INTELLIGENT SYSTEM FOR MEDLINE RECORDSELECTION ENHANCED WITH A LEARNING SYSTEM FOR
CHARACTERISTICS OF ABSTRACT TEXTS AND A
KEYWORD RECOMMENDATION SYSTEMMIYAKO TANAKA

Department of Business Administration, Ube National College of Technology,
2-14-1, Tokiwadai, Ube 755-8555 JapanSANAE NAKAZONO, HIROSHI MATSUNO
Faculty of Science, Yamaguchi University,
1677-1 Yoshida, Yamaguchi 753-8512, JapanHIDEKI TSUJIMOTO, YASUHIKO KITAMURA

Faculty of Engineering, Osaka City University,
3-3-138 Sugimoto, Sumiyoshiku, Osaka 558-8585, JapanSATORU MIYANOHuman Genome Center, Insititute of Medical Science, University of Tokyo,
4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan

We have implemented a system for assisting experts in selecting MEDLINE records
for database construction purposes. This system has twospeci c features: The rst
is a learning mechanism which extracts characteristics in the abstracts of MEDLINE records of interest as patterns. These patterns re ect selection decisions by
experts and are used for screening the records. The second is a keyword recommendation system whichassists and supplementsexperts' knowledge in unexpectedcases. Combined with a conventional keyword-based information retrieval system,
this system may provide an ecient and comfortable environment for MEDLINE
record selection by experts.

1

Introduction

Information retrieval from MEDLINE is a daily activity for individual researchers.

A set of keywords together with their combinations may specify

each individual research topic and this information plays an important role
as a key knowledge to ecient and high quality information retrieval fromMEDLINE.
On the other hand, people working for constructing new databases of speci c biological interests are facing with a di erent situation. There are somecases that selection by keywords and their combinations does not achieve efcient and high quality results. It is sometimes hard to identify appropriate
information for their speci c selection interests.
1For example, one of the authors has been involved in constructing a database1of disordered proteins . In that project, identi cation of a set of keywords andtheir combinations which may capture most of the PDB entries containing
disordered proteins has exhausted a number of very precise text searches andanalyses of the full PDB databases and thorough readings of related articles.
In that project, we have given up to use MEDLINE data fornding candidate

disordered proteins since the \disorderedness" has recently received attention

2;3 and MEDLINE records do not contain explicit descriptions about disor-deredness. This may be an extreme case, but there might be a possibility tomake use of MEDLINE abstracts for this purpose.

Another example is thecase of collecting all MEDLINE records which contain useful information for

4organizing the gene regulatory network of S. cervisiae . This rather ambiguous selection condition assumes experts to read the MEDLINE documents forselection. Even with speci c keywords such as \Saccharomyces cerevisiae AND
regulatory", the experts have to cope with more than 2,800 records in MEDLINE where the total number of MEDLINE records containing \Saccharomyces
cerevisiae" is more than 45,000. High quality and high coverage selection isinevitable in order to reduce the cost for thorough reading of the full articles
after selection. Thus it is required for experts to read the fewest possible candidates MEDLINE documents covering almost all records of interest with thehelp of information retrieval systems in an intelligent way.
For such purpose, any informatics tools which can assist experts eciently
in a comfortable way would be appreciated. This paper describes a system wehave developed for assisting experts in selecting articles, especially for database
construction purposes. The computational learning method

4 we have devel-

oped and tested is implemented in this system together with GUIs.

This

method learns the characteristics in abstracts of MEDLINE as patterns anduses the learnt patterns to select candidate MEDLINE records. This method
has a theoretical foundation for guaranteeing the performance, and the exper-

iment using the articles in Cell about S. cerevisiae showed that 90% correct
records were selected by reading the abstracts of 50% records with the assistance of this learning method. Selection by keywords is, of course, implemented
in this system as a well-established strategy for selection. But in order to copewith the case that appropriate keywords may not come out from experts dueto the nature of selection topic, this system equips a system which recommends
keywords for better selection which is implemented based on the system Key-

5

word Recommendation System .
Section 2 describes a computational learning method and experimental
results supporting this method. In Section 3, GUIs are described for realizing
the learning method in our system. Section 4
2

rstly discusses the di erencebetween natural language processing techniques and the technique proposedin the paper. Secondly, we brie y explain the method to collect MEDLINE
records automatically. Section 5 concludes this paper with further problems.

