A NEW LATENT SEMANTIC ANALYSIS BASED METHODOLOGY

FOR KNOWLEDGE EXTRACTION FROM BIOMEDICAL

LITERATURE AND BIOLOGICAL PATHWAYS DATABASES

F. Abate, A. Acquaviva, E. Ficarra and E. Macii

Politecnico di Torino, Turin, Italy

Keywords:

Bioinformatic, Latent semantic analysis, Text mining, Biological pathway.

Abstract:

Nowadays, a considerable amount of genetic and biomedical studies are mostly diffused on the Web and freely

available. This exciting capability, if from one side opens the way to new scenarios of cooperating research,

on the other side makes the knowledge retrieval and extraction an extremely time consuming operation. In this

context, the development of new tools and algorithms to automatically support the scientist activity to achieve

a reliable interpretation of the complex interactions among biological entities is mandatory.

In this paper we present a new methodology aimed at quantifying the biological degree of correlation among

biomedical terms present in literature. The proposed method overcomes the limitation of current tools based on

public literature information only, by exploiting the trustworthy information provided by biological pathways

databases. We demonstrate how to integrate trusted pathway information in a semantic correlation extraction

chain based on UMLS Metathesaurus and relying on PubMed as literature database. The effectiveness of

the obtained results remarks the importance of automatically quantifying the degree of correlation among

biomedical terms in order to helpfully support the scientist research activity.

1 INTRODUCTION

The pace of genetical and biological research has sur-

prisingly spread in the last few years. The devepe-

ment of new technologies (DNA/RNA next gener-

ation sequencing) and the improvement of the old

one (microarray for gene expression at lower costs)

have being supporting the activity of scientists and

researchers. Hundred of experimental results sup-

ported by the biotechnologic enhancement have high-

lighted the mechanisms behind the complex interac-

tions among biological entities as well as brought to

light new unknown phenomena. Nevertheless, if on

one side this rising technological development opens

the way to amazing scenarios in the biomedical re-

search, the problem of handling the great amount of

new information calls for the need of developing new

computational infrastructures. Speciﬁcally, the capa-

bility of extracting relevant knowledge from genetical

and biomedical studies has become one of the ma-

jor thrusts in bioinformatic research. Nowadays, the

main sources of information about the biomedical re-

search are mainly biomedical literature databases and

classiﬁcation systems such as ontologies and thesau-

rus. Most of these systems are distributed on the Web

and thus they are extremely easy to access and opened

to the entire scientiﬁc community. However, in this

scenario, the scientist have to face the twofold draw-

back of 1) retrieving the documents containing the in-

formation that he/she is looking for and 2) correlat-

ing the concepts in order to better extract the knowl-

edge they provide. Both the operation are extremely

time-consuming and the degree of correlation among

biological concepts strongly depends on the scien-

tist knowledge background. Therefore, an automated

tool for the computation of a quantiﬁed score between

biological terms according to literature information

is strongly necessary in order to support the analysis

of experimental results occurring during the research

activity and to reﬁne the design of the experiments

by focusing on the most relevant features. The cor-

relation score must be the result of a statistical co-

occurrences analysis of biological terms both in the

same article and in the related ones. Moreover, the

tool must be able to integrate as many as possible

sources of information, distributed on the Web and

collected in different formats and structures, in or-

der to guarantee the best accuracy and reliability for

Abate F., Acquaviva A., Ficarra E. and Macii E..

A NEW LATENT SEMANTIC ANALYSIS BASED METHODOLOGY FOR KNOWLEDGE EXTRACTION FROM BIOMEDICAL LITERATURE AND

BIOLOGICAL PATHWAYS DATABASES .

DOI: 10.5220/0003171400660074

In Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms (BIOINFORMATICS-2011), pages 66-74

ISBN: 978-989-8425-36-2

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

the computed biological correlation score. Section 2

overviews the state of the art where the proposed tool

is contextualized. Section 3 shows the methodology

used to both retrieve the biological pathway informa-

tion and extract the knowledge by the overall infor-

mation base. Section 4 demonstrates the effectiveness

of the proposed approach in computing the biological

correlation score among biomedical terms. In Section

5 the conclusions and the opening of the future work

are faced.

