Browsing by Author "Ha, Sook S."

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    Applications of Different Weighting Schemes to Improve Pathway-Based Analysis
    Ha, Sook S.; Kim, Inyoung; Wang, Yue; Xuan, Jianhua (Hindawi, 2011-05-22)
    Conventionally, pathway-based analysis assumes that genes in a pathway equally contribute to a biological function, thus assigning uniform weight to genes. However, this assumption has been proved incorrect, and applying uniform weight in the pathway analysis may not be an appropriate approach for the tasks like molecular classification of diseases, as genes in a functional group may have different predicting power. Hence, we propose to use different weights to genes in pathway-based analysis and devise four weighting schemes. We applied them in two existing pathway analysis methods using both real and simulated gene expression data for pathways. Among all schemes, random weighting scheme, which generates random weights and selects optimal weights minimizing an objective function, performs best in terms of 𝑷 value or error rate reduction. Weighting changes pathway scoring and brings up some new significant pathways, leading to the detection of disease-related genes that are missed under uniform weight.
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    Improved pig behavior analysis by optimizing window sizes for individual behaviors on acceleration and angular velocity data
    Alghamdi, Saleh; Zhao, Zhuqing; Ha, Dong S.; Morota, Gota; Ha, Sook S. (Oxford University Press, 2022-11-01)
    This paper presents the application of machine learning algorithms to identify pigs' behaviors from data collected using the wireless sensor nodes mounted on pigs. The sensor node attached to a pig's back senses the acceleration and angular velocity in three axes, and the sensed data are transmitted to a host computer wirelessly. Two video cameras, one attached to the ceiling of the pigpen and the other one to a fence, provided ground truth for data annotations. The data were collected from pigs for 131 h over 2 mo. As the typical behavior period depends on the behavior type, we segmented the acceleration data with different window sizes (WS) and step sizes (SS), and tested how the classification performance of different activities varied with different WS and SS. After exploring the possible combinations, we selected the optimum WS and SS. To compare performance, we used five machine learning algorithms, specifically support vector machine, k-nearest neighbors, decision trees, naive Bayes, and random forest (RF). Among the five algorithms, RF achieved the highest F1 score for four major behaviors consisting of 92.36% in total. The F1 scores of the algorithm were 0.98 for "eating,"0.99 for "lying,"0.93 for "walking,"and 0.91 for "standing"behaviors. The optimal WS was 7 s for "eating"and "lying,"and 3 s for "walking"and "standing."The proposed work demonstrates that, based on the length of behavior, the adaptive window and step sizes increase the classification performance.
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    Improving DDI Prediction Performance of Node2vec Embedding Using Graph Neural Network Based Models
    Ha, Sook S.; Zhang, Lihui (2022-11-25)
    A drug-drug interaction (DDI) is a reaction between two or more drugs that can reduce or increase the reaction of a medicine synergistically or cause adverse side effects. DDI detection, therefore, is an important objective in patient safety and pharmaceutical industry. Many researchers try to predict the DDI of unknown drugs by training the known DDI data in-silico approaches. In-silico approaches can be categorized into three groups: knowledge-based, similarity-based, and graph-based. Among them, graph-based approaches are known to have achieved great performance by casting DDI prediction as a link prediction problem on DDI graphs. In this paper, we explore how we can improve DDI prediction performance of the embedding learning method node2vec[6] using representation learning algorithms of graph neural networks (GNNs). We first created and trained node2vec model to obtain initial drug features; then we used three GNN based models to improve the learned node2vec drug embedding; finally, we used four different classifiers to implement link prediction, which is DDI prediction. Our experimental results showed that all four classifiers performance were improved using GNN learned embedding.