AI Medical Compendium Journal:
GigaScience

Showing 51 to 60 of 69 articles

Tool recommender system in Galaxy using deep learning.

GigaScience
BACKGROUND: Galaxy is a web-based and open-source scientific data-processing platform. Researchers compose pipelines in Galaxy to analyse scientific data. These pipelines, also known as workflows, can be complex and difficult to create from thousands...

Correcting for experiment-specific variability in expression compendia can remove underlying signals.

GigaScience
MOTIVATION: In the past two decades, scientists in different laboratories have assayed gene expression from millions of samples. These experiments can be combined into compendia and analyzed collectively to extract novel biological patterns. Technica...

Imputing missing RNA-sequencing data from DNA methylation by using a transfer learning-based neural network.

GigaScience
BACKGROUND: Gene expression plays a key intermediate role in linking molecular features at the DNA level and phenotype. However, owing to various limitations in experiments, the RNA-seq data are missing in many samples while there exist high-quality ...

Multi-dimensional machine learning approaches for fruit shape phenotyping in strawberry.

GigaScience
BACKGROUND: Shape is a critical element of the visual appeal of strawberry fruit and is influenced by both genetic and non-genetic determinants. Current fruit phenotyping approaches for external characteristics in strawberry often rely on the human e...

ShinyLearner: A containerized benchmarking tool for machine-learning classification of tabular data.

GigaScience
BACKGROUND: Classification algorithms assign observations to groups based on patterns in data. The machine-learning community have developed myriad classification algorithms, which are used in diverse life science research domains. Algorithm choice c...

DeepPod: a convolutional neural network based quantification of fruit number in Arabidopsis.

GigaScience
BACKGROUND: High-throughput phenotyping based on non-destructive imaging has great potential in plant biology and breeding programs. However, efficient feature extraction and quantification from image data remains a bottleneck that needs to be addres...

Artificial intelligence deciphers codes for color and odor perceptions based on large-scale chemoinformatic data.

GigaScience
BACKGROUND: Color vision is the ability to detect, distinguish, and analyze the wavelength distributions of light independent of the total intensity. It mediates the interaction between an organism and its environment from multiple important aspects....

High-throughput phenotyping with deep learning gives insight into the genetic architecture of flowering time in wheat.

GigaScience
BACKGROUND: Measurement of plant traits with precision and speed on large populations has emerged as a critical bottleneck in connecting genotype to phenotype in genetics and breeding. This bottleneck limits advancements in understanding plant genome...

Deep learning for clustering of multivariate clinical patient trajectories with missing values.

GigaScience
BACKGROUND: Precision medicine requires a stratification of patients by disease presentation that is sufficiently informative to allow for selecting treatments on a per-patient basis. For many diseases, such as neurological disorders, this stratifica...

RootNav 2.0: Deep learning for automatic navigation of complex plant root architectures.

GigaScience
BACKGROUND: In recent years quantitative analysis of root growth has become increasingly important as a way to explore the influence of abiotic stress such as high temperature and drought on a plant's ability to take up water and nutrients. Segmentat...