BioSystems 57 2000 23 – 36
Data differentiation and parameter analysis of a chronic hepatitis B database with an artificial neuromolecular system
Jong-Chen Chen
Department of Management Information Systems, National YunLin Uni6ersity of Science and Technology,
123
Uni6ersity Road, TouLiu
640
, Taiwan, ROC Received 1 December 1999; received in revised form 4 April 2000; accepted 14 April 2000
Abstract
This paper describes the application of a biologically motivated system to the diagnosis of chronic hepatitis B. The system integrates intra- and inter-neuronal information processing so as to capture the biology-like gradual
transformability of structurefunction relationships. The system was applied to a clinical hepatitis B database, divided into two sets. The first set comprised 676 records, of which one half were chronic hepatitis B patients, and the other
half healthy individuals. The second set included 375 records, of which one third were chronic hepatitis B patients; another third were hepatitis B carriers, and the remaining third healthy non-carriers. Each record consisted of ten
examination items. Experimental results showed that the system was able to correctly differentiate 99.3 and 91.2 of the records in the first and the second sets, respectively. Differentiation means making a distinction between different
categories of data in each set. After substantial learning with the first set, the system was then tested with the second set, and it was able to correctly differentiate 95.7 of the records, suggesting a high differentiating capability in this
system. This system demonstrated an effective self-organizing capability in determining significant and insignificant examination items from patterns of the clinical data. It also showed that some combinations of these items were more
effective for determining whether one is infected with chronic hepatitis B than others. © 2000 Elsevier Science Ireland Ltd. All rights reserved.
Keywords
:
Hepatitis B; Data differentiation; Self-organizing learning; Gradual transformability www.elsevier.comlocatebiosystems
1. Introduction
Hepatitis means that the liver has inflamma- tion, which might be caused by viruses, toxic
agents, medications, chemicals, or alcohol. The viruses that cause hepatitis are further divided
into five types: A, B, C, D, and E. Hepatitis caused by the B type virus is called hepatitis B
and is one of the most serious health problems in the world. In Taiwan, statistical reports show that
15 of people are hepatitis B carriers, and that liver cancer has been one of the ten most deadly
diseases we note that patients having hepatitis B might develop liver cancer.
Tel.: + 886-5-5342601; fax: + 886-5-5312077. 0303-264700 - see front matter © 2000 Elsevier Science Ireland Ltd. All rights reserved.
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Currently, most research on hepatitis B is done from the perspectives of clinical practice, molecu-
lar biology, physiology, immunology, biotechnol- ogy, and health education. In this paper, a
biologically inspired computer model that inte- grates learning, memory, and internal dynamics
is used for the diagnosis of a chronic hepatitis B database. The system that we have developed,
called
the artificial
neuromolecular system
ANM, is a vertical information processing ar- chitecture that consists of two complementary
types of neurons: reference neurons Conrad, 1974a, 1976a, 1977; Conrad et al., 1989 and
cytoskeletal
neurons or
enzymatic neurons
Conrad, 1974a,b, 1976b; Conrad et al., 1989. The reference neuron scheme is basically a Heb-
bian model in which the connection between two neurons is strengthened when they are simulta-
neously active. This model also has hierarchical control features. The reference neurons play the
role of assembling cytoskeletal neurons into groups for specific tasks. Cytoskeletal neurons
are capable of performing spatiotemporal input – output pattern transduction. The information
processing capabilities of cytoskeletal neurons are motivated by physiological evidence that the in-
traneuronal mechanisms of a neuron control its firing behavior cf. Liberman et al., 1982a, 1985;
Hameroff, 1987. Rapid depolarizing effects in- duced by the microinjection of second messenger
molecules cAMP led to the suggestion that the cytoskeletal
motions influence
ion channels
Liberman et al., 1982b, 1985. This conclusion is supported by ultrafast electron microscopic stud-
ies that correlate ion channel activity with cy- toskeletal dynamics Matsumoto et al., 1989.
The cytoskeleton comprises a number of differ- ent types of components. In our implementation,
cytoskeletal dynamics were simulated with 2-D cellular automata Langton, 1984; Wolfram,
1984. External signals impinging on a neuron are converted into signal flows transmitted along
the fibers of the cytoskeleton. A cytoskeletal neu- ron fires when specific signals combine at sites on
the cytoskeleton to directly or indirectly activate the molecules that control the ion channels.
The system was educated to perform desired tasks using evolutionary algorithms Bremer-
mann, 1962; Fogel et al., 1966; Conrad, 1974b; Fogel,
1995. These
algorithms act
at the
cytoskeletal neuron level to generate a repertoire of neurons with different pattern processing
capabilities. They also act at the reference neu- ron level to orchestrate different cytoskeletal
neurons into groups suitable for performing de- sired tasks.
Previous investigations on the malleability of the system showed that its functions change in
accordance with changes in the system’s struc- tures Chen and Conrad, 1997b. The experimen-
tal results also provided information about the fitness landscape implicit in the system’s structure
that facilitates evolutionary learning Chen and Conrad, 1997b. The evolution friendliness of
this system increases as its structural complexity increases. This was implemented by adding more
types of cytoskeletal fibers, allowing weaker in- teractions, and increasing redundancy Chen and
Conrad, 1994b. The integration of intra- and interneuronal information processing also plays a
vital role. The two types of information process- ing yield significant computational and learning
synergies Chen and Conrad, 1994a. With these features, the system is able to learn continuously
in complex problem domains and is effective in coping with problem changes Chen and Conrad,
1997b. Choosing significant features for differen- tiating data and insignificant features for tolerat-
ing noise is not an easy problem for any intelligent
system. Our
experimental results
showed that the system exhibits an effective self- organizing capability in striking a balance be-
tween pattern
categorization and
pattern generalization Chen and Conrad, 1997a.
By adjusting its input – output interfaces, the ANM system had been linked to a number of
problem domains, including perceptionaction, bit pattern recognition, and Chinese character
recognition. After evaluating the overall capabili- ties of the ANM system, our expectation is that
it should be well suited for differentiating chronic hepatitis B patients from healthy individ-
uals and for investigating what would be the significant parameters in determining whether
one is infected with chronic hepatitis B.
2. The architecture
