A brief history of learning classifier systems: from CS-1 to XCS and its variants

© 2015, Springer-Verlag Berlin Heidelberg. The direction set by Wilson’s XCS is that modern Learning Classifier Systems can be characterized by their use of rule accuracy as the utility metric for the search algorithm(s) discovering useful rules. Such searching typically takes place within the restricted space of co-active rules for efficiency. This paper gives an overview of the evolution of Learning Classifier Systems up to XCS, and then of some of the subsequent developments of Wilson’s algorithm to different types of learning

Linear and Branched Glyco-Lipopeptide Vaccines Follow Distinct Cross-Presentation Pathways and Generate Different Magnitudes of Antitumor Immunity

Glyco-lipopeptides, a form of lipid-tailed glyco-peptide, are currently under intense investigation as B- and T-cell based vaccine immunotherapy for many cancers. However, the cellular and molecular mechanisms of glyco-lipopeptides (GLPs) immunogenicity and the position of the lipid moiety on immunogenicity and protective efficacy of GLPs remain to be determined.We have constructed two structural analogues of HER-2 glyco-lipopeptide (HER-GLP) by synthesizing a chimeric peptide made of one universal CD4(+) epitope (PADRE) and one HER-2 CD8(+) T-cell epitope (HER(420-429)). The C-terminal end of the resulting CD4-CD8 chimeric peptide was coupled to a tumor carbohydrate B-cell epitope, based on a regioselectively addressable functionalized templates (RAFT), made of four alpha-GalNAc molecules. The resulting HER glyco-peptide (HER-GP) was then linked to a palmitic acid moiety, attached either at the N-terminal end (linear HER-GLP-1) or in the middle between the CD4+ and CD8+ T cell epitopes (branched HER-GLP-2). We have investigated the uptake, processing and cross-presentation pathways of the two HER-GLP vaccine constructs, and assessed whether the position of linkage of the lipid moiety would affect the B- and T-cell immunogenicity and protective efficacy. Immunization of mice revealed that the linear HER-GLP-1 induced a stronger and longer lasting HER(420-429)-specific IFN-gamma producing CD8(+) T cell response, while the branched HER-GLP-2 induced a stronger tumor-specific IgG response. The linear HER-GLP-1 was taken up easily by dendritic cells (DCs), induced stronger DCs maturation and produced a potent TLR- 2-dependent T-cell activation. The linear and branched HER-GLP molecules appeared to follow two different cross-presentation pathways. While regression of established tumors was induced by both linear HER-GLP-1 and branched HER-GLP-2, the inhibition of tumor growth was significantly higher in HER-GLP-1 immunized mice (p<0.005).These findings have important implications for the development of effective GLP based immunotherapeutic strategies against cancers

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency–Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research

Betaglycan expression is transcriptionally up-regulated during skeletal muscle differentiation - Cloning of murine betaglycan gene promoter and its modulation by myoD, retinoic acid, and transforming growth factor-beta

Betaglycan is a membrane-anchored proteoglycan co-receptor that binds transforming growth factor beta (TGF-beta) via its core protein and basic fibroblast growth factor through its glycosaminoglycan chains. In this study we evaluated the expression of betaglycan during the C2C12 skeletal muscle differentiation. Betaglycan expression, as determined by Northern and Western blot, was up-regulated during the conversion of myoblasts to myotubes. The mouse betaglycan gene promoter was cloned, and its sequence showed putative binding sites for SP1, Smad3, Smad4, muscle regulatory factor elements such as MyoD and MEF2, and retinoic acid receptor. Transcriptional activity of the mouse betaglycan promoter reporter was also up-regulated in differentiating C2C12 cells. We found that MyoD, but not myogenin, stimulated this transcriptional activity even in the presence of high serum. Betaglycan promoter activity was increased by RA and inhibited by the three isoforms of TGF-beta. On the other hand, basic fibroblast growth factor, BMP-2, and hepatocyte growth factor/scatter factor, which are inhibitors of myogenesis, had little effect. In myotubes, up-regulated betaglycan was also detectable by TGF-beta affinity labeling and immunofluorescence microscopy studies. The latter indicated that betaglycan was localized both on the cell surface and in the ECM Forced expression of betaglycan in C2C12 myoblasts increases their responsiveness to TGF-beta2, suggesting that it performs a TGF-beta presentation function in this cell lineage. These results indicate that betaglycan expression is up-regulated during myogenesis and that MyoD and RA modulate its expression by a mechanism that is independent of myogenin

Learning classifier systems: then and now

Broadly conceived as computational models of cognition and tools for modeling complex adaptive systems, later extended for use in adaptive robotics, and today also applied to effective classification and data-mining–what has happened to learning classifier systems in the last decade? This paper addresses this question by examining the current state of learning classifier system research