The immune system as a model for pattern recognition and classification

Abstract

Objective: To design a pattern recognition engine based on concepts derived from mammalian immune systems.

Design: A supervised learning system (Immunos-81) was created using software abstractions of T cells, B cells, antibodies, and their interactions. Artificial T cells control the creation of B-cell populations (clones), which compete for recognition of "unknowns." The B-cell clone with the "simple highest avidity" (SHA) or "relative highest avidity" (RHA) is considered to have successfully classified the unknown.

Measurement: Two standard machine learning data sets, consisting of eight nominal and six continuous variables, were used to test the recognition capabilities of Immunos-81. The first set (Cleveland), consisting of 303 cases of patients with suspected coronary artery disease, was used to perform a ten-way cross-validation. After completing the validation runs, the Cleveland data set was used as a training set prior to presentation of the second data set, consisting of 200 unknown cases.

Results: For cross-validation runs, correct recognition using SHA ranged from a high of 96 percent to a low of 63.2 percent. The average correct classification for all runs was 83.2 percent. Using the RHA metric, 11.2 percent were labeled "too close to determine" and no further attempt was made to classify them. Of the remaining cases, 85.5 percent were correctly classified. When the second data set was presented, correct classification occurred in 73.5 percent of cases when SHA was used and in 80.3 percent of cases when RHA was used.

Conclusions: The immune system offers a viable paradigm for the design of pattern recognition systems. Additional research is required to fully exploit the nuances of immune computation.

Figure 1

(A), Generic antigen with eight potential binding sites—primary structure (T-cell recognition). (B), Generic natural antigen with eight potential binding sites—tertiary structure (B-cell recognition). B cells recognize instances of a class by looking for special features. In natural antigens, these may be thought of as surface features. In the figure, sites 6 and 7 are folded into the body of the antigen, making them relatively inaccessible to B cells/antibodies. Therefore, B cells/antibodies directed at this antigen would tend to notice sites 1 through 5 and site 8. In Immunos-81, special features are denoted by paratopic affinities. In clone populations (groups of B cells), the greater the affinity value of a paratopic site, the more likely that site represents a special feature of the antigen. For example, if “male gender” has an affinity of 0.34 and “female gender” an affinity of 0.51, then being female is a stronger indicator than being male of membership in the antigen class recognized by this clone.

Figure 2

T-cell Paratope and unknown. T cells and antigen binding sites are binary. (A) and (B), Antigens and T cells show a value of 1 at a binding site if the feature is present in the represented class, and a 0 if the feature is absent. Class membership may be determined by matching the T-cell receptor sites to those of the unknown. (C), In the present case a match occurs only at the “Age” paratope/epitope pair (one of five sites). Thus, the antigen is unlikely to be a member of the class recognized by this T cell. During recognition, feature sequence is very important. No attempt is made to reorder features to improve match results. Thus, the feature order and the presence or absence of a feature affects recognition. Partial matches may be used to assign degrees of membership to a class, permitting a form of generalization. Partial matching is not implemented in the current version of Immunos-81.

Figure 3

Clone population. A, Each clone consists of a series of B cells that recognize the same antigen class instance. B, Once an antigen class instance is learned, a clone that recognizes the future instances of that class is created. Notice that, once a clone is formed, individual B cells no longer exist, only their pooled representation in the form of the clone. Also, notice that, at the level of the clone, affinities (Cl_aP_na) are continuous variables.

Figure 4

Coronary artery disease antigen/input record. Antigens are presented to Immunos-81 as a list of ordered fields (they may be database records, or arrays). Fields may be of numeric, nominal, or ordinal type.

Figure 5

Learning algorithm.

Figure 6

Components of the coronary artery disease Immunos-81 artificial immune system (AIS). The amino acid library and T cell are the highest-level components—all information entering the system must be registered with both. The clones are created using information stored in T cells. The clones represented above have nonbinary paratopic affinities. These values will vary depending on antigen concentration, the number of epitopes per antigen, and the number of clones present for instances of the same class in the AIS.

Figure 7

Recognition algorithm for Immunos-81, version 1.0.