Detecting Gene Relations From Medline Abstracts

Stephens, Matthew J.; Palakal, Mathew; Mukhopadhyay, Snehasis; Raje, Rajeev R.; Mostafa, Javed

doi:10.1142/9789814447362_0047

Cited by 71 publications

(72 citation statements)

References 2 publications

(1 reference statement)

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“…Each domain has its own special types of semantic relations. For example, Stephens et al (2001) provide a classification scheme for relationships between genes, including classes such as "NP 0 phosphorylates NP 1 ". However, it is plausible that a general-purpose scheme like Table 11 can capture the majority of semantic relations in general text at a reasonable level of granularity.…”

Section: Discussionmentioning

confidence: 99%

Corpus-based Learning of Analogies and Semantic Relations

2005

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Abstract. We present an algorithm for learning from unlabeled text, based on the Vector Space Model (VSM) of information retrieval, that can solve verbal analogy questions of the kind found in the SAT college entrance exam. A verbal analogy has the form A:B::C:D, meaning "A is to B as C is to D"; for example, mason:stone::carpenter:wood. SAT analogy questions provide a word pair, A:B, and the problem is to select the most analogous word pair, C:D, from a set of five choices. The VSM algorithm correctly answers 47% of a collection of 374 college-level analogy questions (random guessing would yield 20% correct; the average college-bound senior high school student answers about 57% correctly). We motivate this research by applying it to a difficult problem in natural language processing, determining semantic relations in noun-modifier pairs. The problem is to classify a noun-modifier pair, such as "laser printer", according to the semantic relation between the noun (printer) and the modifier (laser). We use a supervised nearest-neighbour algorithm that assigns a class to a given noun-modifier pair by finding the most analogous noun-modifier pair in the training data. With 30 classes of semantic relations, on a collection of 600 labeled noun-modifier pairs, the learning algorithm attains an F value of 26.5% (random guessing: 3.3%). With 5 classes of semantic relations, the F value is 43.2% (random: 20%). The performance is state-of-the-art for both verbal analogies and noun-modifier relations.

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Section: Discussionmentioning

confidence: 99%

Corpus-based Learning of Analogies and Semantic Relations

2005

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“…Four replicates were performed for each condition. All MEDLINE abstracts referred to in SGD's literature database were considered as acceptable, noise-free, domain-specific source of information for the yeast genes being considered (Stephens, Palakal et al 2001). A restricted vocabulary is suggested in several recent papers (Stephens, Palakal et al 2001;Chiang and Yu 2003;Glenisson, Antal et al 2003).…”

Section: Expression and Literature Data Setsmentioning

confidence: 99%

Clustering Genes Using Heterogeneous Data Sources

Zeng

Yang

et al. 2010

International Journal of Knowledge Discovery in Bioinformatics

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Clustering of gene expression data is a standard exploratory technique used to identify closely related genes. Many other sources of data are also likely to be of great assistance in the analysis of gene expression data. Such sources include proteinprotein interaction data, transcription factor and regulatory elements data, comparative genomics data, protein expression data and much more. These data provide us with a means to begin elucidating the large-scale modular organization of the cell. Conclusions drawn from more than one data source is likely to lead to new insights. Data sources may be complete or incomplete depending on whether or not they provide information about every gene in the genome. With a view toward a combined analysis of heterogeneous sources of data, we consider the challenging task of developing exploratory analytical techniques to deal with multiple complete and incomplete information sources. The Multi-Source Clustering (MSC) algorithm we developed performs clustering with multiple, but complete, sources of data. To deal with incomplete data sources, we have adopted the MPCK-means clustering algorithm, which is a constrained clustering algorithm, to perform exploratory analysis on one complete source (such as gene expression data) and other potentially incomplete sources provided in the form of constraints. We have shown that the MSC algorithm produces clusters that are biologically more meaningful when integrating gene expression data and text data than those identified using only one source of data. For the constrained clustering algorithm, we have studied the effectiveness of various constraints sets. To address the problem of automatically generating constraints from biological text literature, we considered two methods (cluster-based and similarity-based). The novelty of research presented here is the development of a new clustering algorithm MSC to perform exploratory analysis using two or more diverse but complete data sources, and study of effectiveness of constraints sets and robustness of the constrained clustering algorithm using multiple sources of incomplete biological data, and incorporating such incomplete data into constrained clustering algorithm in form of constraints sets.

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“…However, automated literature mining offers a yet untapped opportunity to induce and integrate many fragments of information gathered by researchers from multiple fields of expertise, into a complete picture exposing the interrelated relationships of various genes, proteins and chemical reactions in cells, and pathological, mental and intellective states in organisms. Many researches [1,2,3,4,5,6,7,8,9] have focused on the gene or protein name extraction, protein-protein interaction and gene-disease relationship extraction from biomedical literature (e.g. MEDLINE).…”

Section: Built By Informationmentioning

confidence: 99%

“…Since then, the extraction of terminological entities and relationships from biomedical literature is the main research efforts in biomedical literature mining. The research instances include words/phrases disambiguation [1], gene-gene relationships [1][8], protein-protein [3,4,5,7,9], and gene-protein interactions or specific relationships between molecular entities such as cellular localization of proteins, molecular binding relationships [18], and interactions between genes or proteins and drugs [19]. Bunescu et al [20] have a comparative study on the methods such as relational learning, Naïve Bayes, SVM etc.…”

Section: Related Work On Biomedical Literature Miningmentioning

confidence: 99%

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Text Mining for Finding Functional Community of Related Genes Using TCM Knowledge

Zhou

Liu³

et al. 2004

Lecture Notes in Computer Science

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Abstract. We present a novel text mining approach to uncover the functional gene relationships, maybe, temporal and spatial functional modular interaction networks, from MEDLINE in large scale. Other than the regular approaches, which only consider the reductionistic molecular biological knowledge in MEDLINE, we use TCM knowledge(e.g. Symptom Complex) and the 50,000 TCM bibliographic records to automatically congregate the related genes. A simple but efficient bootstrapping technique is used to extract the clinical disease names from TCM literature, and term co-occurrence is used to identify the disease-gene relationships in MEDLINE abstracts and titles. The underlying hypothesis is that the relevant genes of the same Symptom Complex will have some biological interactions. It is also a probing research to study the connection of TCM with modern biomedical and post-genomics studies by text mining. The preliminary results show that Symptom Complex gives a novel top-down view of functional genomics research, and it is a promising research field while connecting TCM with modern life science using text mining.

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Detecting Gene Relations From Medline Abstracts

Cited by 71 publications

References 2 publications

Corpus-based Learning of Analogies and Semantic Relations

Corpus-based Learning of Analogies and Semantic Relations

Clustering Genes Using Heterogeneous Data Sources

Text Mining for Finding Functional Community of Related Genes Using TCM Knowledge

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