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
208013251
Modeling the Effect of Size on Defect Proneness for Open-Source Software
Quality is becoming increasingly important with the continuous adoption of open-source software. Previous research has found that there is generally a positive relationship between module size and defect proneness. Therefore, in open-source software development, it is important to monitor module size and understand its impact on defect proneness. However, traditional approaches to quality modeling, which measure specific system snapshots and obtain future defect counts, are not well suited because open-source modules usually evolve and their size changes over time. In this study, we used Cox proportional hazards modeling with recurrent events to study the effect of class size on defect-proneness in the Mozilla product. We found that the effect of size was significant, and we quantified this effect on defect proneness.
6684027
iProLINK: an integrated protein resource for literature mining
The exponential growth of large-scale molecular sequence data and of the PubMed scientific literature has prompted active research in biological literature mining and information extraction to facilitate genome/proteome annotation and improve the quality of biological databases. Motivated by the promise of text mining methodologies, but at the same time, the lack of adequate curated data for training and benchmarking, the Protein Information Resource (PIR) has developed a resource for protein literature mining--iProLINK (integrated Protein Literature INformation and Knowledge). As PIR focuses its effort on the curation of the UniProt protein sequence database, the goal of iProLINK is to provide curated data sources that can be utilized for text mining research in the areas of bibliography mapping, annotation extraction, protein named entity recognition, and protein ontology development. The data sources for bibliography mapping and annotation extraction include mapped citations (PubMed ID to protein entry and feature line mapping) and annotation-tagged literature corpora. The latter includes several hundred abstracts and full-text articles tagged with experimentally validated post-translational modifications (PTMs) annotated in the PIR protein sequence database. The data sources for entity recognition and ontology development include a protein name dictionary, word token dictionaries, protein name-tagged literature corpora along with tagging guidelines, as well as a protein ontology based on PIRSF protein family names. iProLINK is freely accessible at http://pir.georgetown.edu/iprolink, with hypertext links for all downloadable files.
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.
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.
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.
16166828
Maximal lifetime scheduling for K to 1 sensor-target surveillance networks
This paper addresses the maximal lifetime scheduling for k to 1 sensor-target surveillance networks. Given a set of sensors and targets in Euclidean plane, a sensor can watch only one target at a time and a target should be watched by k sensors (k ≥ 2) at anytime. Our task is to schedule sensors to watch targets, such that the lifetime of the surveillance system is maximized, where the lifetime is the duration that all targets are watched. We propose an optimal solution to find the target watching schedule for sensors that achieves the maximal lifetime. This is the first time in the literature that this scheduling problem of sensor surveillance systems has been formulated and the optimal solution has been found. Our solution consists of three steps: (1) computing the maximal lifetime of the surveillance system and a workload matrix by using linear programming techniques; (2) decomposing the workload matrix into a sequence of schedule matrices by extending the Hall's theory, to achieve the maximal lifetime; (3) obtaining a target watching timetable for each sensor based on the schedule matrices. The time complexity of our optimal method is O(m2n3), where m, n are the number of targets and the number of sensors, respectively. We illustrate our optimal method by a numeric example and experiments in the end.
10722883
Use of extreme patient samples for outcome prediction from gene expression data
MOTIVATION
Patient outcome prediction using microarray technologies is an important application in bioinformatics. Based on patients' genotypic microarray data, predictions are made to estimate patients' survival time and their risk of tumor metastasis or recurrence. So, accurate prediction can potentially help to provide better treatment for patients.


RESULTS
We present a new computational method for patient outcome prediction. In the training phase of this method, we make use of two types of extreme patient samples: short-term survivors who got an unfavorable outcome within a short period and long-term survivors who were maintaining a favorable outcome after a long follow-up time. These extreme training samples yield a clear platform for us to identify relevant genes whose expression is closely related to the outcome. The selected extreme samples and the relevant genes are then integrated by a support vector machine to build a prediction model, by which each validation sample is assigned a risk score that falls into one of the special pre-defined risk groups. We apply this method to several public datasets. In most cases, patients in high and low risk groups stratified by our method have clearly distinguishable outcome status as seen in their Kaplan-Meier curves. We also show that the idea of selecting only extreme patient samples for training is effective for improving the prediction accuracy when different gene selection methods are used.
14433108
Using amino acid patterns to accurately predict translation initiation sites
The translation initiation site (TIS) prediction problem is about how to correctly identify TIS in mRNA, cDNA, or other types of genomic sequences. High prediction accuracy can be helpful in a better understanding of protein coding from nucleotide sequences. This is an important step in genomic analysis to determine protein coding from nucleotide sequences. In this paper, we present an in silico method to predict translation initiation sites in vertebrate cDNA or mRNA sequences. This method consists of three sequential steps as follows. In the first step, candidate features are generated using k-gram amino acid patterns. In the second step, a small number of top-ranked features are selected by an entropy-based algorithm. In the third step, a classification model is built to recognize true TISs by applying support vector machines or ensembles of decision trees to the selected features. We have tested our method on several independent data sets, including two public ones and our own extracted sequences. The experimental results achieved are better than those reported previously using the same data sets. Our high accuracy not only demonstrates the feasibility of our method, but also indicates that there might be "amino acid" patterns around TIS in cDNA and mRNA sequences.
11465537
Visualizing the affective structure of a text document
This paper introduces an approach for graphically visualizing the affective structure of a text document. A document is first affectively analyzed using a unique textual affect sensing engine, which leverages commonsense knowledge to classify text more reliably and comprehensively than can be achieved with keyword spotting methods alone. Using this engine, sentences are annotated using six basic Ekman emotions. Colors used to represent each of these emotions are sequenced into a color bar, which represents the progression of affect through a text document. Smoothing techniques allow the user to vary the granularity of the affective structure being displayed on the color bar. The bar is hyperlinked in a way such that it can be used to easily navigate the document. A user evaluation demonstrates that the proposed method for visualizing and navigating a document's affective structure facilitates a user's within-document information foraging activity.
653966
Fan-Type Theorem for Long Cycles Containing a Specified Edge
In this paper, we prove that if G is 3-connected noncomplete graph of order n satisfying min{max{d(u),d(v)}:d(u,v)=2}=μ, then for each edge e, G has a cycle containing e of length at least min{n,2μ}, unless G is a spanning subgraph of Kμ + Kcn−μ or K3+(lKμ−2∪Ks), where n=l(μ−2)+s+3,1≤s≤μ−2.