17120587
Combination methods in microarray analysis
Microarray technology and experiment can produce thousands or tens of thousands of gene expression measurements in a single cellular mRNA sample. Selecting a list of informative differential genes from these measurement data has been the central problem for microarray analysis. Many methods to identify informative genes have been proposed in the past. However, due to the complexity of biological systems, each proposed method seems to perform nicely in a particular data set or specific experiment. It remains a great challenge to come up with a selection method for a wider spectrum of experiments and a broader variety of data sets. In this paper, we take the approach of method combination using data fusion and rank-score graph which have been used successfully in other application domains such as information retrieval, pattern recognition and tracking, and molecular similarity search. Our method combination is efficient and flexible and can be extended to become a general learning system for microarray gene expression analysis.
35256277
Generalized shuffle-exchange digraphs: Hamiltonian properties
The k-ary n-dimensional shuffle-exchange directed network S(k, n) consists of k/sup n/ nodes such that each node is represented as an k-ary n-tuple vector, a/sub 1/a/sub 2/...a/sub n/, where a/sub i/ is in [0, k-1]. Node a/sub 1/a/sub 2/...a/sub n/ is adjacent to node a/sub 2/a/sub 3/...a/sub n/a/sub 1/ (one shuffle link) and k-1 other nodes a/sub 1/a/sub 2/...a/sub n-1/b, where b/spl isin/[0, k-1] and b/spl ne/a/sub n/ (k-1 exchange links). S(k, n) have been widely used as topologies for VLSI networks, parallel architectures, and communication systems. However, since S(k, n) does not exist for the number of nodes between k/sup n/ and k/sup n+1/, Liu and Hsu have recently proposed a class of digraphs GS(k, N), which generalized S(k, n) to any number N of nodes. They also showed that GS(k, N) retains all the nice properties of S(k, n). In this paper, we survey these combinatorial properties and study Hamiltonian properties of GS(k, N). In particular, we have successfully obtained a Hamiltonian circuit for GS(k, k(k+1)) for any k>2.
267445
Biological Nomenclatures: A Source of Lexical Knowledge and Ambiguity
There has been increased work in developing automated systems that involve natural language processing (NLP) to recognize and extract genomic information from the literature. Recognition and identification of biological entities is a critical step in this process. NLP systems generally rely on nomenclatures and ontological specifications as resources for determining the names of the entities, assigning semantic categories that are consistent with the corresponding ontology, and assignment of identifiers that map to well-defined entities within a particular nomenclature. Although nomenclatures and ontologies are valuable for text processing systems, they were developed to aid researchers and are heterogeneous in structure and semantics. A uniform resource that is automatically generated from diverse resources, and that is designed for NLP purposes would be a useful tool for the field, and would further database interoperability. This paper presents work towards this goal. We have automatically created lexical resources from four model organism nomenclature systems (mouse, fly, worm, and yeast), and have studied performance of the resources within an existing NLP system, GENIES. Using nomenclatures is not straightforward because issues concerning ambiguity, synonymy, and name variations are quite challenging. In this paper we focus mainly on ambiguity. We determined that the number of ambiguous gene names within the individual nomenclatures, across the four nomenclatures, and with general English ranged from 0%-10.18%, 1.187%-20.30%, and 0%-2.49% respectively. When actually processing text, we found the rate of ambiguous occurrences (not counting ambiguities stemming from English words) to range from 2.4%-32.9% depending on the organisms considered.
23957657
Terminology model discovery using natural language processing and visualization techniques
Medical terminologies are important for unambiguous encoding and exchange of clinical information. The traditional manual method of developing terminology models is time-consuming and limited in the number of phrases that a human developer can examine. In this paper, we present an automated method for developing medical terminology models based on natural language processing (NLP) and information visualization techniques. Surgical pathology reports were selected as the testing corpus for developing a pathology procedure terminology model. The use of a general NLP processor for the medical domain, MedLEE, provides an automated method for acquiring semantic structures from a free text corpus and sheds light on a new high-throughput method of medical terminology model development. The use of an information visualization technique supports the summarization and visualization of the large quantity of semantic structures generated from medical documents. We believe that a general method based on NLP and information visualization will facilitate the modeling of medical terminologies.
30030780
Headwords and Suffixes in Biomedical Names
Natural Language Processing (NLP) techniques have been used for the task of extracting and mining knowledge from biomedical literature. One of the critical steps of such a task is biomedical named entity tagging (BNER) which usually contains two steps: the first step is the identification of biomedical names in text and the second is the assignment of semantic classes predefined to names identified by the first step. Headwords and suffixes have been used frequently by BNER systems as features for the assignment of semantic classes to names in text. However, there are few studies to evaluate the performance of headwords and suffixes in predicting semantic classes of biomedical terms utilizing knowledge sources in an unsupervised way. We conducted a study to evaluate the performance of headwords and suffixes using names in the Unified Medical Language System (UMLS) where the semantic classes associated with these names were obtained by modifying an existing UMLS semantic group system and incorporating the GENIA ontology. We define headwords and suffixes that are significantly associated with a specific semantic class as semantic suffixes. The performance of semantic assignment using semantic suffixes achieved an F-measure of 86.4% with a precision of 91.6% and a recall of 81.7%. When applying these semantic suffixes obtained using the UMLS to names extracted from the GENIA corpus, the system achieved an F-measure of 73.4% with a precision of 84.2% and a recall of 65.1% where these performance measures could be improved dramatically when limited to names associated with classes that have the corresponding GENIA types.
