To propagate well-based compaction information into the data assessment, two compaction recovery methods that integrate well and seismic data are proposed.
In this context, computational methods that integrate the knowledge of proteins and drug responses were utilized in prioritizing targets and designing drugs towards clinical trials with better efficacy.
Several methods that integrate de novo assembly with genome-based assembly have been proposed.
R-SVM represents a category of methods that integrate the feature selection and classification steps in one "wrapper" method.
The latter method is ∼70% accurate when predicting secondary structure from a single sequence, but prediction methods that integrate sequence comparison typically give better than 85% average accuracy.
With the increasing availability and quality of genome-scale metabolic models and high-throughput data, constraint-based methods that integrate these data have found broad applications.
A future challenge will be to develop methods that integrate these approaches systematically and programmatically, as other cases like these undoubtedly await.
Methods that integrate multiple sources of information (expression levels, biological annotation, protein levels etc) [ 16- 18] are promising but face difficulty in capturing and integrating all the relevant biological information, and their complexity can be prohibitive for the biologist user.
These tools can be generally divided into two classes, methods that integrate the information of sequence conservation across multiple genomes and those that do not take this into account.
Enhanced recovery after surgery (ERAS) is a combination of various perioperative patient care methods that integrate evidence-based interventions which reduce surgical stress, maintain the postoperative physiological function and accelerate recovery in patients undergoing major surgery [ 1].
Therefore, the best results are obtained by combination methods that integrate all available molecular-phasing evidence and genetic haplotyping where the samples are available, and supplement these with population inference as needed.