2

Literature Selection Method by Machine Learning TechniqueThis section brie y explains the method

4 for selecting the records of experts'

interest from MEDLINE. Our strategy is to express the knowledge of experts by
a procedure called a rough reading function and to classify the set of abstracts
converted by the rough reading function with the help of the machine learning

6

system BONSAI . For convenience of explanation, we describe a method forthe purpose of selecting MEDLINE records which contain useful informationfor organizing the gene regulatory network of S. cervisiae.

2.1 Overview of Strategy
Basic Operation
A rough reading function is de ned by a mapping of words in texts to asmall number of the speci ed characters. Table 1 shows an example for theabove purpose of selecting MEDLINE records where words in the abstracts areconverted to the speci ed characters A, x, y, z and o whose implicit meanings
are as follows:
A: Gene Name,

x: Very relevant,z: Weakly relevant

y: Relevant,o: Not relevant.

Note that these characters, each of which corresponds to a word in abstracts,
will be determined by experts according to their interests and knowledges.
The notion of rough reading is based on an observation that when experts
read the abstract of an article for such classi cation, they

rst read the abstractroughly and then, if it is considered as a candidate, they read the abstract
carefully. Thus, the experts' knowledge is re ected onto the converted texts
through the rough reading process.
For the sequences of characters A, x, y, z, and o converted from MEDLINE
abstracts through the rough reading process, we employ the machine learning
system BONSAI and apply it for discovering rules as the indexing and thedecision tree which may reduce the work of experts in selecting the records ofinterest.

Procedure for collecting almost all records of interest by iterating Basic Oper-

ations3

Table 1: Abstract conversion by rough reading function.
CYC7-H3 is a cis-dominant regulatory mutation that causes a 20-foldoverproduction of yeast iso-2-cytochrome c. The CYC7-H3 mutation isan approximately 5 kb deletion with one breakpoint located in the 5' noncoding region of the CYC7 gene, approximately 200 base from the ATG
initiation codon. The deletion apparently fuses a new regulatory region
to the structural portion of the CYC7 locus. The CYC7-H3 deletion encompasses the RAD23 locus, which controls UV sensitivity and the ANP1
locus, which controls osmotic sensitivity. The gene cluster CYC7-RAD23ANP1 displays striking similarity to the gene cluster CYC1-OSM1-RAD7,
which controls, respectively, iso-1-cytochrome c, osmotic sensitivity andUV sensitivity.
conversion AooxooooooooooooAooooooyoooooooooooAooooooooooyoooooooooooo
AzoAyooAzoozyooAzoooyoooAoooooooAooooooyozy
abstract

Figure 1 shows the procedure for selecting almost all MEDLINE records
of interest.

This procedure contains the Basic Operation above.

Initially,

experts need to de ne the set A to be examined by selecting MEDLINE records

according to keywords such as Saccharomyces cerevisiae.

In the following,

we present the procedure which iterates the Basic Operation until almost all
relevant records are collected.
1. Choose the set of records S according to the interests of experts and let

the rest of records be the set K . Experts read abstracts in the set S ,classifying the set S to the set of records P OS of interest and the set ofother records N EG.

2. By applying Basic Operation (rough reading function and BONSAI) to

the set S , the indexing and the decision tree are obtained as a rule forclassifying the rest of abstracts. Also the set Converted K is obtained byapplying only the rough reading function to the set K .

3. The set Converted K is classi ed into the sets POS candi and NEG candi
by applying the rule obtained in 2.
4. Experts terminate the procedure, if they decide that the set POS candi
might have no records which they want. It not, experts iterate the steps
4

Figure 1: Procedure for selecting MEDLINE records of interest with the help of BONSAI.
2 and 3 above by substituting the set S for the set POS candi and substituting the set K for the set NEG candi.