2 BACKGROUND

To improve the quality of the search over public lit-

erature databases, many search engines have been de-

veloped to classify and correlate the huge amount of

paper that a search may return. Most of this tools

improves the search engine capability adopting on-

tologies and thesaurus as information base. GoP-

ubMed (Doms, A. and Schroeder, M., 2005) is a

search engine tools that make the search on PubMed

(PubMed) repository categorized and ranked. In de-

tails, it applies text-mining algorithm in order to clas-

sify abstracts retrieved from PubMed according to

terms coming from GO and Medical Subject Head-

ings (MeSH) vocabulary (MeSH, 2005). A further

extension to GoPubMed is GoGene (Plake, C. et al.,

2009). The scope of this tool is to rank a list of

genes, occurring in a document set after a PubMed

search, according to terms of GO and MeSH. Af-

ter performing a document search by means of GoP-

ubMed, a text mining algorithm looks for gene names,

GO and MeSH terms over the collection of paper ab-

stracts. Finally, it returns a list of genes associated

with concepts of GO and MeSH. Even if the associ-

ation is performed computing a correlation score be-

tween a single gene and a term, the scope of the score

is not to express a measure of biologic relationship

between either two terms, or two gene, or a gene and

a term, but it is an a posteriori statistical analysis that

allows to map a gene into a ontology term. Further-

more, the problem of computing a quantiﬁed semantic

correlation score among biological concepts accord-

ing to the information coming from ontologies has

been addressed in (Wang, J. Z. et al., 2007). In this

work authors developed a tool for measuring the se-

mantic similarity of two Gene Ontology terms (The

Gene Ontology Consortium, 2000). The algorithm

exploits the graph-based hierarchical topology of GO

and it aggregates the semantic contribute of the an-

cestors providing a numeric value that reﬂects the dis-

tance among GO terms from the biological meaning

point of view. Nevertheless, this tools is limited in

foreclosing a set of biological topics related to the

genetical and biological ﬁeld and the base of infor-

mation is strongly limited to the GO vocabulary. For

instance, the medical term “Parkinson is not present

in GO, therefore the correlation between this disease

and a certain gene is uncomputable.

A different approach for providing a quantiﬁed

measure among biological terms, based on the Vector

Space Model theory is proposed in (Abate, F. et al.,

2010). In this work, the authors apply the Latent

Semantic Indexing (LSI) algorithm (Gliozzo, A. M.

and Strapparava, C., 2005) to a set of document ab-

stracts automatically retrieved through PubMed web

services. The LSI algorithm is strongly based on the

decomposition, by means of Singular Value Decom-

position (SVD) algorithm, of a term-by-document oc-

currences matrix X in three sub-matrix U, Σ and V

where U and V contain the left and right singular

vectors of X and the matrix Σ is a diagonal matrix

containing the singular values. The correlation score

about two terms in the X matrix is calculated by ap-

plying the cosine product of the vector of U-by-Σ ma-

trix corresponding to the correlating terms. Trans-

forming the X matrix into the U-by-Σ matrix allows

to consider not only the occurrences of terms but also

the co-occurrences, exploiting the conceptual links

among terms occurring in the different documents,

but belonging to the same context. The analysis per-

formed by the LSI algorithm is further coupled with a

pre-processing phase based on the Metamap program,

provided by NIH (Aronson, A. R., 2001). The algo-

rithm behind Metamap allows to extract biomedical

concepts from document text so that each abstract is

translated into a list of biomedical concepts, namely a

list of UMLS Concept Unique Identiﬁers (CUI) (Bo-

denreider, O., 2004). Moreover, the authors empha-

size that the resulting correlation scores, namely Se-

mantic Correlation Score, SRS allow the comparison

among biological concepts belonging to the same re-

trieved document abstract set but it looses accuracy

when comparing terms and concepts belonging to dif-

ferent experimental run and document set. Therefore,

the SRSs go out of the scope of calculating an uni-

versal correlation score as the validity of the compar-

ison is limited to a speciﬁc experimental run. In or-

der to overcome this limitation, authors analyze the

density function of the statistical distribution of the

correlation scores within a single experimental run

and divide the density function in percentiles. Con-

sequently, the evaluation through percentiles allow to

evaluate the correlation between genes and biological

concepts independently of the experimental run, be-

cause it provides an information on the frequency dis-

tribution of the SRSs in the overall CUI set occurring

A NEW LATENT SEMANTIC ANALYSIS BASED METHODOLOGY FOR KNOWLEDGE EXTRACTION FROM

BIOMEDICAL LITERATURE AND BIOLOGICAL PATHWAYS DATABASES

in an experimental run.

