RECORD
Predicting Genetic Regulatory Response using Classification: Yeast Stress Response
Quantitative Methods · Submitted 7 Jun 2004
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim In computational experiments based on the Gasch S. cerevisiae dataset, we show that the GeneClass method predicts up- and down-regulation on held-out experiments with high accuracy.
The compiled record
The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.
Open the compiled record
The abstract, sentence by sentence
We present a novel classification-based algorithm called GeneClass for learning to predict gene regulatory response. Our approach is motivated by the hypothesis that in simple organisms such as Saccharomyces cerevisiae, we can learn a decision rule for predicting whether a gene is up- or down-regulated in a particular experiment based on (1) the presence of binding site subsequences (``motifs'') in the gene's regulatory region and (2) the expression levels of regulators such as transcription factors in the experiment (``parents''). Thus our learning task integrates two qualitatively different data sources: genome-wide cDNA microarray data across multiple perturbation and mutant experiments along with motif profile data from regulatory sequences. Rather than focusing on the regression task of predicting real-valued gene expression measurements, GeneClass performs the classification task of predicting +1 and -1 labels, corresponding to up- and down-regulation beyond the levels of biological and measurement noise in microarray measurements. GeneClass uses the Adaboost learning algorithm with a margin-based generalization of decision trees called alternating decision trees. In computational experiments based on the Gasch S. cerevisiae dataset, we show that the GeneClass method predicts up- and down-regulation on held-out experiments with high accuracy. We explore a range of experimental setups related to environmental stress response, and we retrieve important regulators, binding site motifs, and relationships between regulators and binding sites that are known to be associated to specific stress response pathways. Our method thus provides predictive hypotheses, suggests biological experiments, and provides interpretable insight into the structure of genetic regulatory networks.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | We present a novel classification-based algorithm called GeneClass for learning to predict gene regulatory response. | contribution 1.00 | 0.94 | 0.07 | asserted 1.00 |
| s1 | Our approach is motivated by the hypothesis that in simple organisms such as Saccharomyces cerevisiae, we can learn a decision rule for predicting whether a gene is up- or down-regulated in a particular experiment based on (1) the presence of binding site subsequences (``motifs'') in the gene's regulatory region and (2) the expression levels of regulators such as transcription factors in the experiment (``parents''). | method 0.69 | 0.21 | 0.05 | hedged possibility 0.50 |
| s2 | Thus our learning task integrates two qualitatively different data sources: genome-wide cDNA microarray data across multiple perturbation and mutant experiments along with motif profile data from regulatory sequences. | method 0.98 | 0.70 | 0.03 | asserted 0.99 |
| s3 | Rather than focusing on the regression task of predicting real-valued gene expression measurements, GeneClass performs the classification task of predicting +1 and -1 labels, corresponding to up- and down-regulation beyond the levels of biological and measurement noise in microarray measurements. | method 0.92 | 0.85 | 0.06 | asserted 1.00 |
| s4 | GeneClass uses the Adaboost learning algorithm with a margin-based generalization of decision trees called alternating decision trees. | method 1.00 | 0.81 | 0.03 | asserted 1.00 |
| s5 | In computational experiments based on the Gasch S. cerevisiae dataset, we show that the GeneClass method predicts up- and down-regulation on held-out experiments with high accuracy. | result 1.00 | 0.97 | 0.57 | asserted 1.00 |
| s6 | We explore a range of experimental setups related to environmental stress response, and we retrieve important regulators, binding site motifs, and relationships between regulators and binding sites that are known to be associated to specific stress response pathways. | result 0.94 | 0.94 | 0.15 | asserted 1.00 |
| s7 | Our method thus provides predictive hypotheses, suggests biological experiments, and provides interpretable insight into the structure of genetic regulatory networks. | interpretation 0.97 | 0.82 | 0.56 | asserted 0.73 |
What was examined, and what was not
- arXiv listing abstract v2
- full text
- figures and tables
- references and citing sentences
- relations to other papers
- P abstract
- S sections
- T body sentences
- C citations
- R cross-paper relations
The paper
Original abstract
We present a novel classification-based algorithm called GeneClass for learning to predict gene regulatory response. Our approach is motivated by the hypothesis that in simple organisms such as Saccharomyces cerevisiae, we can learn a decision rule for predicting whether a gene is up- or down-regulated in a particular experiment based on (1) the presence of binding site subsequences (``motifs'') in the gene's regulatory region and (2) the expression levels of regulators such as transcription factors in the experiment (``parents''). Thus our learning task integrates two qualitatively different data sources: genome-wide cDNA microarray data across multiple perturbation and mutant experiments along with motif profile data from regulatory sequences. Rather than focusing on the regression task of predicting real-valued gene expression measurements, GeneClass performs the classification task of predicting +1 and -1 labels, corresponding to up- and down-regulation beyond the levels of biological and measurement noise in microarray measurements. GeneClass uses the Adaboost learning algorithm with a margin-based generalization of decision trees called alternating decision trees. In computational experiments based on the Gasch S. cerevisiae dataset, we show that the GeneClass method predicts up- and down-regulation on held-out experiments with high accuracy. We explore a range of experimental setups related to environmental stress response, and we retrieve important regulators, binding site motifs, and relationships between regulators and binding sites that are known to be associated to specific stress response pathways. Our method thus provides predictive hypotheses, suggests biological experiments, and provides interpretable insight into the structure of genetic regulatory networks.
Human review
Discussion
Know something about this paper? Leave a comment, or highlight any passage above to comment on it.