BACKGROUND
efficient and precise evaluation of the potential hazards that drugs, environmental and industrial chemicals pose to humans and other organisms remains a challenge  <cit> .
traditional methods rely heavily on experimental animals and are extremely time consuming, inefficient and expensive.
only a fraction of commercially available chemicals have been tested because of the test difficulty of the traditional methods  <cit> .
therefore, it is essential to develop quick and efficient methods to test and predict the hazard and potential mechanisms of toxicity for chemicals.
based on the concept that the similar gene expression patterns for similarly classified chemicals may indicate similar toxicity and underlying molecular mechanisms  <cit> , scientists have used toxicogenomics strategies to predict toxicity of various compounds.
for instance, gene expression profiles have been successfully applied to the classification of toxicants in rodents  <cit> , and discriminate between hepatotoxic and nonhepatotoxic chemical compounds  <cit> .
similarly, this method was used to successfully distinguish genotoxic from nongenotoxic carcinogenetic chemicals by gene expression profiles in primary mouse hepatocyes  <cit> .
short-term transcriptional profiles have also been used to predict the long-term cancer-related safety of environmental and industrial chemicals  <cit> .
several strategies have been employed to classify expression profiles based on exposure to chemicals of known mechanisms of action  <cit> .
different feature selection and classification methods have been used for the classification and prediction of sample grouping based upon microarray data.
comparative studies of the algorithms have revealed that choosing appropriate algorithms for the classification of microarray data is important  <cit> .
but there remains a a need to comprehensively compare feature selection and classification methods for the classification and prediction using toxicogenomics data.
another challenge of using microarray data for prediction is overfitting, where classification models may not correctly predict new data despite good performance on training datasets  <cit> .
to overcome overfitting, it is essential to choose the right methods or develop new ones.
the liver is the major site of chemical metabolism and a principle organ affected by the toxicity of chemical compounds  <cit> .
primary cultured cells such as hepatocytes offer a convenient in vitro system that can easily be manipulated and used to screen chemicals for toxicity using different molecular and biochemical methods.
primary cell cultures can also reduce concerns regarding animal availability, cost, and welfare that affect in vivo studies  <cit> .
there is a long history of using in vitro systems to screen for new drugs to treat human disease and to study cellular and molecular effects of different molecules  <cit> .
in this study, we built a rapid system to classify chemical compounds based on gene expression profiles generated from in vitro cultured primary rat hepatocytes.
primary rat hepatocytes were exposed in triplicate to one of  <dig> compounds or controls for 24 h followed by microarray analysis of the chemical effects.
a total of  <dig> compounds were divided into  <dig> classes based on their known functional properties, modes of action, and health and safety concern lists .
the  <dig> classes included anti-microbial reagents, cancer-related drugs, energetics , halogenated contaminants, hormones and endocrine disruptors, inflammatory mediators, lipid mediators and peroxisomal mediators, metals, oxidative stress mediators, pesticides, ployaromatic hydrocarbons, pharmaceuticals and protective care products , and pregnane x receptor  mediators.
control samples were regarded as one class.
some categories had chemicals that shared similar structures and cellular effects , while other categories shared similar endpoints .
we examined whether we could use microarray technology to accurately classify and predict these  <dig> class compounds so that we can quickly predict the possible mechanisms and toxic effect of a new compound if its gene expression profile in rat hepatocytes is available.
we extensively compared various normalization, feature selection and classification algorithms for the classification of the  <dig> chemicals into the  <dig> classes.
the normalization methods included gene median value and control sample based normalization methods.
feature selection methods included principal component analysis , chisquare, gainratio, inforgain, relief, and svm recursive feature election .
classification methods used included decision tree j <dig>  random forest , naive bayes , simple logistic , and two support vector machine methods, libsvm and smo.
we also proposed a new feature selection algorithm called gradient method, which had a high training classification rate as well as prediction accuracy with the lowest overfitting rate.
biomarkers that can distinguish compounds into the  <dig> classes were identified that can be used to predict molecular and toxic actions of chemicals based on gene expression profiles.
RESULTS
effect of normalization methods on the classification accuracy of compounds into  <dig> classes
microarray experiments were performed using agilent rat whole genome array  in order to identify biomarkers that would distinguish and predict which of  <dig> classes of compounds, including control classes, a chemical exposure belonged.
cultured primary hepatocytes were treated with  <dig> distinct compounds, as well as their respective vehicle controls, for 24 h after which total rna was isolated for array hybridization .
at least three biological replicates for each compound were used and a total of  <dig> array samples were generated.
the microarray data have been deposited in the geo databases with assigned number gse <dig>
the experiments were conducted over two years.
dataset  <dig> is composed of a total of  <dig> array samples produced in  <dig>  dataset  <dig> is composed of  <dig> array samples that were produced in  <dig>  for each dataset, representatives of each of the  <dig> classes were included.
a complete set of  <dig> compounds were included.
we first assessed the impact of normalization methods on the prediction accuracy of which class a chemicals belongs to out of the  <dig> classes.
two normalization methods were applied, median normalization and control normalization.
for median normalization, the gene intensity was normalized by the median value of all the genes per chip and then a gene was normalized by the median value of the same gene across all the samples in the experiment.
