T of a novel linkbased weighting scheme for mining biomedical datasets

T of a novel linkbased weighting scheme for mining biomedical datasets; 2) implementation of a novel link-based associative classifier by combining the feature weighting method, weighted association rule mining (WARM) and the CBA algorithm [5]; 3) application of this method to two important biomedical datasets. In the CAL 120 web following sections, the dataset, link-based feature weighting, WARM and algorithm of LAC will be discussed, followed by the application of LAC to two datasets. In the end, we present our conclusions and future work.Table 2. MDL public keys and their weight.Feature 81 82 83 84 85 doi:10.1371/journal.pone.0051018.tWeight 0.8 1 0.8 1.6cancer cells by 50 , is used and processed as following. First, among the 60 tumor cell lines, IGR-OV1, MDA-MB-468 and MDA-N are removed due to too many missing values. Then, compounds having missing values are also discarded. In the final dataset, 5,937 compounds with 57 bioassay results in total are included. For the Ames dataset, if a compound is positive, it is carcinogenic; for the NCI-60, the compound is “active” only if its GI 50 is greater than 5.2. MDL Public KeysMDL public key set also called MACCS key set is a 166-bit string with each bit encoding a predefined chemical structure feature. MDL public keys are extensively used in biomedical research due to their relatively high performance and the one-toone map between the structural feature and fingerprint [37,38]. The fingerprint is computed by using the CDK [39] software package and reformatted for LAC.3. Bio FingerprintBioassay readouts have been used as features (“biospectra” or “bio fingerprint”) for data mining in several studies and produced high quality models [40,41]. These bioactivity profiles link the potential targets with the chemical compounds and provide insights into the relationships among diseases, compounds and bioactivities. In this study, results 1531364 of related bioassay analyses are used as features for the classification of chemical compounds. Each GI50 value is transformed into “active” (GI50 is greater or equal than 5) or “inactive” (GI50 is less than 5). The T-47D is used as a label class and the results from other cell lines are used as features. For each of the 6,512 compounds in Ames data, we attempt to predict whether it is carcinogenic or not based on the MDL public keys. For the 5,937 compounds in NCI 60, we first use Bio fingerprint to predict whether they are agonist or antagonist to T47D cell line. Then, for those 3,199 compounds in the NCI-60 Table 3. Supports and types of itemsets (frequent or not).Materials and Methods 1. Data SetLAC is applied to two datasets: a. Ames mutagenicity dataset [36], b. NCI-60 tumor cell line dataset [37]. In Ames dataset, there are 6,512 compounds provided in get SRIF-14 SMILES format and is benchmarked by SVM, Random Forests, k-Nearest Neighbors, and Gaussian Processes. The authors used 5-fold cross validation to evaluate the generated models. The area under this ROCCurve (AUC) is utilized to assess the performance which ranges from 0.79 to 0.86. The GI50 data of NCI-60, which is the concentration of the anti-cancer drug that inhibits the growth of Table 1. A compound dataset encoded by MDL public keys.Itemset CID C1 C2 C3 C4 C5 C6 MDL Finger print …81,82,83,84… …82,84… …81,84… …81,82,84,85… …81,82,83,84,85… …82,83,85… 81 83 81 83 83 84 81ClassicalWeightedAdjusted WeightedSupport Frequent Support Frequent Support Frequent 0.67 0.50 0.33 0.3.T of a novel linkbased weighting scheme for mining biomedical datasets; 2) implementation of a novel link-based associative classifier by combining the feature weighting method, weighted association rule mining (WARM) and the CBA algorithm [5]; 3) application of this method to two important biomedical datasets. In the following sections, the dataset, link-based feature weighting, WARM and algorithm of LAC will be discussed, followed by the application of LAC to two datasets. In the end, we present our conclusions and future work.Table 2. MDL public keys and their weight.Feature 81 82 83 84 85 doi:10.1371/journal.pone.0051018.tWeight 0.8 1 0.8 1.6cancer cells by 50 , is used and processed as following. First, among the 60 tumor cell lines, IGR-OV1, MDA-MB-468 and MDA-N are removed due to too many missing values. Then, compounds having missing values are also discarded. In the final dataset, 5,937 compounds with 57 bioassay results in total are included. For the Ames dataset, if a compound is positive, it is carcinogenic; for the NCI-60, the compound is “active” only if its GI 50 is greater than 5.2. MDL Public KeysMDL public key set also called MACCS key set is a 166-bit string with each bit encoding a predefined chemical structure feature. MDL public keys are extensively used in biomedical research due to their relatively high performance and the one-toone map between the structural feature and fingerprint [37,38]. The fingerprint is computed by using the CDK [39] software package and reformatted for LAC.3. Bio FingerprintBioassay readouts have been used as features (“biospectra” or “bio fingerprint”) for data mining in several studies and produced high quality models [40,41]. These bioactivity profiles link the potential targets with the chemical compounds and provide insights into the relationships among diseases, compounds and bioactivities. In this study, results 1531364 of related bioassay analyses are used as features for the classification of chemical compounds. Each GI50 value is transformed into “active” (GI50 is greater or equal than 5) or “inactive” (GI50 is less than 5). The T-47D is used as a label class and the results from other cell lines are used as features. For each of the 6,512 compounds in Ames data, we attempt to predict whether it is carcinogenic or not based on the MDL public keys. For the 5,937 compounds in NCI 60, we first use Bio fingerprint to predict whether they are agonist or antagonist to T47D cell line. Then, for those 3,199 compounds in the NCI-60 Table 3. Supports and types of itemsets (frequent or not).Materials and Methods 1. Data SetLAC is applied to two datasets: a. Ames mutagenicity dataset [36], b. NCI-60 tumor cell line dataset [37]. In Ames dataset, there are 6,512 compounds provided in SMILES format and is benchmarked by SVM, Random Forests, k-Nearest Neighbors, and Gaussian Processes. The authors used 5-fold cross validation to evaluate the generated models. The area under this ROCCurve (AUC) is utilized to assess the performance which ranges from 0.79 to 0.86. The GI50 data of NCI-60, which is the concentration of the anti-cancer drug that inhibits the growth of Table 1. A compound dataset encoded by MDL public keys.Itemset CID C1 C2 C3 C4 C5 C6 MDL Finger print …81,82,83,84… …82,84… …81,84… …81,82,84,85… …81,82,83,84,85… …82,83,85… 81 83 81 83 83 84 81ClassicalWeightedAdjusted WeightedSupport Frequent Support Frequent Support Frequent 0.67 0.50 0.33 0.3.