GP has long been applied to medicine, biology and bioinformatics. Early work by Handley (1993) and Koza and Andre (1996) used GP to make predictions about the behaviour and properties of biological systems, principally proteins. Oakley, a practising medical doctor, used GP to model blood flow in toes (Oakley, 1994) as part of his long term interests in frostbite.
In 2002 Banzhaf and Foster organised BioGEC: the first GECCO workshop on biological applications of genetic and evolutionary computation. BioGEC has become a bi-annual feature of the annual GECCO conference. Half a year later Marchiori and Corne organised EvoBio: the European conference on evolutionary computation, machine learning and data mining in bioinformatics. EvoBio is held every year alongside EuroGP. GP figures heavily in both BioGEC and EvoBIO.
GP is often used in biomedical data mining. Of particular medical interest are very wide data sets, with many inputs per sample (Lavington, Dewhurst, Wilkins, and Freitas, 1999). Examples include infrared spectra (Ellis, Broadhurst, and Goodacre, 2004; Ellis, Broadhurst, Kell, Rowland, and Goodacre, 2002; Goodacre, 2003; Goodacre, Shann, Gilbert, Timmins, McGovern, Alsberg, Kell, and Logan, 2000; Harrigan, LaPlante, Cosma, Cockerell, Goodacre, Maddox, Luyendyk, Ganey, and Roth, 2004; Johnson, Gilbert, Winson, Goodacre, Smith, Rowland, Hall, and Kell, 2000; McGovern, Broadhurst, Taylor, Kaderbhai, Winson, Small, Rowland, Kell, and Goodacre, 2002; Taylor, Goodacre, Wade, Rowland, and Kell, 1998; ?), single nuclear polymorphisms (Barrett, 2003; Reif, White, and Moore, 2004; Shah and Kusiak, 2004), chest pain (Bojarczuk, Lopes, and Freitas, 2000), and Affymetrix GeneChip microarray data (de Sousa, de C. T. Gomes, Bezerra, de Castro, and Von Zuben, 2004; Eriksson and Olsson, 2004; Heidema, Boer, Nagelkerke, Mariman, van der A, and Feskens, 2006; Ho, Hsieh, Chen, and Huang, 2006; Hong and Cho, 2006; Langdon and Buxton, 2004; Li, Jiang, Li, Moser, Guo, Du, Wang, Topol, Wang, and Rao, 2005; Linden and Bhaya, 2007; ?).
Kell and his colleagues in Aberystwyth have had great success in applying GP widely in bioinformatics (see infrared spectra above and (Allen, Davey, Broadhurst, Heald, Rowland, Oliver, and Kell, 2003; Day, Kell, and Griffith, 2002; Gilbert, Goodacre, Woodward, and Kell, 1997; Goodacre and Gilbert, 1999; Jones, Young, Taylor, Kell, and Rowland, 1998; Kell, 2002a, b,c; Kell, Darby, and Draper, 2001; Shaw, Winson, Woodward, McGovern, Davey, Kaderbhai, Broadhurst, Gilbert, Taylor, Timmins, Goodacre, Kell, Alsberg, and Rowland, 2000; ?)). Another very active group is that of Moore and his colleagues (Moore, Parker, Olsen, and Aune, 2002; Motsinger, Lee, Mellick, and Ritchie, 2006; Ritchie, Motsinger, Bush, Coffey, and Moore, 2007; Ritchie, White, Parker, Hahn, and Moore, 2003).
Computational chemistry is widely used in the drug industry. The properties of simple molecules can be calculated. However, the interactions between chemicals which might be used as drugs and medicinal targets within the body are beyond exact calculation. Therefore, there is great interest in the pharmaceutical industry in approximate in silico models which attempt to predict either favourable or adverse interactions between proto-drugs and biochemical molecules. Since these are computational models, they can be applied very cheaply in advance of the manufacturing of chemicals, to decide which of the myriad of chemicals might be worth further study. Potentially, such models can make a huge impact both in terms of money and time without being anywhere near 100% correct. Machine learning and GP have both been tried. GP approaches include (Bains, Gilbert, Sviridenko, Gascon, Scoffin, Birchall, Harvey, and Caldwell, 2002; Barrett and Langdon, 2006; Buxton, Langdon, and Barrett, 2001; Felton, 2000; Globus, Lawton, and Wipke, 1998; Goodacre, Vaidyanathan, Dunn, Harrigan, and Kell, 2004; Harrigan et al., 2004; Hasan, Daugelat, Rao, and Schreiber, 2006; Krasnogor, 2004; Si, Wang, Zhang, Hu, and Fan, 2006; ?; ?).