for control normalization, per chip normalization was conducted as in median normalization, then genes in treated samples and their matched control samples were normalized to the median values of the matched control samples.
we compared the averaged prediction accuracies for each class, by averaging the prediction accuracies of  <dig> initial feature  filtering methods including one-way anova, kruscal wallis and one-way anova unequal variance, four feature selection algorithms including chisquare, gainratio, inforgain, and relief, and  <dig> classification methods including j <dig>  rf, nb, sl and smo.
as depicted in figure  <dig>  the overall averaged prediction accuracies of the two normalization methods for all the classes were comparable.
for an individual class, the median normalization method was generally better than the control normalization method with the exception of the control class whose prediction accuracy was higher using control normalization than median normalization.
effect of initial feature filtering methods on the classification accuracy
to find variable probe sets  to separate microarrays into the  <dig> classes of compounds, we compared three initial feature filtering methods including one-way anova, kruscal wallis and one-way anova unequal variance.
using p-value ranked features, we chose different number of probe sets to compare the mean prediction accuracies for each method by averaging the prediction accuracy of the five classification algorithms decision tree j <dig> , rf, nb, sl and smo.
we observed that when the feature size was increased, the prediction accuracies for the three feature filtering methods increased as well .
the averaged prediction accuracies of the three feature filtering methods were similar, but the kruscal wallis method performed slightly better than the other two methods.
effect of classification algorithms on the classification accuracy of  <dig> class compounds
subsequently, we employed the median normalization and kruscal wallis feature filtering methods, we then focused on comparing a variety of classification algorithms.
six classification algorithms were used for the comparison.
the prediction accuracy shown in figure  <dig> was the mean value obtained by averaging the prediction accuracy of  <dig> feature selection methods including chisquare, gainratio, inforgain, pca, svm-rfe and relief.
we found that, in general, prediction accuracy for almost all class methods increased, with the exception of libsvm, when the number of genes used increased.
in contrast, libsvm performed as well as, or better than, higher gene numbers when only a low number of genes were used  .
regardless of gene numbers were used, j <dig> and nb always performed worse than the other methods.
when the gene  numbers were between  <dig> and  <dig>  libsvm, rf, smo and sl methods performed similarly, with sl performing slightly better than the other three methods.
when feature size reached ≥  <dig>  sl performed the best.
by comparing the performances of these classification algorithms on individual classes, we observed a similar pattern as with the averaged prediction accuracy where libsvm performed well when the feature sizes were small, yet sl and smo performed well when more genes were used.
interestingly, for some compound classes such as pxr, libsvm always performed the best regardless of feature size selected.
however, libsvm was the worst algorithm to predict the control class when the gene numbers were over  <dig> .
compound classes such as metals, halogenated contaminants, and polyaromatic hydrocarbons consistently exhibited higher prediction accuracy than other classes no matter what classification algorithms were applied.
in contrast, certain classes such as anti-microbial and oxidative stress generally received lower prediction accuracy by all the classification algorithms.
effect of feature selection algorithms on classification accuracy
we compared prediction results for six feature selection methods: pca, chisquare, gainratio, inforgain, relief, and svm-rfe .
the prediction accuracy was obtained by the averaging of values of six different classification algorithms: j <dig>  libsvm, nb, rf, smo, and sl.
similar to previous observations , there was an increase in prediction accuracies when the number of genes increased.
overall, the svm-rfe algorithm outperformed other methods no matter what gene number was used .
the maximum gene number found for pca was  <dig>  but it was the poorest performing algorithm in comparison to other algorithms.
inforgain, chisquare and gainratio algorithms gave comparable performances while the relief algorithm generally performed slightly worse.
however, when only  <dig> genes were selected as features, relief outperformed inforgain, chisquare and gainratio algorithms.
the best models for the classification of  <dig> class compounds
after comparing a series of methods, we found that using the svm-rfe feature selection method and the libsvm classification algorithm would achieve a high prediction accuracy for the  <dig> classes of compounds.
nearly 100% accuracy was observed for all feature sizes from  <dig> to  <dig> genes .
in contrast, other feature selection methods such as chisquare, gainratio, inforgain, relief, and pca had much lower accuracy when compared to svm-rfe using the same libsvm classification method.
overall, gainratio had the poorest prediction accuracy compared to other methods.
similar to what was observed in figure  <dig>  as more features were applied using libsvm for classification, prediction accuracies for the  <dig> classes of compounds were reduced.
prediction of chemical classes using an independent dataset
to test the reliability of our classification models, we used the best model to train on dataset  <dig> to predict dataset  <dig>  dataset  <dig> was not used for training in the prediction of classes for dataset  <dig> because of the small size of dataset  <dig>  when tested with svm-rfe based on libsvm, we obtained 100% training accuracy for dataset  <dig> using  <dig> gene features.
however a low  prediction accuracy was achieved suggesting that the method may be overfitting the data.
to overcome these problems, we developed a new feature selection method called gradient method .
we identified the best model for each feature selection method by comparing the different feature selection methods and classification algorithms in training on dataset  <dig>  the best training accuracy by the feature selection methods was achieved using svm-rfe .
the accuracy of relief, inforgain, chisquare, gainratio and gradient methods were comparable, but much lower than svm-rfe .
the prediction accuracies of relief, inforgain, chisquare, gainratio were significantly lower than their respective training accuracies.
the gradient method achieved the highest prediction accuracy, which was close to its training accuracy.
when the gradient method was applied to all the samples  for training, the prediction accuracy was also comparable to other feature selection methods except svm-rfe.
when libsvm classification was conducted, the training classification accuracy of gradient feature selection was much higher than other methods, with the exception of svm-rfe .
