GPDminer: a tool for extracting named entities and analyzing relations in biological literature.

PURPOSE: The expansion of research across various disciplines has led to a substantial increase in published papers and journals, highlighting the necessity for reliable text mining platforms for database construction and knowledge acquisition. This abstract introduces GPDMiner(Gene, Protein, and Disease Miner), a platform designed for the biomedical domain, addressing the challenges posed by the growing volume of academic papers. METHODS: GPDMiner is a text mining platform that utilizes advanced information retrieval techniques. It operates by searching PubMed for specific queries, extracting and analyzing information relevant to the biomedical field. This system is designed to discern and illustrate relationships between biomedical entities obtained from automated information extraction. RESULTS: The implementation of GPDMiner demonstrates its efficacy in navigating the extensive corpus of biomedical literature. It efficiently retrieves, extracts, and analyzes information, highlighting significant connections between genes, proteins, and diseases. The platform also allows users to save their analytical outcomes in various formats, including Excel and images. CONCLUSION: GPDMiner offers a notable additional functionality among the array of text mining tools available for the biomedical field. This tool presents an effective solution for researchers to navigate and extract relevant information from the vast unstructured texts found in biomedical literature, thereby providing distinctive capabilities that set it apart from existing methodologies. Its application is expected to greatly benefit researchers in this domain, enhancing their capacity for knowledge discovery and data management.

Prediction of Poststroke Depression Based on the Outcomes of Machine Learning Algorithms.

Poststroke depression (PSD) is a major psychiatric disorder that develops after stroke; however, whether PSD treatment improves cognitive and functional impairments is not clearly understood. We reviewed data from 31 subjects with PSD and 34 age-matched controls without PSD; all subjects underwent neurological, cognitive, and functional assessments, including the National Institutes of Health Stroke Scale (NIHSS), the Korean version of the Mini-Mental Status Examination (K-MMSE), computerized neurocognitive test (CNT), the Korean version of the Modified Barthel Index (K-MBI), and functional independence measure (FIM) at admission to the rehabilitation unit in the subacute stage following stroke and 4 weeks after initial assessments. Machine learning methods, such as support vector machine, k-nearest neighbors, random forest, voting ensemble models, and statistical analysis using logistic regression were performed. PSD was successfully predicted using a support vector machine with a radial basis function kernel function (area under curve (AUC) = 0.711, accuracy = 0.700). PSD prognoses could be predicted using a support vector machine linear algorithm (AUC = 0.830, accuracy = 0.771). The statistical method did not have a better AUC than that of machine learning algorithms. We concluded that the occurrence and prognosis of PSD in stroke patients can be predicted effectively based on patients' cognitive and functional statuses using machine learning algorithms.

EPITRANS: a database that integrates epigenome and transcriptome data.

Epigenetic modifications affect gene expression and thereby govern a wide range of biological processes such as differentiation, development and tumorigenesis. Recent initiatives to define genome-wide DNA methylation and histone modification profiles by microarray and sequencing methods have led to the construction of databases. These databases are repositories for international epigenetic consortiums or provide mining results from PubMed, but do not integrate the epigenetic information with gene expression changes. In order to overcome this limitation, we constructed EPITRANS, a novel database that visualizes the relationships between gene expression and epigenetic modifications. EPITRANS uses combined analysis of epigenetic modification and gene expression to search for cell function-related epigenetic and transcriptomic alterations (Freely available on the web at http://epitrans.org ).

ISO/IEEE 11073 PHD message generation toolkit to standardize healthcare device.

As senior population increases, various healthcare devices and services are developed such as fall detection device, home hypertension management service, and etc. However, to vitalize healthcare devices and services market, standardization for interoperability between device and service must precede. To achieve the standardization goal, the IEEE 11073 Personal Health Device (PHD) group has been standardized many healthcare devices, but until now there are few devices compatible with the PHD standard. One of main reasons is that it isn't easy for device manufactures to implement standard communication module by analyzing standard documents of over 600 pages. In this paper, we propose a standard message generation toolkit to easily standardize existing non-standard healthcare devices. The proposed toolkit generates standard PHD messages using inputted device information, and the generated messages are adapted to the device with the standard state machine file. For the experiments, we develop a reference H/W, and test the proposed toolkit with three healthcare devices: blood pressure, weighting scale, and glucose meter. The proposed toolkit has an advantage that even if the user doesn't know the standard in detail, the user can easily standardize the non-standard healthcare devices.

Home healthcare settop-box for senior chronic care using ISO/IEEE 11073 PHD standard.

