Life Sciences Commons^™

Auditing Snomed Ct Hierarchical Relations Based On Lexical Features Of Concepts In Non-Lattice Subgraphs, Licong Cui, Olivier Bodenreider, Jay Shi, Guo-Qiang Zhang

Computer Science Faculty Publications

Objective—We introduce a structural-lexical approach for auditing SNOMED CT using a combination of non-lattice subgraphs of the underlying hierarchical relations and enriched lexical attributes of fully specified concept names. Our goal is to develop a scalable and effective approach that automatically identifies missing hierarchical IS-A relations.

Methods—Our approach involves 3 stages. In stage 1, all non-lattice subgraphs of SNOMED CT’s IS-A hierarchical relations are extracted. In stage 2, lexical attributes of fully-specified concept names in such non-lattice subgraphs are extracted. For each concept in a non-lattice subgraph, we enrich its set of attributes with attributes from its ancestor …

Stage-Specific Predictive Models For Cancer Survivability, Elham Sagheb Hossein Pour

Theses and Dissertations

Survivability of cancer strongly depends on the stage of cancer. In most previous works, machine learning survivability prediction models for a particular cancer, were trained and evaluated together on all stages of the cancer. In this work, we trained and evaluated survivability prediction models for five major cancers, together on all stages and separately for every stage. We named these models joint and stage-specific models respectively. The obtained results for the cancers which we investigated reveal that, the best model to predict the survivability of the cancer for one specific stage is the model which is specifically built for that …

Domain Specific Document Retrieval Framework For Real-Time Social Health Data, Swapnil Soni

Kno.e.sis Publications

With the advent of the web search and microblogging, the percentage of Online Health Information Seekers (OHIS) using these online services to share and seek health real-time information has in- creased exponentially. OHIS use web search engines or microblogging search services to seek out latest, relevant as well as reliable health in- formation. When OHIS turn to microblogging search services to search real-time content, trends and breaking news, etc. the search results are not promising. Two major challenges exist in the current microblogging search engines are keyword based techniques and results do not contain real-time information. To address these challenges, …

Mining Effective Multi-Segment Sliding Window For Pathogen Incidence Rate Prediction, Lei Duan, Changjie Tang, Xiasong Li, Guozhu Dong, Xianming Wang, Jie Zuo, Min Jiang, Zhongqi Li, Yongqing Zhang

Kno.e.sis Publications

Pathogen incidence rate prediction, which can be considered as time series modeling, is an important task for infectious disease incidence rate prediction and for public health. This paper investigates the application of a genetic computation technique, namely GEP, for pathogen incidence rate prediction. To overcome the shortcomings of traditional sliding windows in GEP-based time series modeling, the paper introduces the problem of mining effective sliding window, for discovering optimal sliding windows for building accurate prediction models. To utilize the periodical characteristic of pathogen incidence rates, a multi-segment sliding window consisting of several segments from different periodical intervals is proposed and …

Mining Climate Data For Shire Level Wheat Yield Predictions In Western Australia, Yunous Vagh

Theses: Doctorates and Masters

Climate change and the reduction of available agricultural land are two of the most important factors that affect global food production especially in terms of wheat stores. An ever increasing world population places a huge demand on these resources. Consequently, there is a dire need to optimise food production.

Estimations of crop yield for the South West agricultural region of Western Australia have usually been based on statistical analyses by the Department of Agriculture and Food in Western Australia. Their estimations involve a system of crop planting recommendations and yield prediction tools based on crop variety trials. However, many crop …

Pattern Space Maintenance For Data Updates And Interactive Mining, Mengling Feng, Guozhu Dong, Jinyan Li, Yap-Peng Tan, Limsoon Wong

Kno.e.sis Publications

This article addresses the incremental and decremental maintenance of the frequent pattern space. We conduct an in-depth investigation on how the frequent pattern space evolves under both incremental and decremental updates. Based on the evolution analysis, a new data structure, Generator-Enumeration Tree (GE-tree), is developed to facilitate the maintenance of the frequent pattern space. With the concept of GE-tree, we propose two novel algorithms, Pattern Space Maintainer+ (PSM+) and Pattern Space Maintainer− (PSM−), for the incremental and decremental maintenance of frequent patterns. Experimental results demonstrate that the proposed algorithms, on average, outperform the representative state-of-the-art …

Protein Secondary Structure Prediction Using Parallelized Rule Induction From Coverings, Leong Lee, Cyriac Kandoth, Jennifer Leopold, Ronald L. Frank

Computer Science Faculty Research & Creative Works

Protein 3D structure prediction has always been an important research area in bioinformatics. In particular, the prediction of secondary structure has been a well-studied research topic. Despite the recent breakthrough of combining multiple sequence alignment information and artificial intelligence algorithms to predict protein secondary structure, the Q3 accuracy of various computational prediction algorithms rarely has exceeded 75%. In a previous paper [1], this research team presented a rule-based method called RT-RICO (Relaxed Threshold Rule Induction from Coverings) to predict protein secondary structure. The average Q3 accuracy on the sample datasets using RT-RICO was 80.3%, an improvement over comparable computational methods. …

Efficient Computation Of Iceberg Cubes By Bounding Aggregate Functions, Xiuzhen Zhang, Pauline Lienhua Chou, Guozhu Dong

Kno.e.sis Publications

The iceberg cubing problem is to compute the multidimensional group-by partitions that satisfy given aggregation constraints. Pruning unproductive computation for iceberg cubing when nonantimonotone constraints are present is a great challenge because the aggregate functions do not increase or decrease monotonically along the subset relationship between partitions. In this paper, we propose a novel bound prune cubing (BP-Cubing) approach for iceberg cubing with nonantimonotone aggregation constraints. Given a cube over n dimensions, an aggregate for any group-by partition can be computed from aggregates for the most specific n--dimensional partitions (MSPs). The largest and smallest aggregate values computed this way become …

Summarizing Data Sets For Classification, Christopher W. Kinzig, Krishnaprasad Thirunarayan, Gary B. Lamont, Robert E. Marmelstein

Kno.e.sis Publications

This paper describes our approach and experiences with implementing a data mining system using genetic algorithms in C++. In contrast with earlier classification algorithms that tended to “tile” the data sets using some pre-specified “shapes”, the proposed system is based on Marmelstein’s work on determining natural boundaries for class homogeneous regions. These boundaries are further refined to construct a compact set of simple data mining rules for classification.