*dataset  <dig>  has a total of  <dig> array samples what were produced in  <dig>  and dataset  <dig>  includes  <dig> array samples that were hybridized in  <dig>  for each dataset, a complete set of  <dig> compounds were included.
because of the difference between training and prediction accuracies, we further compared the overfitting rates of different feature selection methods over three classification algorithms  that could yield good classification accuracy.
we found that the gradient feature selection method had the lowest overfitting rate for both smo and sl classification algorithms .
when libsvm classification was applied, the gradient algorithm was also fairly low and was similarly low when the feature selection methods including gainratio, inforgain, and relief were used.
inforgain had the highest overfitting rate for the smo classification, followed by relief and gainratio feature selection methods.
svm-rfe had the highest overfitting rate when either sl or libsvm classification was conducted.
the algorithms had the second highest over fitting rates for sl and libsvm were inforgain and pca respectively .
gene expression pattern and functional analysis of gene markers
we chose  <dig> transcripts  identified using the gradient algorithm to perform a two-way hierarchical cluster analysis across the  <dig> classes of compounds.
metal and inflammatory compound classes clustered together to form a separate group from other classes .
antimicrobial, cancer-related drugs and pesticides clustered to form a distinctive subgroup .
interestingly,  <dig> transcripts were strongly and specifically down-regulated by the metal and inflammatory compound classes, but were up-regulated by anti-microbials, cancer related drugs, pesticides, pxr mediators, and some halogenated contaminants.
furthermore, we examined how each compound in the classes of metals and inflammatory mediators, antimicrobial, cancer related drugs, pesticides, and pxr mediators affected these  <dig> transcripts .
we observed that most of the compounds in the antimicrobial, cancer related drugs, pesticides, and pxr mediators classes up-regulated the expression of the transcripts, except cyclosprin a in the antimicrobial class, cytochalasin and vinblastine sulfate in the cancer-related drug class, dexamethasone and ketoconazole in the pxr mediators class.
most of the compounds in the metals and inflammatory mediators classes, in contrast, down-regulated the  <dig> transcripts with the exception of zinc.
seventy-eight genes were well annotated in the  <dig> transcripts.
using gene ontology  and the ipa function analysis tool, we found that cell cycle was the most significantly enriched functional term detected by both tools .
surprisingly, over half the annotated genes belonged to the cell cycle process .
other significant functional terms included cellular assembly and organization, dna replication, recombination and repair, cellular movement, cell death, cellular compromise and cellular growth and proliferation.
most of the genes involved in the cell cycle process also play a role in one or more other functional processes listed in table  <dig>  therefore, cell cycle was the most significant and crucial functional term in the  <dig> gene list.
several gene families had multiple gene numbers participating in cell cycle and other functional processes .
two such families were the kinesin family , 20b ,  <dig> , 2c , and 4a ) and the cyclin family , cyclin b <dig>  cyclin b <dig>  and cyclin e1).
the minichromosome maintenance complex component  family genes were also highly enriched .
pathway analyses also showed that cell cycle associated pathways were the most significant pathways in the gene list, which included mitotic roles of polo-like kinase, and cell cycle: g2/m dna damage checkpoint regulation pathways .
cancer-related pathways  were also highly enriched in the gene list.
several genes were involved in multiple pathways listed in table  <dig>  and they were ccnb <dig>  ccnb <dig>  cell division cycle  <dig>  g <dig> to s and g <dig> to m  and rad <dig> homolog .
our results indicate that cell cycle is an important process and its opposite regulation can help to distinguish different compound classes.
discussion
in this study, we compared the efficacy of a variety of methods including different normalization, feature selection, classification algorithms for the classification of gene expression profiles of  <dig> chemicals into  <dig> classes of compounds.
few reports have studied the impact of normalization methods on the classification accuracy based on microarray data.
since microarray experiments were analyzed over different time periods in our study, a batch effect could significantly affect our analyses as has been observed in other studies  <cit> .
removal of batch effects through normalization can be used to integrate different microarray datasets and improve prediction  <cit> .
therefore, we compared two normalization methods including a method based on median value to all samples and methods based on median value to control samples to remove batch effects.
as expected, the control sample based normalization improved the classification accuracy of the control class since the batch effect of the samples in the control class was removed.
nevertheless, the average classification accuracy is comparable for these two methods, and for other compound classes, the classification accuracy based on median normalization was even higher.
our results indicate that in our case with more than  <dig> class compounds, the control sample based normalization did not improve results relative to a simple median normalization.
for a quick evaluation, we chose 24 h for our microarray test.
our previous experiments did find that there were more genes were changed at 24 h than 48 h  <cit> .
for an in vitro study, time is an issue, we do not want to be too long to increase the cost and also not too short and some effects may not occur.
we have added the discussion and the reference in the discussion part.
note also that primary hepatocytes rapidly adapt to cell culture changing from the in vivo liver cells they once were.
perkins et al.
