31 research outputs found
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
Towards future situation-Awareness:A conceptual middleware framework for opportunistic situation identification
Opportunistic Situation Identification (OSI) is new paradigms for situation-Aware systems, in which contexts for situation identification are sensed through sensors that happen to be available rather than pre-deployed and application-specific ones. OSI extends the application usage scale and reduces system costs. However, designing and implementing OSI module of situation-Aware systems encounters several challenges, including the uncertainty of context availability, vulnerable network connectivity and privacy threat. This paper proposes a novel middleware framework to tackle such challenges, and its intuition is that it facilitates performing the situation reasoning locally on a smartphone without needing to rely on the cloud, thus reducing the dependency on the network and being more privacy-preserving. To realize such intuitions, we propose a hybrid learning approach to maximize the reasoning accuracy using limited phone's storage space, with the combination of two the-state-The-Art techniques. Specifically, this paper provides a genetic algorithm based optimization approach to determine which pre-computed models will be selected for storage under the storage constraints. Validation of the approach based on an open dataset indicates that the proposed approach achieves higher accuracy with comparatively small storage cost. Further, the proposed utility function for model selection performs better than three baseline utility functions
Efficient Architectural Design Space Exploration via Predictive Modeling
Efficiently exploring exponential-size architectural design spaces with many interacting parameters remains an open problem: the sheer number of experiments required renders detailed simulation intractable. We attack this via an automated approach that builds accurate predictive models. We simulate sampled points, using results to teach our models the function describing relationships among design parameters. The models can be queried and are very fast, enabling efficient design tradeoff discovery. We validate our approach via two uniprocessor sensitivity studies, predicting IPC with only 1-2 % error. In an experimental study using the approach, training on 1 % of a 250Kpoint CMP design space allows our models to predict performance with only 4-5 % error. Our predictive modeling combines well with techniques that reduce the time taken by each simulation experiment, achieving net time savings of three-four orders of magnitude