2.2 Experimental results4
In order to evaluate the performance of the strategy, we prepared 758 MED-

LINE records from Journal Cell. Experts classi ed them into two sets, namely,
the set of 210 records of interest and the set of 548 records not of interest, by
careful reading of the records. Then, by using these two classi ed sets as oracle, we made experiments to evaluate the performance of the strategy. The
set of these 758 abstracts corresponds to the set A in Figure 1.
set A, we chose randomly

From theve pairs of sets A, B, C, D, and E each of which

consists of 20 abstracts of interest P OS and 50 abstracts not of interest N EG.
We iterated Basic Operations seven times for each of theseve pairs of sets.

Figure 2 shows how the ratio of abstracts of interest grows with the number of
iterations onve pairs of sets AE. We should note that Figure 2 shows thatthe method enables us to select nearly 90% of abstracts of interest while leaving half amount of abstracts unread. A mathematical performance evaluation4

in a simpler framework is also given .
5

100
A
B
C
D
E

positive example ratio(%)

90
80
70
60
50
40

1

2

read record

positive record

A
B
C
D
E

49.5%
40.4%
53.8%
70.7%
49.2%

87.6%
89.5%
91.9%
93.3%
86.7%

3

4

5

6

7

number of iteration

Figure 2: 90% of abstracts of interest can be obtained leaving half amount unread.4
3

Design of Tools and Graphical User Interfaces

3.1 Tools for de ning rough reading function and classifying abstracts
From the discussions in Section 2, experts need to do the following two works;(i) de ne the rough reading function, that is, choose and rank words according
to the experts' knowledge of the importance of the words, (ii) classify abstracts
to positive ones (ones of interest) and negative ones (not ones of interest).
Figure 3 shows a snapshot of a tool for choosing and ranking words in theset of abstracts A in Figure 1. The system chooses automatically the words

from the set A which has the following features: the number of occurrences of
the word is high and the word has a tendency to occur in positive abstracts.
See

4 for the formal description of the property. The words listed in Figure 3 are

the words chosen by the system as important words. All words are classi ed
to three ranks of importance, high, middle, and low in terms of the level of theproperty stated above, but details are omitted here. Experts can check thesesuggested words and change the importance of the words or delete the words
from the list only by clicking the button, if necessary. Note that, since theranks of importance are recalculated at the each stage of suggestions, some ofthem would be changed by the system. Thus, the previous rank of importanceof the words is displayed in the list as is seen in Figure 3. Of course, experts
can input any words of their intentions to the tool.

This tool also has aconvenient function of picking up abstracts containing the intended word for6

helping experts to check the signi cance of the word as is seen in the right partof Figure 3.
Experts classify MEDLINE records into three sets, YES, NO, UNKNOWN
according to their decision. Figure 4 shows a snapshot of a tool for that job.
The bodies of all abstracts do not always need to be displayed, since experts
can decide whether the abstract is needed or not only by seeing the title inmany cases.

The window describing the body of abstract will be displayedon the screen by pushing the button \?" in Figure 4. The indicator at thelower part of the tool indicates the numbers of records, these are decided for
YES or NO, suggested for YES candidate or NO candidate. This indicator canencourage experts by showing the current status in handling the records.
After accomplishing above two processes corresponding to Figures 3 and 4,
BONSAI starts the job in order to pick up the next YES candidate abstracts.

3.2 Keyword recommendation system helping experts to classify abstracts
Recall that POS

candi in Figure 1 becomes the set S which is the set of

abstracts to be read by experts in the next step.

However, the number of

abstracts sometimes reaches hundreds. We may well prefer to read them inorder of our interest rather than to do in a random order.

To reduce thenumber of MEDLINE abstracts to read, we can use the following clues:

Keywords We can specify keywords, which are to appear in the abstract, inthe title, or in the MH term, to reduce the records to be the ones which
are related to a more speci c

eld or topic.

Author We can specify names of authors who published papers to reduce therecords to be the ones that are related to a researcher or a research group.

Journal We can specify titles of journals to reduce the records to be the ones
that are more credible.

Publication Year We can specify the year of publication to reduce the records
to be the ones that are the latest.
The key word recommendation system initiatively proposes a set of terms
according to the above categories to reduce the number of records. The snapshot of the tool is shown in Figure 5. This is useful because (1) we sometimes
cannot think of proper keywords to specify aeld or topic, (2) it is meaninglessto specify an author or journal name which are not included in the original
documents, (3) it is dicult to specify a journal name in an abbreviated form
which is used in MEDLINE (for example, Eur J Biochem). By using the tool,7

Figure 3: A tool for choosing and ranking words in the selected abstracts.