<dig>  <cit>  found that this adaptation effect appeared to dominate chemical effects at 48 hrs.
to examine if the microarray data could be used to predict what chemical class the rat hepatocytes were exposed to, we compared different classification algorithms for their performance in predicting which of the  <dig> classes a chemical exposure belongs.
we found that decision tree analysis always performed the poorest, which was consistent with other comparative reports  <cit> .
nb method did not perform well in this study, although has it performed well in two class datasets  <cit> .
overall two svm algorithms, libsvm and smo, performed very well.
svms have been shown to have the best performance in other comparative studies using both gene expression  <cit>  and proteomics data  <cit> .
interestingly, we found that libsvm outperformed other classification algorithms when the gene feature size was small, and thus far we have not found any studies to report similar results.
this is the reason why more features had less performance in figure  <dig> and it did not happen in other classification algorithms.
sl classification algorithm also performed fairly well in comparison to other algorithms.
although a few studies have demonstrated that sl is an appropriate method for the classification of gene expression data  <cit> , there has been no comparison of this method to other methods using gene expression data.
our results offer some useful information on applying this method to multiple class prediction.
we also conducted a comprehensive comparison of various feature selection methods on the classification of the  <dig> classes of compounds.
for training purposes, we found that svm-rfe usually outperformed other methods as has been confirmed elsewhere  <cit> .
however, when an independent dataset, dataset  <dig>  was used for a prediction, svm-rfe gave a high overfitting rate.
it had the highest overfitting rate when either sl or libsvm classification was performed.
considering that a high training accuracy does not entail a high prediction accuracy, we developed a new feature selection algorithm called gradient method to find reasonable variable features across multiple classes.
the gradient method had a similar training accuracy when compared to most of the feature selection methods .
it also achieved the highest prediction accuracy using the independent dataset  with the lowest overfitting rate .
although efforts to reduce the prediction overfitting based on microarray data have been made  <cit> , our method is a valuable addition for selecting features to produce a more reliable prediction.
genes selected as biomarkers generally tend to have similar expression patterns relative to one or more classes of chemicals they are exposed to.
when this common effect is seen between multiple classes a similar function may be impacted across the classes thereby providing some insight into mechanisms by which a chemical causes effects.
we identified a cluster of  <dig> transcripts that were significantly down-regulated by metals and inflammatory compounds, but were up-regulated by anti-microbials, cancer-related drugs, pesticides, and pxr mediators.
over half of the genes in this cluster are involved in cell cycling.
this result indicates that metals and inflammatory mediators may share similar activity.
the inflammatory mediator class contained compounds such as aspirin, ibuprofen, il- <dig>  lps, tnfα, have inflammation regulation function.
a series of studies has also revealed that metal complexes could affect inflammation  <cit> .
the reduction of cell cycle gene expression by both inflammatory mediators and metals suggests that these compounds may regulate inflammation by inhibiting the cell cycle process.
an important pathway involved in the cell cycle is mitotic roles of polo-like kinase pathway was significantly impacted where more than half the genes in the pathway  were repressed by the compounds in the two classes .
the polo-like kinases  make up an evolutionarily conserved and newly emerging family of essential cell cycle regulators.
plks regulate diverse cellular and biochemical events at various stages of the m-phase.
they are required at several key points through mitosis, starting from control of the g2/m transition through phosphorylation of cdc25c and mitotic cyclins and in the dna damage checkpoint adaptation to prevent entry into mitosis.
at the beginning of mitosis, various proteins are recruited to the centrosome, a maturation process that requires plk.
plks are also required for the establishment of a bipolar spindle.
they have a role in the metaphase to anaphase transition via its interaction with the apc/cyclosome  <cit>  .
many of these cell cycle genes including mcm, cyclin and kif family genes directly interact with each other and directly or indirectly interact with the nf-κb complex .
nf-κb is a well known molecule that participates in the inflammation process and plays an important role in cell cycle  <cit> .
the network analysis identified a possible new mechanism of the inflammation regulation through the cell cycle gene regulated nf-κb complex pathway  <cit> .
further studies are needed to explore how these cell cycle genes affect the nf-κb pathway.
we have manually divided the chemicals into  <dig> classes according to their known mode actions and toxicities.
while imperfect, it is a simple and usable classification scheme.
based on the classification system and gene expression profiles, we have successfully developed a gene expression classifier to predict different compound classes.
this gene list should have applicability in predicting what class a new compound belongs to when a gene expression profile available with the  <dig> genes.
CONCLUSIONS
our results indicate that using microarrays and a supervised machine learning approach to predict chemical toxicants, their potential toxicity and mechanisms of action is practical and efficient.
choosing the right feature and classification algorithms for this multiple category classification and prediction is critical.