Nevertheless, the effectiveness of the methodol-

ogy introduced in (Abate, F. et al., 2010) strongly de-

pends on the reliance of the information in the re-

trieved abstract documents. Scientiﬁc papers, and

speciﬁcally those from PubMed as far as the biomedi-

cal ﬁeld is concerned, are certainly the most consulted

source of information by scientist and biologist be-

cause they contain the more updated state of the art in

science and medicine. However, the nature itself of

biomedical literature is characterized by a signiﬁcant

level of uncertainty. In fact, the scientiﬁc publica-

tions, reporting the state of the art on scientiﬁc topics,

are continually evolving and, as a consequence, the

addicted argumentations might be often contradict-

ing. Therefore, a biological correlation score com-

puted by considering documents coming exclusively

from the biomedical literature is affected by a certain

level of uncertainty. For this reason, integrating the

set of documents coming from literature with trusted

information is a mandatory step in order to enhance

the computational accuracy of the biological correla-

tion among biological concepts. In order to get the

scope, a twofold question must be answered: ﬁrstly,

what are the trusted source of informations and to

what extend a source of information can be consid-

ered as trusted; secondly, once the trusted informa-

tion have been collected, what is the more effective

way to integrate this information in the Latent Seman-

tic Analysis algorithmic infrastructure?

2.1 Trusted Information Sources

It is worth underlining that several sources of biomed-

ical information, mainly ontologies and thesaurus, are

universally accepted as trusted source of information.

GO, for instance, correlates gene and gene products

by means of annotations. GO curators coordinate

the development of the ontology rooted in the exper-

imental literature through an annotation-driven pro-

cess and, moreover, the accuracy of the relations and

terms deﬁnitions are continually checked (Hill, D. P.

et al., 2008). Moreover, genetic and metabolic path-

ways are a graph-based representation of the interac-

tion of genes and proteins in a biological process.

The annotations related to new discovered path-

ways are collected in several pathway databases.

These databases play a key role in the genetic and bio-

logic research ﬁeld. Examples of pathways databases

that have been developed in the last years are the Ky-

oto Encyclopedia of Genes and Genomes (KEGG)

(Kanehisa, M. and Goto, S., 1999), BioGRID (Stark,

C. et al., 2006), HumanCyc (Romero, P. et al., 2004).

On the base of pathways information, a number of

knowledge based tools have been developed. In par-

ticular, Pathway Commons is a projects that aims

at integrating all the distributed databases and the

pathway information under a common web interface

(Pathway Commons, 2007), providing a single ac-

cess point to multiple pathway data sources. Path-

way Commons runs on cPath software engine (Ce-

rami, E. G. et al., 2006), an open source framework

that makes the aggregation of custom pathway data

sets easy. The key feature of cPath is mainly the

ability to support standard pathways exchange format

as PSI-MI (Hermjakob, H. et al., 2004) and BioPax

(BioPAX, 2007), as well as providing all the informa-

tion content in XML format through web service API.

Moreover, cPath stores external link records matching

the biological entity with Gene Ontology terms if the

references are present in the PSI-MI ﬁle.

2.2 Integration of Trusted Information

in the LSI Algorithm

LSI is particularly effective for text clustering ap-

plications because it exploits co-occurrences among

terms in order to gather latent associations (Gliozzo,

A. M. and Strapparava, C., 2005). This feature makes

the LSI particularly successful in handling synonyms

and polysemys. However, LSI considers all the words

in the documents set as a bag of words, where all the

terms are equally weighted. In (Chakraborti, S. et al.,

Figure 1: Modiﬁed Occurrences Matrix with sprinkling la-

bel classes.

