Gene Expression Model Selector (GEMS) is a system that constructs, in a supervised fashion, diagnostic and outcome prediction models from array gene expression data. Examples of such models are: (a) models that detect cancer, (b) models that determine the correct subtype of cancer or (c) models that predict survival after treatment. Models that support such complex decision making are widely recognized as having the potential to revolutionize medicine in the years to come. In addition to the decision support models, GEMS can be used to select a small number of genes that are as good or better than the full gene set for diagnosis and/or outcome prediction. These biomarkers (genes) are also useful for discovery purposes (e.g., they suggest plausible causes and treatments of various types of cancer). Finally, GEMS provides estimates of the models' performance (e.g., accuracy) in future applications (i.e., when applied on patients not used to build the models but who come from the same patient population as the ones used to build the models), and allows users to run the models for individual patients.
A method of determining a local causal neighborhood of a target variable from a data set can include identifying variables of the data set as candidates of the local causal neighborhood using statistical characteristics, and including the identified variables within a candidate set. False positive variables can be removed from the candidate set according to further statistical characteristics applied to each variable of the candidate set. The remaining variables of the candidate set can be identified as the local causal neighborhood of the target variable.
This technology enhances the capabilities of continuum robots by not only detecting contact during movement but also estimating the position of the contact during the movements executed by the robot. An algorithmic feedback loop can then constrain the movement of the robot to avoid damage to its robot arm, damage to another robot arm or damage to surrounding structure. Applications for this technology include enhanced safe telemanipulation for multi-arm continuum robots in surgery, micro-assembly in confined spaces, and exploration in unknown environments.
This technology enables continuum robots (aka snake robots) to precisely navigate the intricate structures of deep anatomical passages during minimally invasive or natural orifice surgery. Collateral surgical damage is minimized by the force sensing capabilities of the algorithms used.
TagDock is an efficient rigid body molecular docking algorithm that generates three-dimensional models of oligomeric biomolecular complexes in instances where there is limited experimental restraint data to guide the docking calculations. Through distance difference analysis TagDock additionally recommends followup experiments to further discriminate divergent (score-degenerate) clusters of TagDock's initial solution models