 efficient and precise evaluation of the potential hazards that drugs, environmental and industrial chemicals pose to humans and other organisms remains a challenge. traditional methods rely heavily on experimental animals and are extremely time consuming, inefficient and expensive. only a fraction of commercially available chemicals have been tested because of the test difficulty of the traditional methods. therefore, it is essential to develop quick and efficient methods to test and predict the hazard and potential mechanisms of toxicity for chemicals. based on the concept that the similar gene expression patterns for similarly classified chemicals may indicate similar toxicity and underlying molecular mechanisms, scientists have used toxicogenomics strategies to predict toxicity of various compounds. for instance, gene expression profiles have been successfully applied to the classification of toxicants in rodents, and discriminate between hepatotoxic and nonhepatotoxic chemical compounds. similarly, this method was used to successfully distinguish genotoxic from nongenotoxic carcinogenetic chemicals by gene expression profiles in primary mouse hepatocyes. short-term transcriptional profiles have also been used to predict the long-term cancer-related safety of environmental and industrial chemicals. several strategies have been employed to classify expression profiles based on exposure to chemicals of known mechanisms of action. different feature selection and classification methods have been used for the classification and prediction of sample grouping based upon microarray data. comparative studies of the algorithms have revealed that choosing appropriate algorithms for the classification of microarray data is important. but there remains a a need to comprehensively compare feature selection and classification methods for the classification and prediction using toxicogenomics data. another challenge of using microarray data for prediction is overfitting, where classification models may not correctly predict new data despite good performance on training datasets. to overcome overfitting, it is essential to choose the right methods or develop new ones. the liver is the major site of chemical metabolism and a principle organ affected by the toxicity of chemical compounds. primary cultured cells such as hepatocytes offer a convenient in vitro system that can easily be manipulated and used to screen chemicals for toxicity using different molecular and biochemical methods. primary cell cultures can also reduce concerns regarding animal availability, cost, and welfare that affect in vivo studies. there is a long history of using in vitro systems to screen for new drugs to treat human disease and to study cellular and molecular effects of different molecules. in this study, we built a rapid system to classify chemical compounds based on gene expression profiles generated from in vitro cultured primary rat hepatocytes. primary rat hepatocytes were exposed in triplicate to one of compounds or controls for 24 h followed by microarray analysis of the chemical effects. a total of compounds were divided into classes based on their known functional properties, modes of action, and health and safety concern lists. the classes included anti-microbial reagents, cancer-related drugs, energetics, halogenated contaminants, hormones and endocrine disruptors, inflammatory mediators, lipid mediators and peroxisomal mediators, metals, oxidative stress mediators, pesticides, ployaromatic hydrocarbons, pharmaceuticals and protective care products, and pregnane x receptor mediators. control samples were regarded as one class. some categories had chemicals that shared similar structures and cellular effects, while other categories shared similar endpoints. we examined whether we could use microarray technology to accurately classify and predict these class compounds so that we can quickly predict the possible mechanisms and toxic effect of a new compound if its gene expression profile in rat hepatocytes is available. we extensively compared various normalization, feature selection and classification algorithms for the classification of the chemicals into the classes. the normalization methods included gene median value and control sample based normalization methods. feature selection methods included principal component analysis, chisquare, gainratio, inforgain, relief, and svm recursive feature election. classification methods used included decision tree j random forest, naive bayes, simple logistic, and two support vector machine methods, libsvm and smo. we also proposed a new feature selection algorithm called gradient method, which had a high training classification rate as well as prediction accuracy with the lowest overfitting rate. biomarkers that can distinguish compounds into the classes were identified that can be used to predict molecular and toxic actions of chemicals based on gene expression profiles.  effect of normalization methods on the classification accuracy of compounds into classes microarray experiments were performed using agilent rat whole genome array in order to identify biomarkers that would distinguish and predict which of classes of compounds, including control classes, a chemical exposure belonged. cultured primary hepatocytes were treated with distinct compounds, as well as their respective vehicle controls, for 24 h after which total rna was isolated for array hybridization. at least three biological replicates for each compound were used and a total of array samples were generated. the microarray data have been deposited in the geo databases with assigned number gse the experiments were conducted over two years. dataset is composed of a total of array samples produced in dataset is composed of array samples that were produced in for each dataset, representatives of each of the classes were included. a complete set of compounds were included. we first assessed the impact of normalization methods on the prediction accuracy of which class a chemicals belongs to out of the classes. two normalization methods were applied, median normalization and control normalization. for median normalization, the gene intensity was normalized by the median value of all the genes per chip and then a gene was normalized by the median value of the same gene across all the samples in the experiment. for control normalization, per chip normalization was conducted as in median normalization, then genes in treated samples and their matched control samples were normalized to the median values of the matched control samples. we compared the averaged prediction accuracies for each class, by averaging the prediction accuracies of initial feature filtering methods including one-way anova, kruscal wallis and one-way anova unequal variance, four feature selection algorithms including chisquare, gainratio, inforgain, and relief, and classification methods including j rf, nb, sl and smo. as depicted in figure  the overall averaged prediction accuracies of the two normalization methods for all the classes were comparable. for an individual class, the median normalization