Recently, Rothschild et al. (2018) investigated the association between gut microbiota, environmental factors and genetic features, and demonstrated that gut microbiota community structure was predominantly shaped by environmental factors rather than genetic ancestry or individual single nucleotide polymorphisms (SNPs).
For example, a study by Lee and colleagues [ 14] proposed a prediction method of normal weight and overweight status based on BMI using facial features and demonstrated that normal and overweight females could be classified using only facial features.
Meinel et al. [ 20] analysed the kinetic data and a limited set of morphological features, and demonstrated that providing these features to radiologists may enhance their diagnostic performance, regardless of their experience level.
Ortiz et al. [102] used the SVM classifier to verify the performance of three different feature extraction methods, including PCA, learning vector quantization (LVQ), and voxels as features (VAF) and demonstrated that LVQ features could generate the best result.
Domain experts have two major advantages over novices with regard to problem solving: experts more accurately encode deep problem features (feature encoding) and demonstrate better conceptual understanding of critical problem features (feature knowledge).
Borevitz et al. [ 18] coined the term "single feature polymorphism" and demonstrated that this approach can be applied to organisms with somewhat larger genomes, specifically Arabidopsis thaliana with a genome size of 140 Mb.
Our results present a complex picture regarding the polarity of facial features and demonstrate that some modern human-like facial morphology is intermittently present in Middle Pleistocene humans.
Determining the most effective combination of parameters, adjusting them according to the landscape features, and demonstrating how they change as a sequence evolves characterize a proactive evolutionary strategy.
This value is about 60% of that obtained with a commercial platinum foil electrode, which is a notable feature and demonstrates the potential of LSM as an alternative low cost cathode for DBFCs.
This paper briefly describes a general framework for the extraction and systematic storage of low-level visual features, and demonstrates its applicability in image categorization using a linear categorization algorithm originally developed for the characterization of text documents.
In this paper, we present an efficient algorithm that acquires representation knowledge in the form of "deep features", and demonstrate its effectiveness in the domain of algebra as well as synthetic domains.