2006), authors introduce the “sprinkling”, an efﬁcient

technique that enhances text classiﬁcation accuracy

taking into account document class labels. Label-

ing the documents helps LSI promoting inferred la-

tent associations between words conceptually belong-

ing to the same class. Considering the LSI algorithm,

the documents labeling results in creating a new oc-

currences matrix where new terms are added. Fig-

BIOINFORMATICS 2011 - International Conference on Bioinformatics Models, Methods and Algorithms

ure 1 shows how sprinkling technique inﬂuences the

occurrence matrix content during the LSI algorithm

computation. The class-labelled terms corresponds

to extra rows with non-zero value only in the la-

belled documents, and all-zeros otherwise. The term

rows added by the sprinkling artiﬁcially create new

co-occurrences among terms belonging to the same

document class making explicit the implicit associa-

tions. Therefore, the efﬁciency in promoting implicit

associations makes the sprinkling process a good can-

didate to integrate trusted informationin the LSI algo-

rithm in order to promote implicit associations among

terms embedded in the document set. Starting from

LSI and the sprinkling idea we developed a new cus-

tomized methodology in order to integrate the asso-

ciations coming from the trusted biological pathway

with the information included in a set of documents

retrieved from PubMed web services.

3 METHODOLOGY

The complete ﬂow of the propose method is mainly

composed by three phases depicted in Figure 2:

Document Abstract Analysis, Semantic Analysis and

Trusted Info Integration, Results Presentation. The

following sub-sections describe the methodologies

behind each phases in more details.

Figure 2: Complete Semantic Analysis Flow.

3.1 The Document Abstract Analysis

Phase

The initial set of documents is composed by ab-

stract retrieved through PubMed web services. In

particular, we consider the case the scientist is in-

terest in quantify the biological correlation score

among a biological process and a speciﬁed gene.

The set of keyword used to query the PubMed web-

service is composed by the biological process and the

gene name (initial keyword set), and a list of syn-

onyms and terms correlated to both the gene and the

biological process (expanded keyword set), resulting

from querying the UMLS database(Bodenreider, O.,

2004). In detail, in order to get the more complete

set of synonyms and correlated terms, we exploit the

fact that ULMS database store the relations among

concepts belonging to almost 37 different biomedi-

cal database and ontologies (Bodenreider, O., 2004).

Hereafter, we select the relevant relations in order to

download the more related abstract set, exploiting the

methodology introduced in (Abate, F. et al., 2010). It

is worth noting that the more the retrieved abstracts

relate both the biological process and the gene, the

more the resulting biological score is accurate. In fact,

the presence of unrelated with the speciﬁed terms in-

creases the noising information that in turn alter the

result accuracy.

Moreover, in the Document Abstract Analysis

phase, the retrieved set of document abstracts from

PubMed written in Natural Languageare translated in

a Concept Language exploiting Metamap capability.

In fact, Metamap allows to map natural language text

in Concept Unique Identiﬁers (CUI). Pre-processing

the abstracts by means of Metamap makes the seman-

tic analysis more accurate in that it reduces the am-

biguities among terms that express the same concept

with different sentences and words, and it allows to

reduce non-relevant terms ﬁltered by Semantic Type

(Abate, F. et al., 2010). Therefore, at the end of

Document Abstract Analysis phase, the set of abstract

documents from PubMed translated in Concept Lan-

guage is returned forming the set of documents on

whom the semantic analysis is performed.

3.2 The Semantic Analysis and Trusted

Info Integration Phase

This phase is the main core of the proposed ﬂow and

it consists of 1) retrieving the Trusted Information, 2)

applying the sprinkling technique in order to integrate

the information from the document abstract, 3) per-

forming the latent semantic analysis in order to get

the most accurate SRS list .

3.2.1 Trusted Information

The proposed tool considers genetical pathways as

trusted source information. In fact, genetical path-

ways are a graphical-based representation of the gene

involved in a speciﬁc biological pathways. Thus, they

provide a direct link between a gene and the corre-

sponding biological process and in this sense they are

good candidate as additional source of information

A NEW LATENT SEMANTIC ANALYSIS BASED METHODOLOGY FOR KNOWLEDGE EXTRACTION FROM

BIOMEDICAL LITERATURE AND BIOLOGICAL PATHWAYS DATABASES

Figure 3: GetPathwayInformationByBiologicalProcess pro-

cedure to get trusted information.

to the biomedical abstracts set. Moreover, genetical

pathways are constantly subjected to a review process

and consequently they can be considered as trusted.

In order to automatically access genetical pathways

information, a software client for querying Pathway

Commons web-service has been developed. In

details, pathways information are extracted through

the GetPathwayInformationByBiologicalProcess

procedure shown in Figure 3.