method was generally better than the control normalization method with the exception of the control class whose prediction accuracy was higher using control normalization than median normalization. effect of initial feature filtering methods on the classification accuracy to find variable probe sets to separate microarrays into the classes of compounds, we compared three initial feature filtering methods including one-way anova, kruscal wallis and one-way anova unequal variance. using p-value ranked features, we chose different number of probe sets to compare the mean prediction accuracies for each method by averaging the prediction accuracy of the five classification algorithms decision tree j, rf, nb, sl and smo. we observed that when the feature size was increased, the prediction accuracies for the three feature filtering methods increased as well. the averaged prediction accuracies of the three feature filtering methods were similar, but the kruscal wallis method performed slightly better than the other two methods. effect of classification algorithms on the classification accuracy of class compounds subsequently, we employed the median normalization and kruscal wallis feature filtering methods, we then focused on comparing a variety of classification algorithms. six classification algorithms were used for the comparison. the prediction accuracy shown in figure  was the mean value obtained by averaging the prediction accuracy of feature selection methods including chisquare, gainratio, inforgain, pca, svm-rfe and relief. we found that, in general, prediction accuracy for almost all class methods increased, with the exception of libsvm, when the number of genes used increased. in contrast, libsvm performed as well as, or better than, higher gene numbers when only a low number of genes were used. regardless of gene numbers were used, j and nb always performed worse than the other methods. when the gene numbers were between and libsvm, rf, smo and sl methods performed similarly, with sl performing slightly better than the other three methods. when feature size reached ≥  sl performed the best. by comparing the performances of these classification algorithms on individual classes, we observed a similar pattern as with the averaged prediction accuracy where libsvm performed well when the feature sizes were small, yet sl and smo performed well when more genes were used. interestingly, for some compound classes such as pxr, libsvm always performed the best regardless of feature size selected. however, libsvm was the worst algorithm to predict the control class when the gene numbers were over. compound classes such as metals, halogenated contaminants, and polyaromatic hydrocarbons consistently exhibited higher prediction accuracy than other classes no matter what classification algorithms were applied. in contrast, certain classes such as anti-microbial and oxidative stress generally received lower prediction accuracy by all the classification algorithms. effect of feature selection algorithms on classification accuracy we compared prediction results for six feature selection methods: pca, chisquare, gainratio, inforgain, relief, and svm-rfe. the prediction accuracy was obtained by the averaging of values of six different classification algorithms: j libsvm, nb, rf, smo, and sl. similar to previous observations, there was an increase in prediction accuracies when the number of genes increased. overall, the svm-rfe algorithm outperformed other methods no matter what gene number was used. the maximum gene number found for pca was but it was the poorest performing algorithm in comparison to other algorithms. inforgain, chisquare and gainratio algorithms gave comparable performances while the relief algorithm generally performed slightly worse. however, when only genes were selected as features, relief outperformed inforgain, chisquare and gainratio algorithms. the best models for the classification of class compounds after comparing a series of methods, we found that using the svm-rfe feature selection method and the libsvm classification algorithm would achieve a high prediction accuracy for the classes of compounds. nearly 100% accuracy was observed for all feature sizes from to genes. in contrast, other feature selection methods such as chisquare, gainratio, inforgain, relief, and pca had much lower accuracy when compared to svm-rfe using the same libsvm classification method. overall, gainratio had the poorest prediction accuracy compared to other methods. similar to what was observed in figure  as more features were applied using libsvm for classification, prediction accuracies for the classes of compounds were reduced. prediction of chemical classes using an independent dataset to test the reliability of our classification models, we used the best model to train on dataset to predict dataset dataset was not used for training in the prediction of classes for dataset because of the small size of dataset when tested with svm-rfe based on libsvm, we obtained 100% training accuracy for dataset using gene features. however a low prediction accuracy was achieved suggesting that the method may be overfitting the data. to overcome these problems, we developed a new feature selection method called gradient method. we identified the best model for each feature selection method by comparing the different feature selection methods and classification algorithms in training on dataset the best training accuracy by the feature selection methods was achieved using svm-rfe. the accuracy of relief, inforgain, chisquare, gainratio and gradient methods were comparable, but much lower than svm-rfe. the prediction accuracies of relief, inforgain, chisquare, gainratio were significantly lower than their respective training accuracies. the gradient method achieved the highest prediction accuracy, which was close to its training accuracy. when the gradient method was applied to all the samples for training, the prediction accuracy was also comparable to other feature selection methods except svm-rfe. when libsvm classification was conducted, the training classification accuracy of gradient feature selection was much higher than other methods, with the exception of svm-rfe. *dataset has a total of array samples what were produced in and dataset includes array samples that were hybridized in for each dataset, a complete set of compounds were included. because of the difference between training and prediction accuracies, we further compared the overfitting rates of different feature selection methods over three classification algorithms that could yield good classification accuracy. we found that the gradient feature selection method had the lowest overfitting rate for both smo and sl classification algorithms. when libsvm classification was applied, the gradient algorithm was also fairly low and was similarly low when the feature selection methods including gainratio, inforgain, and relief were used. inforgain had the highest overfitting rate for the smo classification, followed by relief and gainratio feature selection methods. svm-rfe had the highest overfitting rate