Figure 4: A tool for classifying abstracts into YES, NO, or UNKNOWN.
8

Figure 5: Keyword Recommendation system for MEDLINE.
we can reduce a large number of records of the set S in Figure 1 to be a small
set which is readable at once.
Here we show the mechanism of the tool that proposes a set of terms. As
we mentioned, it can proposes a set of terms categorized according to keyword,
author, journal, or publication year, but the process is common. Hence, as an
example, we show how the tool process the terms in the category of keyword.
At the initial stage, the tool performs the following preprocesses:
1. It extracts theelds of title, MH term, and abstract from MEDLINE

records, and decomposes theelds into a set of keywords. It removes

meaningless keywords such as \a," \of," \the," and so on.
2. It produces two indices; K-D index and D-K index. K-D index contains
pairs of a keyword and a list of record ID's. To an input of a keyword,
it returns a set of records that the keyword hits. Conversely, D-K index
contains pairs of a record ID and a list of keywords. To an input of arecord ID, it returns a set of keywords that the record contains.
At the stage of keyword recommendation, an initial set of records is pro-vided by the set S in Figure 1.

The tool sums up the keywords that are
9contained in the initial set by using the D-K index and shows them in orderof the number of appearance. When a keyword is chosen, the tool recalculates
the set of records that the keyword hits by using the K-D index, and repeats
the above process again.

7

Tools described in this section are available on the site .

4

Discussion

Applying Natural Language Processing technique is surely the most e ective
method of extracting useful information from the biological literature databasesuch as MEDLINE. In fact, many investigations on this direction are made in
the literature

8;9;10 .

Our approach is di erent. We believe that it is enough to provide a whole
abstract to biologists of their interest, since they can identify the desired sentence precisely from the abstract by themselves without large e ort. It leads
us to that we can suppose the unit of processing is not a sentence but a whole
abstract. Note that the technique proposed in the paper

4 is based on this as-

sumption. That is, a whole abstract is converted to the sequence of charactersin accordance with the knowledge of biologists, and the converted characters
are processed at the same time for obtaining the useful information to biologists.
This paper concretely demonstrates the next step of the paper

4 by devel-

oping the tools for selecting MEDLINE records. In addition, in order to copewith the diculties of reading the large amount of abstract at once, we also
combine the keyword recommendation system with the tool which enables biologists to read abstracts in the order of their interest. The function of the tool
is inherently needed, since, in many cases of our supposing situation, biologists
must identify the abstracts without any ideas of the keywords.
In our system, we need to collect a large number of MEDLINE abstracts inadvance for analyzing their abstracts by using our machine learning techniqueand for preprocessing keywords to be recommended by the keyword recommendation system. To assist them, we are utilizing the MetaCommander

12 . The

MetaCommander is a generic software robot that automatically collects datafrom WWW information sources dispersed on the Internet by interpreting ascript. It is compatible with the CGI, so, for example, it can submit a querywith some parameters to the PubMed and can collect MEDLINE records thatare returned as the answer.

Hence, the MetaCommander can make an an-

noying task for collecting a large number of related records easier. Moreover,it can selectively collect the latest records only, so we may provide a service
where we recommend a MEDLINE abstract to read as soon as it is submitted
10

to the MEDLINE database.

5

Conclusion

We developed an intelligent system for selecting MEDLINE records aiming at
the situation that keywords and their combinations do not promise ecientand high quality results.
The

The system consists of the following two systems.

rst is a tool which chooses records of interest with the help of machinelearning technique.

The eciency of the tool had been guaranteed by the4experiments in the paper . The second is a tool which recommends a set ofterms categorized according to keyword, author, journal, or publication year.
We can reduce a large number of records suggested by the above tool to be asmall set which is readable by experts at once. Both tools have the capability
for experts to interact dynamically with the GUIs.
Of course, we are recognizing that it is indispensable for us to verify theability of the tool by the experiment in real works of biologists. Our subsequente orts need to be paid to make the tool open to the public. For the realization,
we are planning the experiment and considering the evaluation method of thecurrently available tool with the help of biologists.
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