GetPathwayInformationByBiologicalProcess re-

turns from Pathway Commons the list of pathways

where the biological process speciﬁed as input occurs

in the pathway description or in the labeling title.

Hereafter, for each pathway in the list, the cpath id

(i.e. the unique pathway identiﬁer according to cPath

framework (Cerami, E. G. et al., 2006), is extracted

from the pathway description and it is used to access

the complete set of genes involved in the pathway.

It is worth noting that the presented method

achieves a twofold scope: ﬁrstly, the link between

the speciﬁed gene and biological process is con-

ﬁrmed consulting a trusted source of information;

secondly, a list of gene trustly involved in the spec-

iﬁed biological process are collected. The latter in-

formation is particularly tailored in enhancing the co-

occurrences of genes correlated with the speciﬁed

gene and biological process, increasing the overall ac-

curacy during the semantic analysis.

3.2.2 Applying Sprinkling to Semantic Analysis

In this phase, the information extracted from pathway

database are integrated in the semantic analysis pro-

cess of PubMed abstracts. The term-by-document oc-

currence matrix is the main data structure processed

by the LSI algorithm and, consequently, it is the

main access point to integrate additional information.

The term-by-document occurrence matrix is a two-

dimensional occurrence matrix in which the columns

represent the dimensional space of a corpus of docu-

ments and the rows represent the dimensionalspace of

Figure 4: Occurrences Matrix adding pathway informa-

tion. The dashed line separate the abstract document set

by document pathways.

the terms occurring into the documents. However, the

abstract set and the retrieved pathways present a dif-

ferent structure of the informational content. The ab-

stract set is composed by documents already suitable

for the latent semantic analysis algorithm, whereas

the pathways are represented as a connected graph.

Therefore, in order to integrate pathways content in

the semantic analysis ﬂow, the information coming

from pathways are ﬁrstly extracted and translated into

a new document containing the trusted information.

In detail, the created trusted document contains the

list of gene names involved in the retrieved path-

ways as well as the list of retrieved biological path-

ways. Nevertheless, adding a single document to

the overall document set weakly affects the seman-

tic analysis in terms of accuracy because poorly rel-

evant from the statistical point of view. Thus, the

trusted document, containing the extracted pathway

information, is replicated. We refer at this procedure

as document padding. Figure 4 shows a trivial term-

by-document occurrences matrix sprinkled with addi-

tional document padding. The number of instances of

the padded document, namely NPD, is obtained ac-

cording the following equation:

NPD = ⌈p· NAD⌉ (1)

where NAD is the total number of PubMed abstract

document and p is a padding factor spanning from

0 to 1. Consistently with this equation, the num-

ber of padded document spans from zero to the same

number of document abstracts. Padding the overall

document set with trusted information affects the sin-

gular values of the occurrence matrix. In fact, the sin-

gular values reﬂect on the distribution of the prevalent

concepts occurring in the document set. The higher

the singular value score, the greater is the presence

BIOINFORMATICS 2011 - International Conference on Bioinformatics Models, Methods and Algorithms

of the concept in the overall document corpus. It is

worth noting that increasing the p factor makes the

trusted concepts included in the trusted documents

predominant with respect to the overall concepts.

Figure 5: Singular values obtained launching the latent se-

mantic analysis for computing the biological correlation

score between AKT1 gene and Angiogenesis biological

process. The ordinate reports the singular value score and

the abscissa represents the index in the diagonal singular

value matrix.

The analysis of the singular values is fundamental

to evaluate how the trusted document padding affects

on the overall document corpus by coherently tuning

the p factor value. In the LSI algorithm, the singular

values, obtained after applying the SVD, represent the

attendance of the concepts in the analyzed documents

set. The more a concept is relevant and frequent in

the document set the higher is the singular value cor-

responding to it. Moreover,during the latent semantic

analysis the singular values computed by SVD are set

in the diagonal Σ matrix in ascending order, and con-

sequently the ﬁrst singular values represent the more

relevant concepts in the analyzed corpus. Figure 5

plots the ﬁrst ten singular values resulting from the

analysis of abstract document set for the computa-

tion of the correlation score among the Angiogenesis

biological process and AKT1 gene term. The singular

values are obtained with ﬁve different p factor values.

