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Bioinformaticians are still in the early stages of developing methods to integrate high-throughput regulatory data, such as ChIP-Seq and RNA-Seq, with regulatory element prediction, variant calling, and databases of known regulatory elements and variants.
High quality guidelines developed at the international level can facilitate the development of locally appropriate guidelines for specific practice settings and, increasingly, local health providers are developing methods to integrate evidence in their own settings [ 24].
Developing methods to integrate individualized risk stratification within routine clinical care has the potential to remedy this paradoxical practice pattern by alerting clinicians to each patient's potential benefits from treatment and enabling more patient centered, evidence based, efficient care with safer, better outcomes.
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To overcome this lack of uniformity, it is important to develop methods to integrate EHRs to provide a multi-nation dataset of health.
In fact, dynamic models for macroscale integration are within reach, but there is a real need to develop methods to integrate microscale knowledge.
For models projecting the future of species distributions, especially that of plants [72], such rapid change underlines the importance of developing methods to explicitly integrate how fire activity affects vegetation, in addition to species range changes based on plant-climate relationships alone.
We have developed new methods to integrate efficiently reaction diffusion parabolic problems with nonlinear reaction terms.
Prof. Pe'er's lab develops computational methods to integrate diverse high throughput data and unravel a holistic systems level view of the cell in an attempt to answer such questions as: How does this calculation differ between cell-type, individual and species?
The overarching aim of the management initiative has been to develop hybrid methods to integrate ecological knowledge with scientific approaches such as underwater visual surveys for designing marine protected areas (MPAs).
Often, it is the interaction of many genes that causes phenotypic variations.In this work, using cancer as an example, we develop graph-based methods to integrate multiple microarray datasets to discover disease-related co-expression network modules.
In this work, using cancer as an example, we develop graph-based methods to integrate multiple microarray datasets to discover disease-related co-expression network modules.
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