Exploring and Exploiting Disease Interactions from Multi-Relational Gene and Phenotype Networks

The availability of electronic health care records is unlocking the potential for novel studies on understanding and modeling disease co-morbidities based on both phenotypic and genetic data. Moreover, the insurgence of increasingly reliable phenotypic data can aid further studies on investigating the potential genetic links among diseases. The goal is to create a feedback loop where computational tools guide and facilitate research, leading to improved biological knowledge and clinical standards, which in turn should generate better data. We build and analyze disease interaction networks based on data collected from previous genetic association studies and patient medical histories, spanning over 12 years, acquired from a regional hospital. By exploring both individual and combined interactions among these two levels of disease data, we provide novel insight into the interplay between genetics and clinical realities. Our results show a marked difference between the well defined structure of genetic relationships and the chaotic co-morbidity network, but also highlight clear interdependencies. We demonstrate the power of these dependencies by proposing a novel multi-relational link prediction method, showing that disease co-morbidity can enhance our currently limited knowledge of genetic association. Furthermore, our methods for integrated networks of diverse data are widely applicable and can provide novel advances for many problems in systems biology and personalized medicine

Predicting Positive p53 Cancer Rescue Regions Using Most Informative Positive (MIP) Active Learning

Many protein engineering problems involve finding mutations that produce proteins with a particular function. Computational active learning is an attractive approach to discover desired biological activities. Traditional active learning techniques have been optimized to iteratively improve classifier accuracy, not to quickly discover biologically significant results. We report here a novel active learning technique, Most Informative Positive (MIP), which is tailored to biological problems because it seeks novel and informative positive results. MIP active learning differs from traditional active learning methods in two ways: (1) it preferentially seeks Positive (functionally active) examples; and (2) it may be effectively extended to select gene regions suitable for high throughput combinatorial mutagenesis. We applied MIP to discover mutations in the tumor suppressor protein p53 that reactivate mutated p53 found in human cancers. This is an important biomedical goal because p53 mutants have been implicated in half of all human cancers, and restoring active p53 in tumors leads to tumor regression. MIP found Positive (cancer rescue) p53 mutants in silico using 33% fewer experiments than traditional non-MIP active learning, with only a minor decrease in classifier accuracy. Applying MIP to in vivo experimentation yielded immediate Positive results. Ten different p53 mutations found in human cancers were paired in silico with all possible single amino acid rescue mutations, from which MIP was used to select a Positive Region predicted to be enriched for p53 cancer rescue mutants. In vivo assays showed that the predicted Positive Region: (1) had significantly more (p<0.01) new strong cancer rescue mutants than control regions (Negative, and non-MIP active learning); (2) had slightly more new strong cancer rescue mutants than an Expert region selected for purely biological considerations; and (3) rescued for the first time the previously unrescuable p53 cancer mutant P152L

A reinforcement learning approach to autonomous decision-making in Smart Electricity Markets

For the vision of a Smart Grid to materialize, substantial advances in intelligent decentralized control mechanisms are required. We propose a novel class of autonomous broker agents for retail electricity trading that can operate in a wide range of Smart Electricity Markets, and that are capable of deriving long-term, profit-maximizing policies. Our brokers use Reinforcement Learning with function approximation, they can accommodate arbitrary economic signals from their environments, and they learn efficiently over the large state spaces resulting from these signals. Our design is the first that can accommodate an offline training phase so as to automatically optimize the broker for particular market conditions. We demonstrate the performance of our design in a series of experiments using real-world energy market data, and find that it outperforms previous approaches by a significant margin

Active learning for probability estimation using Jensen-Shannon divergence

Abstract. Active selection of good training examples is an important approach to reducing data-collection costs in machine learning; however, most existing methods focus on maximizing classification accuracy. In many applications, such as those with unequal misclassification costs, producing good class probability estimates (CPEs) is more important than optimizing classification accuracy. We introduce novel approaches to active learning based on the algorithms Bootstrap-LV and ACTIVEDECORATE, by using Jensen-Shannon divergence (a similarity measure for probability distributions) to improve sample selection for optimizing CPEs. Comprehensive experimental results demonstrate the benefits of our approaches.

Fast Active Exploration for Link-Based Preference Learning using Gaussian Processes

Abstract. In preference learning, the algorithm observes pairwise relative judgments (preference) between items as training data for learning an ordering of all items. This is an important learning problem for applications where absolute feedback is difficult to elicit, but pairwise judgments are readily available (e.g., via implicit feedback [13]). While it was already shown that active learning can effectively reduce the number of training pairs needed, the most successful existing algorithms cannot generalize over items or queries. Considering web search as an example, they would need to learn a separate relevance score for each document-query pair from scratch. To overcome this inefficiency, we propose a link-based active preference learning method based on Gaussian Processes (GPs) that incorporates dependency information from both feature-vector representations as well as relations. Specifically, to meet the requirement on computational efficiency of active exploration, we introduce a novel incremental update method that scales as well as the non-generalizing models. The proposed algorithm is evaluated on datasets for information retrieval, showing that it learns substantially faster than algorithms that cannot model dependencies.