Jung-Woo Ha

Evolutionary hypernetwork models for aptamer-based cardiovascular disease diagnosis

We present a biology-inspired probabilistic graphical model, called the hypernetwork model, and its application to medical diagnosis of disease. The hypernetwork models are a way of simulated DNA computing. They have a set of hyperedges representing a subset of features in the training data. These characteristics allow the hypernetwork models to work similarly to associative memories and make their learning results more understandable. This comprehensibility is one of main advantages of the models over other machine learning algorithms such as support vector machines and artificial neural networks which are used in a wide range of applications but are not easy to understand their learning results. Since medical applications require both competitive performance and understandability of results, the hypernetwork models are suitable for this kind of applications. However, ordinary hypernetwork models have limitations that hyperedges cannot be changed after they are sampled once. To improve this diversity problem, we adopted simple evolutionary computation method, the hyperedges replacement strategy as the method of keeping the diversity into conventional hypernetworks in addition to error correction for model learning. To show the improvement, we used aptamer-based cardiovascular disease data. Experiment results show that the hypernetworks can achieve fairly competitive performance and the results are also comprehensible.

Constructing higher-order miRNA-mRNA interaction networks in prostate cancer via hypergraph-based learning

BackgroundDysregulation of genetic factors such as microRNAs (miRNAs) and mRNAs has been widely shown to be associated with cancer progression and development. In particular, miRNAs and mRNAs cooperate to affect biological processes, including tumorigenesis. The complexity of miRNA-mRNA interactions presents a major barrier to identifying their co-regulatory roles and functional effects. Thus, by computationally modeling these complex relationships, it may be possible to infer the gene interaction networks underlying complicated biological processes.ResultsWe propose a data-driven, hypergraph structural method for constructing higher-order miRNA-mRNA interaction networks from cancer genomic profiles. The proposed model explicitly characterizes higher-order relationships among genetic factors, from which cooperative gene activities in biological processes may be identified. The proposed model is learned by iteration of structure and parameter learning. The structure learning efficiently constructs a hypergraph structure by generating putative hyperedges representing complex miRNA-mRNA modules. It adopts an evolutionary method based on information-theoretic criteria. In the parameter learning phase, the constructed hypergraph is refined by updating the hyperedge weights using the gradient descent method. From the model, we produce biologically relevant higher-order interaction networks showing the properties of primary and metastatic prostate cancer, as candidates of potential miRNA-mRNA regulatory circuits.ConclusionsOur approach focuses on potential cancer-specific interactions reflecting higher-order relationships between miRNAs and mRNAs from expression profiles. The constructed miRNA-mRNA interaction networks show oncogenic or tumor suppression characteristics, which are known to be directly associated with prostate cancer progression. Therefore, the hypergraph-based model can assist hypothesis formulation for the molecular pathogenesis of cancer.

Evolutionary layered hypernetworks for identifying microRNA-mRNA regulatory modules

Exploring micro RNA (miRNA) and mRNA regulatory interactions may give new insights into diverse biological phenomena. While elucidating complex miRNA-mRNA interactions has been studied with experimental and computational approaches, it is still difficult to infer miRNA-mRNA regulatory modules. Here we present a novel method for identifying functional miRNA-mRNA modules from heterogeneous expression data. The proposed approach is layered hypernetworks consisting of two layers which are the layer of modality-dependent hypernetworks and of an integrating hypernetwork. The layered hypernetwork model is suitable for detecting relationships between heterogeneous modalities. Applied to the analysis of miRNA and mRNA expression profiles on multiple human cancers, the proposed model identifies oncogenic miRNA-mRNA regulatory modules. The experimental results show that our method provides a competitive performance to support vector machines, and outperforms other standard machine learning algorithms. The biological significance of the discovered miRNA-mRNA modules were validated by literature reviews.

Bayesian evolutionary hypergraph learning for predicting cancer clinical outcomes

Predicting the clinical outcomes of cancer patients is a challenging task in biomedicine. A personalized and refined therapy based on predicting prognostic outcomes of cancer patients has been actively sought in the past decade. Accurate prognostic prediction requires higher-order representations of complex dependencies among genetic factors. However, identifying the co-regulatory roles and functional effects of genetic interactions on cancer prognosis is hindered by the complexity of the interactions. Here we propose a prognostic prediction model based on evolutionary learning that identifies higher-order prognostic biomarkers of cancer clinical outcomes. The proposed model represents the interactions of prognostic genes as a combinatorial space. It adopts a flexible hypergraph structure composed of a large population of hyperedges that encode higher-order relationships among many genetic factors. The hyperedge population is optimized by an evolutionary learning method based on sequential Bayesian sampling. The proposed learning approach effectively balances performance and parsimony of the model using information-theoretic dependency and complexity-theoretic regularization priors. Using MAQC-II project data, we demonstrate that our model can handle high-dimensional data more effectively than state-of-the-art classification models. We also identify potential gene interactions characterizing prognosis and recurrence risk in cancer.

