Knowledge Discovery via Machine Learning for Neurodegenerative Disease Researchers

Ever-increasing size of the biomedical literature makes more precise information retrieval and tapping into implicit knowledge in scientific literature a necessity. In this chapter, first, three new variants of the expectation–maximization (EM) method for semisupervised document classification (Machine Learning 39:103–134, 2000) are introduced to refine biomedical literature meta-searches. The retrieval performance of a multi-mixture per class EM variant with Agglomerative Information Bottleneck clustering (Slonim and Tishby (1999) Agglomerative information bottleneck. In Proceedings of NIPS-12 ) using Davies–Bouldin cluster validity index (IEEE Transactions on Pattern Analysis and Machine Intelligence 1:224–227, 1979), rivaled the state-of-the-art transductive support vector machines (TSVM) (Joachims (1999) Transductive inference for text classification using support vector machines. In Proceedings of the International Conference on Machine Learning (ICML) ). Moreover, the multi-mixture per class EM variant refined search results more quickly with more than one order of magnitude improvement in execution time compared with TSVM. A second tool, CRFNER, uses conditional random fields (Lafferty et al. (2001) Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In Proceedings of ICML-2001 ) to recognize 15 types of named entities from schizophrenia abstracts outperforming ABNER (Settles (2004) Biomedical named entity recognition using conditional random fields and rich feature sets. In Proceedings of COLING 2004 International Joint Workshop on Natural Language Processing in Biomedicine and its Applications (NLPBA) ) in biological named entity recognition and reaching F₁ performance of 82.5% on the second set of named entities.