TY - JOUR AU - Ayed, Mohamed AU - Lim, Hansaim AU - Xie, Lei PY - 2019 DA - 2019/12/20 TI - Biological representation of chemicals using latent target interaction profile JO - BMC Bioinformatics SP - 674 VL - 20 IS - 24 AB - Computational prediction of a phenotypic response upon the chemical perturbation on a biological system plays an important role in drug discovery, and many other applications. Chemical fingerprints are a widely used feature to build machine learning models. However, the fingerprints that are derived from chemical structures ignore the biological context, thus, they suffer from several problems such as the activity cliff and curse of dimensionality. Fundamentally, the chemical modulation of biological activities is a multi-scale process. It is the genome-wide chemical-target interactions that modulate chemical phenotypic responses. Thus, the genome-scale chemical-target interaction profile will more directly correlate with in vitro and in vivo activities than the chemical structure. Nevertheless, the scope of direct application of the chemical-target interaction profile is limited due to the severe incompleteness, biasness, and noisiness of bioassay data. SN - 1471-2105 UR - https://doi.org/10.1186/s12859-019-3241-3 DO - 10.1186/s12859-019-3241-3 ID - Ayed2019 ER -