when either sl or libsvm classification was conducted. the algorithms had the second highest over fitting rates for sl and libsvm were inforgain and pca respectively. gene expression pattern and functional analysis of gene markers we chose transcripts identified using the gradient algorithm to perform a two-way hierarchical cluster analysis across the classes of compounds. metal and inflammatory compound classes clustered together to form a separate group from other classes. antimicrobial, cancer-related drugs and pesticides clustered to form a distinctive subgroup. interestingly, transcripts were strongly and specifically down-regulated by the metal and inflammatory compound classes, but were up-regulated by anti-microbials, cancer related drugs, pesticides, pxr mediators, and some halogenated contaminants. furthermore, we examined how each compound in the classes of metals and inflammatory mediators, antimicrobial, cancer related drugs, pesticides, and pxr mediators affected these transcripts. we observed that most of the compounds in the antimicrobial, cancer related drugs, pesticides, and pxr mediators classes up-regulated the expression of the transcripts, except cyclosprin a in the antimicrobial class, cytochalasin and vinblastine sulfate in the cancer-related drug class, dexamethasone and ketoconazole in the pxr mediators class. most of the compounds in the metals and inflammatory mediators classes, in contrast, down-regulated the transcripts with the exception of zinc. seventy-eight genes were well annotated in the transcripts. using gene ontology and the ipa function analysis tool, we found that cell cycle was the most significantly enriched functional term detected by both tools. surprisingly, over half the annotated genes belonged to the cell cycle process. other significant functional terms included cellular assembly and organization, dna replication, recombination and repair, cellular movement, cell death, cellular compromise and cellular growth and proliferation. most of the genes involved in the cell cycle process also play a role in one or more other functional processes listed in table  therefore, cell cycle was the most significant and crucial functional term in the gene list. several gene families had multiple gene numbers participating in cell cycle and other functional processes. two such families were the kinesin family, 20b,, 2c, and 4a ) and the cyclin family, cyclin b cyclin b and cyclin e1). the minichromosome maintenance complex component family genes were also highly enriched. pathway analyses also showed that cell cycle associated pathways were the most significant pathways in the gene list, which included mitotic roles of polo-like kinase, and cell cycle: g2/m dna damage checkpoint regulation pathways. cancer-related pathways were also highly enriched in the gene list. several genes were involved in multiple pathways listed in table  and they were ccnb ccnb cell division cycle g to s and g to m and rad homolog. our results indicate that cell cycle is an important process and its opposite regulation can help to distinguish different compound classes. discussion in this study, we compared the efficacy of a variety of methods including different normalization, feature selection, classification algorithms for the classification of gene expression profiles of chemicals into classes of compounds. few reports have studied the impact of normalization methods on the classification accuracy based on microarray data. since microarray experiments were analyzed over different time periods in our study, a batch effect could significantly affect our analyses as has been observed in other studies. removal of batch effects through normalization can be used to integrate different microarray datasets and improve prediction. therefore, we compared two normalization methods including a method based on median value to all samples and methods based on median value to control samples to remove batch effects. as expected, the control sample based normalization improved the classification accuracy of the control class since the batch effect of the samples in the control class was removed. nevertheless, the average classification accuracy is comparable for these two methods, and for other compound classes, the classification accuracy based on median normalization was even higher. our results indicate that in our case with more than class compounds, the control sample based normalization did not improve results relative to a simple median normalization. for a quick evaluation, we chose 24 h for our microarray test. our previous experiments did find that there were more genes were changed at 24 h than 48 h. for an in vitro study, time is an issue, we do not want to be too long to increase the cost and also not too short and some effects may not occur. we have added the discussion and the reference in the discussion part. note also that primary hepatocytes rapidly adapt to cell culture changing from the in vivo liver cells they once were. perkins et al. found that this adaptation effect appeared to dominate chemical effects at 48 hrs. to examine if the microarray data could be used to predict what chemical class the rat hepatocytes were exposed to, we compared different classification algorithms for their performance in predicting which of the classes a chemical exposure belongs. we found that decision tree analysis always performed the poorest, which was consistent with other comparative reports. nb method did not perform well in this study, although has it performed well in two class datasets. overall two svm algorithms, libsvm and smo, performed very well. svms have been shown to have the best performance in other comparative studies using both gene expression and proteomics data. interestingly, we found that libsvm outperformed other classification algorithms when the gene feature size was small, and thus far we have not found any studies to report similar results. this is the reason why more features had less performance in figure  and it did not happen in other classification algorithms. sl classification algorithm also performed fairly well in comparison to other algorithms. although a few studies have demonstrated that sl is an appropriate method for the classification of gene expression data, there has been no comparison of this method to other methods using gene expression data. our results offer some useful information on applying this method to multiple class prediction. we also conducted a comprehensive comparison of various feature selection methods on the classification of the classes of compounds. for training purposes, we found that svm-rfe usually outperformed other methods as has been confirmed elsewhere. however, when an independent dataset, dataset was used for a prediction, svm-rfe gave a high overfitting rate. it had the highest overfitting rate when either sl or libsvm classification was performed. considering that a high training accuracy does not entail a high prediction accuracy, we developed a new feature selection algorithm called gradient method to find reasonable variable features across multiple classes. the