When the document padding is increased by tuning

the p factor, the concepts in the trusted documents be-

come more relevant respect to the concepts in the ab-

stract document set. When the p factor increases even

more the concepts in the trusted document set become

so relevant that the concepts in the abstract document

set are overcome. As a consequence, the singular

values corresponding to the trusted document set are

boosted up and they occupy the ﬁrst positions in the

diagonal Σ matrix. This is the reason why, as shown

in Figure 5, the greater the p factor value, the higher

the ﬁrst singular value score. Therefore, with greater

p factor values the most relevant concepts in the over-

all document set correspond mainly to the concepts in

the trusted document set.

The alteration on the singular values of the occur-

rence matrix by the trusted document padding con-

sequently reﬂects on the correlation score between

the biological terms. Consider the scenario depicted

in Figure 4: TermA and TermB present a weak cor-

relation in the document abstract set because quite

linearly independent, whereas they are strongly cor-

related in the trusted document set. The document

padding increases the parallelism of the two terms

vector, consequently enhancing the correlation score.

Furthermore, both TermA and TermB are correlated

with TermC creating an indirect correlation due to co-

occurrence. If the TermC occurs even in the trusted

document set the resulting correlation score between

TermA and TermB is further enhanced. Moreover,

when a term occurs both in the document abstract

set and in the trusted documents set, the term con-

ceptually belongs to the trusted concepts as well. If

two biological terms belong to the trusted concepts

their correlation score is higher because conceptu-

ally linked to the predominant concepts in the over-

all documents content. Speciﬁcally, the biological

correlation score between the biological process and

the gene term is inﬂuenced by trusted information

by a twofold effect: in the case the terms corre-

sponding to biological process and gene term occur

both in the document abstract set and in the trusted

documents the score is directly affected; moreover,

the correlation score is further enhanced by the pres-

ence of terms correlated with both the biological pro-

cess and gene terms because occurring both in the

document abstract set and in the trusted document

set. Table 1 shows the correlation scores between

Angiogenesis and AKT1 gene, both in SRSs and in

Percentile. The reported results show that increasing

the p factor value implies a corresponding increase in

the correlation score. Therefore, it is possible to assert

that the proposed methodology, provides an effective

correlation score combining the information extracted

in the document abstract set with the trusted informa-

tion extracted from pathways databases. Moreover,

the inﬂuence of the trusted information on the seman-

tic text analysis is tuned by the p factor parameter that

directly affects the correlation results.

Table 1: SRS and Percentile values corresponding to p fac-

tor.

p Factor 0.1 0.2 0.3 0.4 0.5

SRS 15.7 17 18.1 18.8 19.5

Percentile 76 78 82 83 84

A NEW LATENT SEMANTIC ANALYSIS BASED METHODOLOGY FOR KNOWLEDGE EXTRACTION FROM

BIOMEDICAL LITERATURE AND BIOLOGICAL PATHWAYS DATABASES

3.3 The Results Presentation Phase

An evaluation criterion allowing the comparison

among each gene in the genes set and each biological

process is fundamental in order to focus the atten-

tion only on those genescorrelated with the biological

processes of interest behind a certain degree of cor-

relation. Therefore, the correlation measurement

between genes and biological processes must ex-

press an universally valid score allowing the compar-

ison among different correlation analysis. Actually,

SRS provides a percentage measurement to compare

genes and biological process corresponding to terms

belonging to the same occurrence matrix and thus

it quantiﬁes the semantic relationship of biological

terms during the same semantic text analysis (i.e.

a single execution of the tool). Different analyses

are characterized by different sets of documents and

concepts. Consequently, SRSs values computed on

different occurrences matrices are not directly compa-

rable. In order to deﬁne an evaluation criterion that al-

lows to compare SRSs resulting from differentseman-

tic analysis, and therefore different occurrences ma-

trices, we analyzed the SRS distribution on different

occurrences matrix. The SRS Distribution (SRSD) is

deﬁned as the density function that reports the per-

centage of occurrences, or frequencies, of a certain

score for the possible SRSs corresponding to all the

CUIs occurring within the correlation analysis. Fur-

thermore, the SRSD has been divided into hundred

percentile intervals of equivalent area depending on

the frequency percentage of the SRS in the density

function. We deﬁne each interval as Biological Cor-

relation Index (BCI) expressing that the higher the

BCI, the greater the degree of biological correlation

between the biological process and the genes. The

evaluation through BCI allows to evaluate the corre-

lation between genes and biological process indepen-

dently of the experimental run, because it provides an

information on the frequency distribution of the SRSs

in the overall terms occurring in an experimental run.

