Expression data in HK-2 cells following exposure to CsA (external dataset 1)
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE27096 | GEO | 2015/12/31
SECONDARY ACCESSION(S): PRJNA141947
REPOSITORIES: GEO
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Project description:Background: The development of a new drug from candidate to market is a complex process requiring vast resources of time, money and personnel. The rate of failure in the development pipeline is enormous, leading to wasted resources that could have been better employed on alternative candidates. The requirement for early stage prediction of toxicity is, then, of paramount importance to expedite the introduction of new therapies to clinical practice. To date, most transcriptomics efforts to solve this problem have applied Support Vector Machine techniques to data derived from in vivo studies in rats. Results: We applied a toxicogenomics approach to determine whether known renal toxicants could be identified as such from their effects on the transcriptome of the human renal proximal tubular epithelial cell line, HK-2. Based on clustering of differentially expressed genes, we identified 3 toxicity groups within the set of compounds. We used Random Forest to generate a classifier to accurately place compounds in groups. The classifier is based on a signature biomarker comprising 21 genes identified by Random Forest and could differentiate between the groups with high accuracy. Furthermore, we could correctly classify external samples from a dataset exhibiting a marked ‘batch effect’. Conclusions: No toxicity-associated gene expression alterations could be identified across a set of toxic compounds. Random Forest is a suitable technique for the classification of compounds into toxicity groups. Using a measure of differential expression rather than expression level per se generates a robust classifier that can potentially be applied in a platform-independent manner.
2015-12-31 | GSE27189 | GEO
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2015-12-31 | GSE27169 | GEO
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2015-12-31 | GSE27168 | GEO
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2015-12-31 | GSE27210 | GEO
Project description:Background: The development of a new drug from candidate to market is a complex process requiring vast resources of time, money and personnel. The rate of failure in the development pipeline is enormous, leading to wasted resources that could have been better employed on alternative candidates. The requirement for early stage prediction of toxicity is, then, of paramount importance to expedite the introduction of new therapies to clinical practice. To date, most transcriptomics efforts to solve this problem have applied Support Vector Machine techniques to data derived from in vivo studies in rats. Results: We applied a toxicogenomics approach to determine whether known renal toxicants could be identified as such from their effects on the transcriptome of the human renal proximal tubular epithelial cell line, HK-2. Based on clustering of differentially expressed genes, we identified 3 toxicity groups within the set of compounds. We used Random Forest to generate a classifier to accurately place compounds in groups. The classifier is based on a signature biomarker comprising 21 genes identified by Random Forest and could differentiate between the groups with high accuracy. Furthermore, we could correctly classify external samples from a dataset exhibiting a marked ‘batch effect’. Conclusions: No toxicity-associated gene expression alterations could be identified across a set of toxic compounds. Random Forest is a suitable technique for the classification of compounds into toxicity groups. Using a measure of differential expression rather than expression level per se generates a robust classifier that can potentially be applied in a platform-independent manner.
2015-12-31 | GSE27209 | GEO
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2015-12-31 | GSE27208 | GEO
Project description:Background: The development of a new drug from candidate to market is a complex process requiring vast resources of time, money and personnel. The rate of failure in the development pipeline is enormous, leading to wasted resources that could have been better employed on alternative candidates. The requirement for early stage prediction of toxicity is, then, of paramount importance to expedite the introduction of new therapies to clinical practice. To date, most transcriptomics efforts to solve this problem have applied Support Vector Machine techniques to data derived from in vivo studies in rats. Results: We applied a toxicogenomics approach to determine whether known renal toxicants could be identified as such from their effects on the transcriptome of the human renal proximal tubular epithelial cell line, HK-2. Based on clustering of differentially expressed genes, we identified 3 toxicity groups within the set of compounds. We used Random Forest to generate a classifier to accurately place compounds in groups. The classifier is based on a signature biomarker comprising 21 genes identified by Random Forest and could differentiate between the groups with high accuracy. Furthermore, we could correctly classify external samples from a dataset exhibiting a marked ‘batch effect’. Conclusions: No toxicity-associated gene expression alterations could be identified across a set of toxic compounds. Random Forest is a suitable technique for the classification of compounds into toxicity groups. Using a measure of differential expression rather than expression level per se generates a robust classifier that can potentially be applied in a platform-independent manner.
2015-12-31 | GSE27204 | GEO
Project description:Background: The development of a new drug from candidate to market is a complex process requiring vast resources of time, money and personnel. The rate of failure in the development pipeline is enormous, leading to wasted resources that could have been better employed on alternative candidates. The requirement for early stage prediction of toxicity is, then, of paramount importance to expedite the introduction of new therapies to clinical practice. To date, most transcriptomics efforts to solve this problem have applied Support Vector Machine techniques to data derived from in vivo studies in rats. Results: We applied a toxicogenomics approach to determine whether known renal toxicants could be identified as such from their effects on the transcriptome of the human renal proximal tubular epithelial cell line, HK-2. Based on clustering of differentially expressed genes, we identified 3 toxicity groups within the set of compounds. We used Random Forest to generate a classifier to accurately place compounds in groups. The classifier is based on a signature biomarker comprising 21 genes identified by Random Forest and could differentiate between the groups with high accuracy. Furthermore, we could correctly classify external samples from a dataset exhibiting a marked ‘batch effect’. Conclusions: No toxicity-associated gene expression alterations could be identified across a set of toxic compounds. Random Forest is a suitable technique for the classification of compounds into toxicity groups. Using a measure of differential expression rather than expression level per se generates a robust classifier that can potentially be applied in a platform-independent manner.
2015-12-31 | GSE27202 | GEO
Project description:Background: The development of a new drug from candidate to market is a complex process requiring vast resources of time, money and personnel. The rate of failure in the development pipeline is enormous, leading to wasted resources that could have been better employed on alternative candidates. The requirement for early stage prediction of toxicity is, then, of paramount importance to expedite the introduction of new therapies to clinical practice. To date, most transcriptomics efforts to solve this problem have applied Support Vector Machine techniques to data derived from in vivo studies in rats. Results: We applied a toxicogenomics approach to determine whether known renal toxicants could be identified as such from their effects on the transcriptome of the human renal proximal tubular epithelial cell line, HK-2. Based on clustering of differentially expressed genes, we identified 3 toxicity groups within the set of compounds. We used Random Forest to generate a classifier to accurately place compounds in groups. The classifier is based on a signature biomarker comprising 21 genes identified by Random Forest and could differentiate between the groups with high accuracy. Furthermore, we could correctly classify external samples from a dataset exhibiting a marked ‘batch effect’. Conclusions: No toxicity-associated gene expression alterations could be identified across a set of toxic compounds. Random Forest is a suitable technique for the classification of compounds into toxicity groups. Using a measure of differential expression rather than expression level per se generates a robust classifier that can potentially be applied in a platform-independent manner.
2015-12-31 | GSE27198 | GEO