Cattle genome-wide analysis reveals genetic signatures in trypanotolerant N'Dama

BackgroundIndigenous cattle in Africa have adapted to various local environments to acquire superior phenotypes that enhance their survival under harsh conditions. While many studies investigated the adaptation of overall African cattle, genetic characteristics of each breed have been poorly studied.ResultsWe performed the comparative genome-wide analysis to assess evidence for subspeciation within species at the genetic level in trypanotolerant N’Dama cattle. We analysed genetic variation patterns in N’Dama from the genomes of 101 cattle breeds including 48 samples of five indigenous African cattle breeds and 53 samples of various commercial breeds. Analysis of SNP variances between cattle breeds using wMI, XP-CLR, and XP-EHH detected genes containing N’Dama-specific genetic variants and their potential associations. Functional annotation analysis revealed that these genes are associated with ossification, neurological and immune system. Particularly, the genes involved in bone formation indicate that local adaptation of N’Dama may engage in skeletal growth as well as immune systems.ConclusionsOur results imply that N’Dama might have acquired distinct genotypes associated with growth and regulation of regional diseases including trypanosomiasis. Moreover, this study offers significant insights into identifying genetic signatures for natural and artificial selection of diverse African cattle breeds.

Layered hypernetwork models for cross-modal associative text and image keyword generation in multimodal information retrieval

Conventional methods for multimodal data retrieval use text-tag based or cross-modal approaches such as tag-image co-occurrence and canonical correlation analysis. Since there are differences of granularity in text and image features, however, approaches based on lower-order relationship between modalities may have limitations. Here, we propose a novel text and image keyword generation method by cross-modal associative learning and inference with multimodal queries. We use a modified hypernetwork model, i.e. layered hypernetworks (LHNs) which consists of the first (lower) layer and the second (upper) layer which has more than two modality-dependent hypernetworks and one modality-integrating hypernetwork, respectively. LHNs learn higher-order associative relationships between text and image modalities by training on an example set. After training, LHNs are used to extend multimodal queries by generating text and image keywords via cross-modal inference, i.e. text-to-image and image-to-text. The LHNs are evaluated on Korean magazine articles with images on women fashions and life-style. Experimental results show that the proposed method generates vision-language cross-modal keywords with high accuracy. The results also show that multimodal queries improve the accuracy of keyword generation compared with uni-modal ones.

Mutual information-based evolution of hypernetworks for brain data analysis

Cortical analysis becomes increasingly important for brain research and clinical diagnosis. This problem involves a combinatorial search to find the essential modules among a large number of brain regions. Despite several statistical approaches, cortical analysis remains a formidable challenge due to high-dimensionality and sparsity of data. Here we describe an evolutionary method for finding significant modules from cortical data. The method uses a hypernetwork which is encoded as a population of hyperedges, where hyperedges represent building blocks or potential modules. We develop an efficient method for evolving the hypernetwork using mutual information to generate essential hyperedges. We evaluate the method on predicting intelligence quotient (IQ) levels and finding potential significant modules on IQ from brain MRI data consisting of 62 healthy adults with over 80,000 measured points (variables). The experimental results show that our information-theoretic evolutionary hypernetworks improve the classification accuracy by 5∼15%. Moreover, it extracts significant cortical modules that distinguish high IQ from low IQ groups.

Dual-memory neural networks for modeling cognitive activities of humans via wearable sensors

Wearable devices, such as smart glasses and watches, allow for continuous recording of everyday life in a real world over an extended period of time or lifelong. This possibility helps better understand the cognitive behavior of humans in real life as well as build human-aware intelligent agents for practical purposes. However, modeling the human cognitive activity from wearable-sensor data stream is challenging because learning new information often results in loss of previously acquired information, causing a problem known as catastrophic forgetting. Here we propose a deep-learning neural network architecture that resolves the catastrophic forgetting problem. Based on the neurocognitive theory of the complementary learning systems of the neocortex and hippocampus, we introduce a dual memory architecture (DMA) that, on one hand, slowly acquires the structured knowledge representations and, on the other hand, rapidly learns the specifics of individual experiences. The DMA system learns continuously through incremental feature adaptation and weight transfer. We evaluate the performance on two real-life datasets, the CIFAR-10 image-stream dataset and the 46-day Lifelog dataset collected from Google Glass, showing that the proposed model outperforms other online learning methods.

Place Recognition Using Ensemble Learning of Mobile Multimodal Sensory Information

Place awareness is an essential for location-based services that are widely provided to smartphone users. However, traditional GPS-based methods are only valid outdoors where the GPS signal is strong and also require symbolic place information of the physical location. In this paper, environmental sounds and images are used to recognize important aspects of each place. The proposed method extracts feature vectors from visual, auditory and location data recorded by a smartphone with built-in camera, microphone and GPS sensors modules. The heterogeneous feature vectors were then learned by an ensemble learning method that learns each group of feature vectors for each classifier respectively and votes to produce the highest weighted result. The proposed method is evaluated for place recognition using a data group of 3000 samples in six places and the experimental results show a remarkably improved recognition accuracy when using all kinds of sensory data comparing to results using data from a single sensor or audio-visual integrated data only.

Text-to-image retrieval based on incremental association via multimodal hypernetworks

Text-to-image retrieval is to retrieve the images associated with the textual queries. A text-to-image retrieval model requires an incremental learning method for its practical use since the multimodal data grow up dramatically. Here we propose an incremental text-to-image retrieval method using a multimodal association model. The association model is based on a hypernetwork (HN) where a vertex corresponds to a textual word or a visual patch and a hyperedge represents a higher-order multimodal association. Using the HN incrementally learned by a sequential Bayesian sampling, in the multimodal hypernetwork-based text-to-image retrieval, a given text query is crossmodally expanded to the visual query and then similar images are retrieved to the expanded visual query. We evaluated the proposed method using 3,000 images with textual description from Flickr.com. The experimental results present that the proposed method achieves very competitive retrieval performances compared to a baseline method. Moreover, we demonstrate that our method provides robust text-to-image retrieval results for the increasing data.