4 EXPERIMENTAL RESULTS

The following section reports the correlation

scores among a set of three biological processes

(Angiogenesis, Apoptosis, and Signal Transduction)

and ten gene terms (VEGFA, ANGPT2, ANGPT1,

AKT1, BCL-xL, BCL-2, P53, PTEN, MAPK1,

CCND1) applying the proposed ﬂow. Figure 6 re-

sumes the results and demonstrate the effectiveness of

integrating the information coming from the PubMed

document abstract set with the information from

pathway database. The biological correlation scores

obtained integrating trusted information (Trusted

INFO) are compared with the scores obtained without

integrating any trusted information (Normal INFO).

Moreover, p factor has been set to 0.5. This choice

implies that the trusted document set has been padded

in order to equal almost the half of the number of

abstract document set and to emphasize the effect of

the integration of trusted information on the overall

document set. Furthermore, the biological correlation

score is reported in the BCI format in order to allow

the comparison among the scores of same set of genes

computed in different biological process (Abate, F.

et al., 2010).

(a) Angiogenesis.

(b) Apoptosis.

Figure 6: Experimental Results Expressed in BCI score.

In order to have a better understanding of the re-

sulting biological correlation scores, the functional

deﬁnition of the gene set according to NCI Thesaurus

is listed below:

• VEGFA: This gene is involved in the regulation of

blood vessel growth.

BIOINFORMATICS 2011 - International Conference on Bioinformatics Models, Methods and Algorithms

• ANGPT2: This gene encodes angiopoietin-2

protein and it plays a role in both angiogenesis

and neovascularization.

• ANGPT1: This gene plays a role in both vascular

development and angiogenesis.

• AKT1: This gene is involved in signal transduc-

tion and negative regulation of apoptosis.

• BCL-xL: This protein plays a role in the modula-

tion of apoptosis.

• BCL2: This gene is involved in apoptotic regu-

lation. Overexpression of this gene promotes the

pathogenesis of B-Cell lymphomas, due to anti-

apoptotic activity.

• P53: This protein plays a role in the regulation of

both the cell cycle and apoptosis.

• PTEN: This gene plays a role in signal transduc-

tion and apoptosis. It is also involved in the regu-

lation of cell cycle progression.

• MAPK1: This gene plays a role in signal trans-

duction and positive regulation of the cell cycle.

• CCND1: This gene plays a role in the regulation

of mitotic events.

From the same NCI Thesaurus source,

Angiogenesis is deﬁned as the “blood vessel

formation” and, speciﬁcally for the case of tumor

angiogenesis, it is “the growth of blood vessels from

surrounding tissue to a solid tumor”. It is worth

noting that the results concerning the biological

correlation scores between angiogenesis and the gene

set coherently reﬂect the NCI Thesaurus deﬁnitions.

In fact, VEGFA, ANGPT1 and ANGPT2 present the

almost same correlation scores with the Angiogenesis

biological process both in the case of Trusted INFO

and in the case of Normal INFO. The small difference

in the results depends on the fact that in both the cases

the biological correlation scores presents highest

BCI, therefore the Trusted INFO poorly affect the

accuracy of the computation. Moreover, the corre-

lation score of AKT1 gene term with Angiogenesis

process is highly enhanced by the integration of

Trusted INFO. This gene is speciﬁcally involved in

the Signal Transduction and in the Apoptosis process,

whereas it is less typical of Angiogenesis biological

process respect to VEGFA, ANGPT1, and ANGPT2

genes even if correlated with it. Our results reﬂects

this assertion because AKT1 gene directly appears in

the Angiogenesis pathway and it is directly correlated

with this biological process according to the trusted

information set, but its correlation according to the

information coming from the document abstract

set is minor compared with VEGFA, ANGPT1, and

ANGPT2 genes. Coherently, this gene presents a

lower biological correlation score compared with

VEGFA, ANGPT1, and ANGPT2 genes even if,

correctly, its score has been signiﬁcantly boosted by

adding Trusted INFO.