gradient method had a similar training accuracy when compared to most of the feature selection methods. it also achieved the highest prediction accuracy using the independent dataset with the lowest overfitting rate. although efforts to reduce the prediction overfitting based on microarray data have been made, our method is a valuable addition for selecting features to produce a more reliable prediction. genes selected as biomarkers generally tend to have similar expression patterns relative to one or more classes of chemicals they are exposed to. when this common effect is seen between multiple classes a similar function may be impacted across the classes thereby providing some insight into mechanisms by which a chemical causes effects. we identified a cluster of transcripts that were significantly down-regulated by metals and inflammatory compounds, but were up-regulated by anti-microbials, cancer-related drugs, pesticides, and pxr mediators. over half of the genes in this cluster are involved in cell cycling. this result indicates that metals and inflammatory mediators may share similar activity. the inflammatory mediator class contained compounds such as aspirin, ibuprofen, il- lps, tnfα, have inflammation regulation function. a series of studies has also revealed that metal complexes could affect inflammation. the reduction of cell cycle gene expression by both inflammatory mediators and metals suggests that these compounds may regulate inflammation by inhibiting the cell cycle process. an important pathway involved in the cell cycle is mitotic roles of polo-like kinase pathway was significantly impacted where more than half the genes in the pathway were repressed by the compounds in the two classes. the polo-like kinases make up an evolutionarily conserved and newly emerging family of essential cell cycle regulators. plks regulate diverse cellular and biochemical events at various stages of the m-phase. they are required at several key points through mitosis, starting from control of the g2/m transition through phosphorylation of cdc25c and mitotic cyclins and in the dna damage checkpoint adaptation to prevent entry into mitosis. at the beginning of mitosis, various proteins are recruited to the centrosome, a maturation process that requires plk. plks are also required for the establishment of a bipolar spindle. they have a role in the metaphase to anaphase transition via its interaction with the apc/cyclosome. many of these cell cycle genes including mcm, cyclin and kif family genes directly interact with each other and directly or indirectly interact with the nf-κb complex. nf-κb is a well known molecule that participates in the inflammation process and plays an important role in cell cycle. the network analysis identified a possible new mechanism of the inflammation regulation through the cell cycle gene regulated nf-κb complex pathway. further studies are needed to explore how these cell cycle genes affect the nf-κb pathway. we have manually divided the chemicals into classes according to their known mode actions and toxicities. while imperfect, it is a simple and usable classification scheme. based on the classification system and gene expression profiles, we have successfully developed a gene expression classifier to predict different compound classes. this gene list should have applicability in predicting what class a new compound belongs to when a gene expression profile available with the genes.  our results indicate that using microarrays and a supervised machine learning approach to predict chemical toxicants, their potential toxicity and mechanisms of action is practical and efficient. choosing the right feature and classification algorithms for this multiple category classification and prediction is critical. 
our results indicate that using microarrays and a supervised machine learning approach to predict chemical toxicants, their potential toxicity and mechanisms of action is practical and efficient. therefore, we developed a new feature selection algorithm called gradient method that had a relatively high training classification as well as prediction accuracy with the lowest overfitting rate of the methods tested. we comprehensively compared various normalization of gene expression profiles, feature selection and classification algorithms for the classification of these chemicals into compound classes. svm recursive feature selection had the highest overfitting rate when an independent dataset was used for a prediction. choosing the right feature and classification algorithms for this multiple category classification and prediction is critical. in this study, in vitro cultures of primary rat hepatocytes were exposed to chemicals and vehicle controls, representing compound classes. here, we investigated the use of microarrays to predict chemical toxicants and their possible mechanisms of action. analysis of biomarkers that distinguished the classes of compounds identified a group of genes principally involved in cell cycle function that were significantly downregulated by metal and inflammatory compounds, but were induced by anti-microbial, cancer related drugs, pesticides, and pxr mediators. we found that normalization had little effect on the averaged classification accuracy.
 high throughput transcriptomics profiles such as those generated using microarrays have been useful in identifying biomarkers for different classification and toxicity prediction purposes. here, we investigated the use of microarrays to predict chemical toxicants and their possible mechanisms of action.  in this study, in vitro cultures of primary rat hepatocytes were exposed to chemicals and vehicle controls, representing compound classes. we comprehensively compared various normalization of gene expression profiles, feature selection and classification algorithms for the classification of these chemicals into compound classes. we found that normalization had little effect on the averaged classification accuracy. two support vector machine methods, libsvm and sequential minimal optimization, had better classification performance than other methods. svm recursive feature selection had the highest overfitting rate when an independent dataset was used for a prediction. therefore, we developed a new feature selection algorithm called gradient method that had a relatively high training classification as well as prediction accuracy with the lowest overfitting rate of the methods tested. analysis of biomarkers that distinguished the classes of compounds identified a group of genes principally involved in cell cycle function that were significantly downregulated by metal and inflammatory compounds, but were induced by anti-microbial, cancer related drugs, pesticides, and pxr mediators.  our results indicate that using microarrays and a supervised machine learning approach to predict chemical toxicants, their potential toxicity and mechanisms of action is practical and efficient. choosing the right feature and classification algorithms for this multiple category classification and prediction is critical. biomarkermicroarrayhepatocyteschemicalclassification 