Results 51 to 60 of about 404,815 (264)
Directed evolution of enzymes at the crossroads of tradition and innovation
An iterative cycle of data‐driven enzyme optimization comprising four stages: genetic diversification of a template enzyme, expression of protein variants, high‐throughput evaluation, and machine‐learning‐guided redesign of the next variant library.
Maria Tomkova +2 more
wiley +1 more source
Random-effects model forest plots showing the impact of allogeneic BT on the survival of PCa patients after RP. [PDF]
Random-effects model forest plots showing the impact of allogeneic BT on the survival of PCa patients after RP.
Su-Liang Li (3704542) +2 more
core +1 more source
Random forests are a type of ensemble method which makes predictions by combining the results of several independent trees. However, the theory of random forests has long been outpaced by their application. In this paper, we propose a novel random forests algorithm based on cooperative game theory.
Jianyuan Sun +3 more
openaire +2 more sources
Decision Trees (DTs) and Random Forests (RFs) are powerful discriminative learners and tools of central importance to the everyday machine learning practitioner and data scientist. Due to their discriminative nature, however, they lack principled methods to process inputs with missing features or to detect outliers, which requires pairing them with ...
Alvaro H. C. Correia +2 more
openaire +4 more sources
Elevated Connectivity During Language Processing Is Associated With Cognitive Performance in SeLECTS
ABSTRACT Objective Self‐Limited Epilepsy with Centrotemporal Spikes (SeLECTS) is associated with language impairments despite seizures originating in the motor cortex, suggesting aberrant cross‐network interactions. Here we tested whether functional connectivity in SeLECTS during language tasks predicts language performance.
Wendy Qi +8 more
wiley +1 more source
Suppose that rooted forests (in which the edges in each tree are directed away from the root of the tree) are formed by starting with a set of \(n\) labelled vertices and succesively adding an edge \(uv\) from a randomly chosen vertex \(u\) to the root \(v\) of a randomly chosen tree not containing \(u\). The author derives several enumeration formulae
openaire +1 more source
Abstract Although the random forest classification procedure works well in datasets with many features, when the number of features is huge and the percentage of truly informative features is small, such as with DNA microarray data, its performance tends to decline significantly.
Dhammika Amaratunga +2 more
openaire +2 more sources
ABSTRACT Background and Purpose White matter hyperintensities (WMH) are a core neuroimaging marker of cerebral small vessel disease (CSVD). Sleep apnoea (SA) is a recognized vascular risk factor, but its associations with regional WMH burden, short‐interval WMH change and cognitive performance in population‐based cohorts remain incompletely defined. We
Peng Cheng +4 more
wiley +1 more source
A New Forest Growing Stock Volume Estimation Model Based on AdaBoost and Random Forest Model [PDF]
Forest growing stock volume is a crucial indicator for assessing forest resources. However, contemporary machine learning models used in estimating forest growing stock volume often exhibit fluctuating precision and are confined to specific tree species,
Weiheng Xu +4 more
core +1 more source
ABSTRACT Introduction Glucagon‐like peptide‐1 receptor agonists (GLP‐1 RAs) have demonstrated significant weight‐reducing effects and may offer benefits in idiopathic intracranial hypertension (IIH); however, recent concerns about the risk of non‐arteritic anterior ischemic optic neuropathy (NAION) have emerged.
Faisal A. Al‐Harbi +9 more
wiley +1 more source