As the number of seniors with chronic disease increases, the need of home healthcare settop-box is increased to manage their chronic disease in their home environment. Using the home healthcare settop-box, the patients can regularly check their health data, and finally, it can lead the decrease of medical expenses. For the home healthcare settop-box, the most important factor is the standard compatibility, which can interoperate with standard devices of any other companies. In this paper, we propose a home healthcare settop-box using ISO/IEEE 11073 PHD standard. It collects health data according to the PHD standard, and provides a chronic-care service based on the collected data. The proposed settop-box is connected with 3 devices of weigh scale, blood pressure monitor, and glucose meter, and tested at 10 homes for a month. Lastly, the proposed settop-box can be used for various healthcare services such as Google Health and Telemedicine Services using a healthcare platform server.

Real-time elderly activity monitoring system based on a tri-axial accelerometer.

PURPOSE: The purpose of this study is to develop an automatic human movement classification system for the elderly using single waist-mounted tri-axial accelerometer. METHODS: Real-time movement classification algorithm was developed using a hierarchical binary tree, which can classify activities of daily living into four general states: (1) resting state such as sitting, lying, and standing; (2) locomotion state such as walking and running; (3) emergency state such as fall and (4) transition state such as sit to stand, stand to sit, stand to lie, lie to stand, sit to lie, and lie to sit. To evaluate the proposed algorithm, experiments were performed on five healthy young subjects with several activities, such as falls, walking, running, etc. RESULTS: The results of experiment showed that successful detection rate of the system for all activities were about 96%. To evaluate long-term monitoring, 3 h experiment in home environment was performed on one healthy subject and 98% of the movement was successfully classified. CONCLUSIONS: The results of experiment showed a possible use of this system which can monitor and classify the activities of daily living. For further improvement of the system, it is necessary to include more detailed classification algorithm to distinguish several daily activities.

Design of a context-aware model to enhance medication adherence.

Medication adherence is important to patients who suffer from chronic disease. Regular medication activity reduces the cost of caring disease and prohibits the worsening of disease condition. To support patients taking medicine correctly, we developed a medication assistance system which alarms medication situation through multimedia messages and help patients to take a medicine. To enable the system copes with various situations related to a medication service, we designed a medication context model and implemented a state based context aware application. We also applied our system to patients and saw a little improvement in medication adherence.

Dynamic activity classification based on automatic adaptation of postural orientation.

We propose a dynamic activity classification system with tri-axial accelerometer sensor using adaptation of user's postural orientation. In general, the sensor module is worn at a fixed position such as waist, head, wrist, thigh, and so on. However, in reality, the tilt of the attached sensor could be changed from time to time in actions such as sitting down, standing up, lying, walking or running. Moreover, most of the users want to wear the sensor at their own favorite positions instead of a recommended position. In these cases, the activity detection methods based on fixed tilt value may produce serious problem in their performance. Therefore, we propose a user adapted activity classification method which enables users to freely wear the sensor everywhere on their torso. In order to decide tilt values corresponding user's postural orientation, we focused on tilt-free activities such as walking and running. While walking, the algorithm tries to modify the predefined reference tilt values for the three axes, X, Y and Z. From an experiment, we have achieved 88% of the activity classification accuracy even though the tilt angle is changed while wearing sensors.

ProteinVista: a fast molecular visualization system using Microsoft Direct3D.

Many tools have been developed to visualize protein and molecular structures. Most high quality protein visualization tools use the OpenGL graphics library as a 3D graphics system. Currently, the performance of recent 3D graphics hardware has rapidly improved. Recent high-performance 3D graphics hardware support Microsoft Direct3D graphics library more than OpenGL and have become very popular in personal computers (PCs). In this paper, a molecular visualization system termed ProteinVista is proposed. ProteinVista is well-designed visualization system using the Microsoft Direct3D graphics library. It provides various visualization styles such as the wireframe, stick, ball and stick, space fill, ribbon, and surface model styles, in addition to display options for 3D visualization. As ProteinVista is optimized for recent 3D graphics hardware platforms and because it uses a geometry instancing technique, its rendering speed is 2.7 times faster compared to other visualization tools.

Predicting the interactome of Xanthomonas oryzae pathovar oryzae for target selection and DB service.

