The high-dimensionality of the statistical models and the plurality of methods considered mean that, at the level of the literature, machine-learning techniques probably give rise to more variability, although they do come with more expressiveness and power.
a matrix of predictions of gene-disease relationships based on known relationships mined from the literature and machine learning predictions [29].
In the literature, different machine learning solutions have been applied to the recognition of activities including Naive Bayes [3], Decision Trees [4], Support Vector Machines (SVMs) [5], Deep Neural Networks [6] and Hidden Markov Models (HMMs) [7].
Table 8 summarises the available literature on machine learning algorithms for binary (I123 FP-CIT classI123 FP-CITi.e. normal vs abnormal image appearanclassification0, listed in order of reported maximum accuracy figures, where available.e
Use of ontologies is not limited to the annotation of databases [ 32]: ontologies have provided a significant contribution to high-throughput data analysis and increasingly are seen as a device to make scientific literature more machine processable.
The small proportion of Open Access (OA) in chemistry means that it is very uncommon for scientists to extract information from the literature using machines and indeed many publishers expressly forbid this.
A key part of the chemical sciences are the reactions that chemists perform and report in great number, and the goal of PatentEye is to demonstrate the potential to create an automated system capable of extracting reactions from the literature, creating machine-understandable representations using CML and sharing them as Open Data.
Unlike most passive vibration absorbers proposed in the literature for suppressing machine tool vibrations, the NLTVA comprises both a linear and a nonlinear restoring force.
Till date, many studies have been reported in the literature on solving machine tool selection problems using diverse multi-criteria decision-making (MCDM) methods, mathematical models and knowledge-based systems.
Many groups combine literature information and machine learning to build network models for disease.
However, these terms are still represented in the corpus of irrelevant literature, so a machine learning approach to classification is highly likely to outperform a simple keyword search.
