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Integration by parts is a fundamental technique in calculus for evaluating integrals involving the product of two functions. It is particularly useful when direct integration is not feasible. The method is based on the product rule for differentiation, which states that the derivative of a product equals the derivative of the first function times the second, plus the first function times the derivative of the second. By integrating this identity and rearranging terms, the integration by parts...
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Definite integrals involving the product of two functions over a fixed interval can be evaluated using integration by parts. This method rewrites the integral as the difference of a product evaluated at the endpoints and a remaining definite integral that is often simpler to compute.A representative example is the definite integral of the inverse tangent function. Since there is no direct integration formula for arctan ⁡x, the integrand is rewritten as a product of arctan⁡ x and the...
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A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
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Towards a data-integrated cell.

Noël Malod-Dognin1,2, Julia Petschnigg1, Sam F L Windels1

  • 1Department of Computer Science, University College London, London, WC1E 6BT, UK.

Nature Communications
|February 20, 2019
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Summary
This summary is machine-generated.

We developed an integrated cell (iCell) model to unify diverse molecular data, identifying novel cancer genes. This approach reveals previously unknown genes crucial for cancer progression and patient survival.

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Area of Science:

  • Computational biology
  • Genomics
  • Systems biology

Background:

  • Growing volume of molecular data necessitates integrated analysis frameworks.
  • Current methods struggle to unify diverse omics data for comprehensive cell understanding.

Purpose of the Study:

  • To propose a novel data-driven concept and computational prototype for an integrated cell (iCell).
  • To identify novel cancer-associated genes by integrating multi-omics data and molecular networks.
  • To biologically validate the role of identified genes in cancer and assess their prognostic value.

Main Methods:

  • Integration of three omics data types with tissue-specific molecular interaction networks.
  • Construction of iCells for four cancer types and their corresponding tissue controls.
  • Identification of highly rewired genes in cancer, followed by functional validation using knockdown and cell viability assays.
  • Survival analysis using Kaplan-Meier curves for thousands of patients.

Main Results:

  • Identified numerous highly rewired genes in cancer, many with unknown functions and not detectable by single-network analysis.
  • Biologically validated the role of these novel genes in cancer progression via knockdown experiments.
  • Confirmed the prognostic significance of identified genes through patient survival data.
  • Extended the methodology to uncover pan-cancer genes.

Conclusions:

  • The iCell concept provides a unified framework for integrating multi-omics data, enabling novel biological discoveries.
  • This data-driven approach successfully identifies and validates previously unrecognized genes critical for cancer.
  • The methodology is versatile, facilitating integrative comparisons across diverse cell types and tissues for broader applications.