BACKGROUND: Protein-protein interactions (PPIs) play key roles in various cellular functions. In addition, some critical inter-species interactions such as host-pathogen interactions and pathogenicity occur through PPIs. Phytopathogenic bacteria infect hosts through attachment to host tissue, enzyme secretion, exopolysaccharides production, toxins release, iron acquisition, and effector proteins secretion. Many such mechanisms involve some kind of protein-protein interaction in hosts. Our first aim was to predict the whole protein interaction pairs (interactome) of Xanthomonas oryzae pathovar oryzae (Xoo) that is an important pathogenic bacterium that causes bacterial blight (BB) in rice. We developed a detection protocol to find possibly interacting proteins in its host using whole genome PPI prediction algorithms. The second aim was to build a DB server and a bioinformatic procedure for finding target proteins in Xoo for developing pesticides that block host-pathogen protein interactions within critical biochemical pathways. DESCRIPTION: A PPI network in Xoo proteome was predicted by bioinformatics algorithms: PSIMAP, PEIMAP, and iPfam. We present the resultant species specific interaction network and host-pathogen interaction, XooNET. It is a comprehensive predicted initial PPI data for Xoo. XooNET can be used by experimentalists to pick up protein targets for blocking pathological interactions. XooNET uses most of the major types of PPI algorithms. They are: 1) Protein Structural Interactome MAP (PSIMAP), a method using structural domain of SCOP, 2) Protein Experimental Interactome MAP (PEIMAP), a common method using public resources of experimental protein interaction information such as HPRD, BIND, DIP, MINT, IntAct, and BioGrid, and 3) Domain-domain interactions, a method using Pfam domains such as iPfam. Additionally, XooNET provides information on network properties of the Xoo interactome. CONCLUSION: XooNET is an open and free public database server for protein interaction information for Xoo. It contains 4,538 proteins and 26,932 possible interactions consisting of 18,503 (PSIMAP), 3,118 (PEIMAP), and 8,938 (iPfam) pairs. In addition, XooNET provides 3,407 possible interaction pairs between two sets of proteins; 141 Xoo proteins that are predicted as membrane proteins and rice proteomes. The resultant interacting partners of a query protein can be easily retrieved by users as well as the interaction networks in graphical web interfaces. XooNET is freely available from http://bioportal.kobic.kr/XooNET/.

Distance correction system for localization based on linear regression and smoothing in ambient intelligence display.

This paper suggests the method of correcting distance between an ambient intelligence display and a user based on linear regression and smoothing method, by which distance information of a user who approaches to the display can he accurately output even in an unanticipated condition using a passive infrared VIR) sensor and an ultrasonic device. The developed system consists of an ambient intelligence display and an ultrasonic transmitter, and a sensor gateway. Each module communicates with each other through RF (Radio frequency) communication. The ambient intelligence display includes an ultrasonic receiver and a PIR sensor for motion detection. In particular, this system selects and processes algorithms such as smoothing or linear regression for current input data processing dynamically through judgment process that is determined using the previous reliable data stored in a queue. In addition, we implemented GUI software with JAVA for real time location tracking and an ambient intelligence display.

Proactive medication assistances based on spatiotemporal context awareness of aged persons.

In this paper, we developed a system that could assist appropriate activities for medication adherence of the elderly. It employs a proactive knowledge which is represented as templates predefined for their medication activities. The knowledge-based assistance depends on the contexts considerably, which the system can recognize by continuously monitoring the current position and the time-schedule for their medications. The monitoring is performed with ultrasonic sensors and infrared sensors mounted in a display and a pillbox mainly. According to the recognized contexts, the medication activities can he serviced through old person-friendly multimedia display. In special, since the knowledge is well-defined by XML, only its content modification can provide a variety of services individually customized to the elderly.

Daily activity recognition system for the elderly using pressure sensors.

As transformed to aging society rapidly, the number of old persons who live alone is drastically increased. Because these old people may have disorder of bodily function, and be suffering from geriatric disease, needs of a health assistance system to make them healthier are strongly increased. In this paper, we propose a daily activity recognition system for an old person using pressure sensors. The target daily activities are MEAL, SLEEP, EXCRETION, GO-OUT, and REST. The proposed system installs pressure sensors to furniture and floors in home, and recognizes daily activities based on the object usage information. By using the proposed system, we can provide a warning sign for unhealthy cases such as skipping meals. And, the families who live in remote place can check that their parent takes a healthy daily living.

Toward real time detection of the basic living activity in home using a wearable sensor and smart home sensors.

As the elderly people living alone are enormously increasing recently, we need the system inferring activities of daily living (ADL) for maintaining healthy life and recognizing emergency. The system should be constructed with sensors, which are used to associate with people's living while remaining as non intrusive views as possible. To do this, the proposed system use a triaxial accelerometer sensor and environment sensors indicating contact with subject in home. Particularly, in order to robustly infer ADLs, we present component ADL, which is decided with conjunction of human motion together, not just only contacted object identification. It is an important component in inferring ADL. In special, component ADL decision firstly refines misclassified initial activities, which improves the accuracy of recognizing ADL. Preliminary experiments results for proposed system provides overall recognition rate of over 97% over 8 component ADLs, which can be effectively applicable to recognize the final ADLs.