3172606
Gene name ambiguity of eukaryotic nomenclatures
MOTIVATION
With more and more scientific literature published online, the effective management and reuse of this knowledge has become problematic. Natural language processing (NLP) may be a potential solution by extracting, structuring and organizing biomedical information in online literature in a timely manner. One essential task is to recognize and identify genomic entities in text. 'Recognition' can be accomplished using pattern matching and machine learning. But for 'identification' these techniques are not adequate. In order to identify genomic entities, NLP needs a comprehensive resource that specifies and classifies genomic entities as they occur in text and that associates them with normalized terms and also unique identifiers so that the extracted entities are well defined. Online organism databases are an excellent resource to create such a lexical resource. However, gene name ambiguity is a serious problem because it affects the appropriate identification of gene entities. In this paper, we explore the extent of the problem and suggest ways to address it.


RESULTS
We obtained gene information from 21 organisms and quantified naming ambiguities within species, across species, with English words and with medical terms. When the case (of letters) was retained, official symbols displayed negligible intra-species ambiguity (0.02%) and modest ambiguities with general English words (0.57%) and medical terms (1.01%). In contrast, the across-species ambiguity was high (14.20%). The inclusion of gene synonyms increased intra-species ambiguity substantially and full names contributed greatly to gene-medical-term ambiguity. A comprehensive lexical resource that covers gene information for the 21 organisms was then created and used to identify gene names by using a straightforward string matching program to process 45,000 abstracts associated with the mouse model organism while ignoring case and gene names that were also English words. We found that 85.1% of correctly retrieved mouse genes were ambiguous with other gene names. When gene names that were also English words were included, 233% additional 'gene' instances were retrieved, most of which were false positives. We also found that authors prefer to use synonyms (74.7%) to official symbols (17.7%) or full names (7.6%) in their publications.


CONTACT
lifeng.chen@dbmi.columbia.edu
10940267
BioLog: A Browser Based Collaboration and Resource Navigation Assistant for BioMedical Researchers
We often realize that communicating with other colleagues who are studying similar topics helps to identify information relevant to our area of study, which otherwise may not have been found. We wish to accelerate acquisition of collective knowledge in a defined area by identifying specific spheres of inquiry. Such spheres correspond to groups of people who are experts in a field. In this paper we provide a systematic way to gain knowledge from their online search activity, and enable them to organize and share their search findings for further analysis. We have built a prototype system, BioLog, to help biomedical researchers share this implicit knowledge among their peers and store their access patterns into a central system for reuse. BioLog has been deployed it in two labs within TGen as a pilot study. The data has been gathered and analyzed by preliminary text-mining and collaborative filtering methods.
5322427
Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution
Feature selection, as a preprocessing step to machine learning, is effective in reducing dimensionality, removing irrelevant data, increasing learning accuracy, and improving result comprehensibility. However, the recent increase of dimensionality of data poses a severe challenge to many existing feature selection methods with respect to efficiency and effectiveness. In this work, we introduce a novel concept, predominant correlation, and propose a fast filter method which can identify relevant features as well as redundancy among relevant features without pairwise correlation analysis. The efficiency and effectiveness of our method is demonstrated through extensive comparisons with other methods using real-world data of high dimensionality
14550653
QoS Topology Control with Minimal Total Energy Cost in Ad Hoc Wireless Networks
This paper discusses the energy efficient QoS topology control problem in ad hoc wireless networks. Given a set of nodes in a plane, end-to-end traffic demands and delay bounds between node pairs, the problem is to find a network topology that can meet the QoS requirements and the total transmission power of nodes is minimized. We consider two cases of the problem: 1) the traffic demands are not splittable, and 2) the traffic demands are splittable. The first case is formulated as an integer linear programming problem. The latter case is formulated as a mixed integer linear programming problem. A greedy algorithm and an approximation algorithm with ratio n are proposed to solve the problem, where n is the number of nodes. Extensive simulations are conducted to evaluate the performance of proposed algorithms.
11656540
Searching for Interacting Features
Feature interaction presents a challenge to feature selection for classification. A feature by itself may have little correlation with the target concept, but when it is combined with some other features, they can be strongly correlated with the target concept. Unintentional removal of these features can result in poor classification performance. Handling feature interaction can be computationally intractable. Recognizing the presence of feature interaction, we propose to efficiently handle feature interaction to achieve efficient feature selection and present extensive experimental results of evaluation.
34878858
Dimensionality reduction of unsupervised data
Dimensionality reduction is an important problem for efficient handling of large databases. Many feature selection methods exist for supervised data having class information. Little work has been done for dimensionality reduction of unsupervised data in which class information is not available. Principal component analysis (PCA) is often used. However, PCA creates new features. It is difficult to obtain intuitive understanding of the data using the new features only. We are concerned with the problem of determining and choosing the important original features for unsupervised data. Our method is based on the observation that removing an irrelevant feature from the feature set may not change the underlying concept of the data, but not so otherwise. We propose an entropy measure for ranking features, and conduct extensive experiments to show that our method is able to find the important features. Also it compares well with a similar feature ranking method (Relief) that requires class information unlike our method.