### Abstract

The emergence of high-throughput technologies for measuring biological systems has introduced problems for data interpretation that must be addressed for proper inference. First, analysis techniques need to be matched to the biological system, reflecting in their mathematical structure the underlying behavior being studied. When this is not done, mathematical techniques will generate answers, but the values and reliability estimatesmay not accurately reflect the biology. Second, analysis approaches must address the vast excess in variables measured (e.g. transcript levels of genes) over the number of samples (e.g. tumors, time points), known as the 'large-p, small-n' problem. In large-p, small-n paradigms, standard statistical techniques generally fail, and computational learning algorithms are prone to overfit the data. Here we review the emergence of techniques that match mathematical structure to the biology, the use of integrated data and prior knowledge to guide statistical analysis, and the recent emergence of analysis approaches utilizing simple biological models.We show that novel biological insights have been gained using these techniques.

Original language | English (US) |
---|---|

Article number | bbp044 |

Pages (from-to) | 30-39 |

Number of pages | 10 |

Journal | Briefings in Bioinformatics |

Volume | 11 |

Issue number | 1 |

DOIs | |

State | Published - Oct 23 2009 |

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### Keywords

- Bayesian analysis
- Computational molecular biology
- Databases
- Metabolic pathways
- Signal pathways

### ASJC Scopus subject areas

- Molecular Biology
- Information Systems

### Cite this

*Briefings in Bioinformatics*,

*11*(1), 30-39. [bbp044]. https://doi.org/10.1093/bib/bbp044

**Knowledge-based data analysis comes of age.** / Ochs, Michael F.

Research output: Contribution to journal › Article

*Briefings in Bioinformatics*, vol. 11, no. 1, bbp044, pp. 30-39. https://doi.org/10.1093/bib/bbp044

}

TY - JOUR

T1 - Knowledge-based data analysis comes of age

AU - Ochs, Michael F.

PY - 2009/10/23

Y1 - 2009/10/23

N2 - The emergence of high-throughput technologies for measuring biological systems has introduced problems for data interpretation that must be addressed for proper inference. First, analysis techniques need to be matched to the biological system, reflecting in their mathematical structure the underlying behavior being studied. When this is not done, mathematical techniques will generate answers, but the values and reliability estimatesmay not accurately reflect the biology. Second, analysis approaches must address the vast excess in variables measured (e.g. transcript levels of genes) over the number of samples (e.g. tumors, time points), known as the 'large-p, small-n' problem. In large-p, small-n paradigms, standard statistical techniques generally fail, and computational learning algorithms are prone to overfit the data. Here we review the emergence of techniques that match mathematical structure to the biology, the use of integrated data and prior knowledge to guide statistical analysis, and the recent emergence of analysis approaches utilizing simple biological models.We show that novel biological insights have been gained using these techniques.

AB - The emergence of high-throughput technologies for measuring biological systems has introduced problems for data interpretation that must be addressed for proper inference. First, analysis techniques need to be matched to the biological system, reflecting in their mathematical structure the underlying behavior being studied. When this is not done, mathematical techniques will generate answers, but the values and reliability estimatesmay not accurately reflect the biology. Second, analysis approaches must address the vast excess in variables measured (e.g. transcript levels of genes) over the number of samples (e.g. tumors, time points), known as the 'large-p, small-n' problem. In large-p, small-n paradigms, standard statistical techniques generally fail, and computational learning algorithms are prone to overfit the data. Here we review the emergence of techniques that match mathematical structure to the biology, the use of integrated data and prior knowledge to guide statistical analysis, and the recent emergence of analysis approaches utilizing simple biological models.We show that novel biological insights have been gained using these techniques.

KW - Bayesian analysis

KW - Computational molecular biology

KW - Databases

KW - Metabolic pathways

KW - Signal pathways

UR - http://www.scopus.com/inward/record.url?scp=77950344077&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77950344077&partnerID=8YFLogxK

U2 - 10.1093/bib/bbp044

DO - 10.1093/bib/bbp044

M3 - Article

VL - 11

SP - 30

EP - 39

JO - Briefings in Bioinformatics

JF - Briefings in Bioinformatics

SN - 1467-5463

IS - 1

M1 - bbp044

ER -