An effective method for component activity classification supporting location awareness and user identification.

We present an effective method for component activity classification supporting location awareness and user identification at the same time. The system is comprised of three modules: Pressure Sensing Module (PSM), Activity Detecting Module (ADM), and Receiving Station (RS). The ADM having a unique id is a wearable module putting on one's waist-belt, which classifies component activity such as sitting chair, lying bed, sitting sofa, etc. utilizing both user's interaction with household furniture and atomic activities like lie, sit, and stand. We limit transmission range of RF chip in PSM to around 1 m so that we can find the most adjacent furniture to the ADM. It makes possible to find the user's relative location to the PSM, so we can aware of both who and where the acting person is while recognizing his/her activities. We obtained 92.5% of average precision of the activity classification.

Normalization of gene/protein names in biological literatures using Vector-Space Model.

As the number of biological literatures grows exponentially, needs for text mining system are increased. In text mining area, normalization is mapping gene/protein names to a database. It is necessary to combine extracted information from various literatures and to create a database or an ontology using literatures. Previous normalization researches used direct comparison methods between a database and literatures, but it is weak to extremely variational gene/protein names in literatures. Therefore, in this paper, we propose a normalization method using Vector-Space Model. For each gene/protein name, we rank identifiers using Vector-Space Model, and find the most similar identifier with the name. Experimental result shows the proposed method has 70.7% f-measure.

A Hub-protein based visualization of large protein-protein interaction networks.

According as the protein-protein interaction (PPI) data more increase, we need to optimally visualize them as network, in that describing the relationship among proteins is able to easily analyze biological processes happened in a cell. In this paper, to fast layout large-scale PPI networks, we proposed a method taking hub-proteins into consideration, which have more interactions than any other proteins in a network. In other words, it enforces two core parts of Walshaw's multilevel force-directed placement algorithm (MLFDP) to be modified. The modification is achieved by coarsening and expanding all neighboring proteins of hub-protein just once, whereas only two proteins in Walshaw's method. Our experiments show that the quality of layout is better optimal and time cost is reduced up to 63% in comparison with other methods.

Semi-automated clustering of gene expression data sets.

Clustering, as one of key analysis tools for gene expression data sets, attempts to discover groups of genes having similar expression patterns. In order to get a reasonable biological interpretation, it is desirable that a clustering result be accurate enough. However, conventional clustering methods do not always meet this demand since they require the exact tuning of input parameters and cluster centers for an acceptable quality of result. Through an intuitive user interaction, UI-Cluster solves the problem mentioned above, and yields better clustering results.

Finding the evidence for protein-protein interactions from PubMed abstracts.

MOTIVATION: Protein-protein interactions play critical roles in biological processes, and many biologists try to find or to predict crucial information concerning these interactions. Before verifying interactions in biological laboratory work, validating them from previous research is necessary. Although many efforts have been made to create databases that store verified information in a structured form, much interaction information still remains as unstructured text. As the amount of new publications has increased rapidly, a large amount of research has sought to extract interactions from the text automatically. However, there remain various difficulties associated with the process of applying automatically generated results into manually annotated databases. For interactions that are not found in manually stored databases, researchers attempt to search for abstracts or full papers. RESULTS: As a result of a search for two proteins, PubMed frequently returns hundreds of abstracts. In this paper, a method is introduced that validates protein-protein interactions from PubMed abstracts. A query is generated from two given proteins automatically and abstracts are then collected from PubMed. Following this, target proteins and their synonyms are recognized and their interaction information is extracted from the collection. It was found that 67.37% of the interactions from DIP-PPI corpus were found from the PubMed abstracts and 87.37% of interactions were found from the given full texts. AVAILABILITY: Contact authors.

BioProber: software system for biomedical relation discovery from PubMed.

The numbers of articles and journals that are published are increasing at a considerable rate, and the published information is growing continuously and fast. Because of this, researches to acquire knowledge automatically have been carried out in the areas of information retrieval, information extraction and text mining. Information retrieval approaches are good for specific topics that the number of related articles is small. But, if the number is bigger, searching skill and knowledge acquisition ability are useless. Though many efforts have been made to extract information from literature, many approaches have concentrated on specific entities, such as proteins, genes and their interactions, and much information is still remained in unstructured text. So, we have developed a system that discovers relations between various categories of biomedical entities. Our system collects abstracts from PubMed by queries representing a topic and visualizes relationship from the collection by automatic information extraction.