Moreover, the integration of Trusted INFO en-

hances the biological correlation score of P53, PTEN,

MAPK1 and CCND1. These genes are generally in-

volved in Apoptosis, Signal Transduction and Cell

Cycle biological processes that are indirectly related

with Angiogenesis process as well. The AKT1 gene,

that is mainly involved in Signal Transduction and

Apoptosis, also occurs in the Angiogenesis pathway.

The biological correlation of the AKT1 gene with

P53, PTEN, MAPK1 and CCND1 genes and, on the

other side, the occurrence of this gene in the trusted

document concerning Angiogenesis process creates

an indirect link between P53, PTEN, MAPK1 and

CCND1 genes and the same Angiogenesis process. It

accordingly results in a biological correlation score

enhancement.

Furthermore, the introduction of Trusted infor-

mation positively increases ANGPT1, ANGPT2 and

MAPK1 genes scores. These genes are generally typi-

cal of Angiogenesis and Signal Transduction but these

processes are sometime related to Apoptosis and thus

they co-occur in the document abstact set. There-

fore, even if the Apoptosis pathway do not include this

gene set, the indirect biological correlation between

terms in the Trusted information and terms occurring

in the document abstract set, belonging to the Apop-

tosis semantic area, increases the resulting value of

the biological correlation score. However, all of them

present a score signiﬁcatively lower than the one of

Apoptosis genes AKT1, BCL-xL, BCL2, and P53.

According to the results concerning Signal Trans-

duction process, the biological correlation score of

MAPK1 gene is particularly interesting. This gene

is strongly related to Signal Transduction, but this

biological processes is quite generic and it interact

with many biological phenomena typical of the cell

cycle. Considering only the document abstract set

as information base, the occurrence of MAPK1 gene

is not always frequently guaranteed, thus resulting

in a low correlation score. However, integrating the

Trusted information the score is almost triplicated.

The introduction of Trusted information creates the

missing conceptual links of the MAPK1 gene with

terms semantically correlated with the biological pro-

cess of interest, resulting in an increase of the ﬁnal

biological correlation score. This result remarks the

effectiveness of the proposed approach in enhancing

accuracy of measuring the biological degree of corre-

lation.

A NEW LATENT SEMANTIC ANALYSIS BASED METHODOLOGY FOR KNOWLEDGE EXTRACTION FROM

BIOMEDICAL LITERATURE AND BIOLOGICAL PATHWAYS DATABASES

Furthermore, integrating Trusted information in the

document abstract set makes the results more accurate

also reducing the biological correlation score. BCL2

gene, for instance, is functionally speciﬁc of Apopto-

sis biological process. On the wave of this assertion,

this gene further decreases the correlation score in the

case of both Angiogenesis and Signal Transduction

when applying Trusted information. The expected be-

havior would be at most of unaltered result because

the Trusted information do not addict any further in-

formation concerning the correlation score between

BCL2 and the two biological processes. However,

integrating Trusted information increases the corre-

lation score of those terms related to the biological

processes and their correlation scores overcomes the

BCL2 one, that in turn becomes lower. Similar con-

sideration are also valid about the biological correla-

tion score between BCL-xL gene and ANGPT2 gene

and Signal Transduction biological process.

5 CONCLUSIONS

The proposed semantic analysis tool provides a

framework for measuring the biological correlation

score among biomedical terms. The score is the

results of a text mining analysis performed on the

abstracts of the most relevant scientiﬁc publications

with the support of the completeness of the UMLS

Metathesaurus. The knowledgeextracted by biomedi-

cal literature is further integrated with the information

coming from sources generally known as trustworthy.

In order to satisfy this requirement, public biological

pathways databases have been chosen and the embed-

ded information are ﬁrst retrieved and then correlated

in the overall semantic analysis ﬂow. Moreover, in

order to integrate the knowledge coming from an het-

erogeneous base of information, a new version of the

latent semantic analysis, based on the sprinkling tech-

nique, has been developed. The results remark the

efﬁciency of the proposed approach in enhancing the

accuracy of the biological correlation score computa-

tion by combining the knowledge extracted by scien-

tiﬁc document abstract set with the pathways infor-

mation.

As future step the complete automation of the tool

will allow to retrieve the document abstract set in an

automated and accurate manner. Moreover, the in-

tegration of information coming from other trusted

sources will be tailored and developed in order to bet-

ter improve the overall accuracy and robustness of the

proposed semantic analysis method.

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