modeling approaches enable the robot to enrich its visual perceptions of the surrounding environment, the vista space, with semantic information about meaningful spatial structures useful for further interaction with the environment and the human partner

Neurons and symbols: a manifesto

We discuss the purpose of neural-symbolic integration including its principles, mechanisms and applications. We outline a cognitive computational model for neural-symbolic integration, position the model in the broader context of multi-agent systems, machine learning and automated reasoning, and list some of the challenges for the area of neural-symbolic computation to achieve the promise of effective integration of robust learning and expressive reasoning under uncertainty

What Can Artificial Intelligence Do for Scientific Realism?

The paper proposes a synthesis between human scientists and artificial representation learning models as a way of augmenting epistemic warrants of realist theories against various anti-realist attempts. Towards this end, the paper fleshes out unconceived alternatives not as a critique of scientific realism but rather a reinforcement, as it rejects the retrospective interpretations of scientific progress, which brought about the problem of alternatives in the first place. By utilising adversarial machine learning, the synthesis explores possibility spaces of available evidence for unconceived alternatives providing modal knowledge of what is possible therein. As a result, the epistemic warrant of synthesised realist theories should emerge bolstered as the underdetermination by available evidence gets reduced. While shifting the realist commitment away from theoretical artefacts towards modalities of the possibility spaces, the synthesis comes out as a kind of perspectival modelling

Symbol Emergence in Robotics: A Survey

Humans can learn the use of language through physical interaction with their environment and semiotic communication with other people. It is very important to obtain a computational understanding of how humans can form a symbol system and obtain semiotic skills through their autonomous mental development. Recently, many studies have been conducted on the construction of robotic systems and machine-learning methods that can learn the use of language through embodied multimodal interaction with their environment and other systems. Understanding human social interactions and developing a robot that can smoothly communicate with human users in the long term, requires an understanding of the dynamics of symbol systems and is crucially important. The embodied cognition and social interaction of participants gradually change a symbol system in a constructive manner. In this paper, we introduce a field of research called symbol emergence in robotics (SER). SER is a constructive approach towards an emergent symbol system. The emergent symbol system is socially self-organized through both semiotic communications and physical interactions with autonomous cognitive developmental agents, i.e., humans and developmental robots. Specifically, we describe some state-of-art research topics concerning SER, e.g., multimodal categorization, word discovery, and a double articulation analysis, that enable a robot to obtain words and their embodied meanings from raw sensory--motor information, including visual information, haptic information, auditory information, and acoustic speech signals, in a totally unsupervised manner. Finally, we suggest future directions of research in SER.Comment: submitted to Advanced Robotic

TopologyNet: Topology based deep convolutional neural networks for biomolecular property predictions

Although deep learning approaches have had tremendous success in image, video and audio processing, computer vision, and speech recognition, their applications to three-dimensional (3D) biomolecular structural data sets have been hindered by the entangled geometric complexity and biological complexity. We introduce topology, i.e., element specific persistent homology (ESPH), to untangle geometric complexity and biological complexity. ESPH represents 3D complex geometry by one-dimensional (1D) topological invariants and retains crucial biological information via a multichannel image representation. It is able to reveal hidden structure-function relationships in biomolecules. We further integrate ESPH and convolutional neural networks to construct a multichannel topological neural network (TopologyNet) for the predictions of protein-ligand binding affinities and protein stability changes upon mutation. To overcome the limitations to deep learning arising from small and noisy training sets, we present a multitask topological convolutional neural network (MT-TCNN). We demonstrate that the present TopologyNet architectures outperform other state-of-the-art methods in the predictions of protein-ligand binding affinities, globular protein mutation impacts, and membrane protein mutation impacts.Comment: 20 pages, 8 figures, 5 table