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Learning from crowds in digital pathology using scalable variational Gaussian processes.
Scientific reports
Jun 2, 2021
The volume of labeled data is often the primary determinant of success in developing machine learning algorithms. This has increased interest in methods for leveraging crowds to scale data labeling efforts, and methods to learn from noisy crowd-sourc...
MetaVelvet-DL: a MetaVelvet deep learning extension for de novo metagenome assembly.
BMC bioinformatics
Jun 2, 2021
BACKGROUND: The increasing use of whole metagenome sequencing has spurred the need to improve de novo assemblers to facilitate the discovery of unknown species and the analysis of their genomic functions. MetaVelvet-SL is a short-read de novo metagen...
coupleCoC+: An information-theoretic co-clustering-based transfer learning framework for the integrative analysis of single-cell genomic data.
PLoS computational biology
Jun 2, 2021
Technological advances have enabled us to profile multiple molecular layers at unprecedented single-cell resolution and the available datasets from multiple samples or domains are growing. These datasets, including scRNA-seq data, scATAC-seq data and...
Utilizing Whole Slide Images for the Primary Evaluation and Peer Review of a GLP-Compliant Rodent Toxicology Study.
Toxicologic pathology
Jun 1, 2021
The approach undertaken to deliver a Good Laboratory Practice (GLP) validation of whole slide images (WSIs) and the associated workflow for the digital primary evaluation and peer review of a GLP-compliant rodent inhalation toxicity study is describe...
Galaxy-ML: An accessible, reproducible, and scalable machine learning toolkit for biomedicine.
PLoS computational biology
Jun 1, 2021
Supervised machine learning is an essential but difficult to use approach in biomedical data analysis. The Galaxy-ML toolkit (https://galaxyproject.org/community/machine-learning/) makes supervised machine learning more accessible to biomedical scien...
Converting tabular data into images for deep learning with convolutional neural networks.
Scientific reports
May 31, 2021
Convolutional neural networks (CNNs) have been successfully used in many applications where important information about data is embedded in the order of features, such as speech and imaging. However, most tabular data do not assume a spatial relation...
SPASOS 1.1: a program for the inference of ancestral shape ontogenies.
Cladistics : the international journal of the Willi Hennig Society
May 28, 2021
We recently published a method to infer ancestral landmark-based shape ontogenies that takes into account the possible existence of changes in developmental timing. Here we describe SPASOS, a software to perform that analysis. SPASOS is an open-sourc...
Protein Structure Prediction: Conventional and Deep Learning Perspectives.
The protein journal
May 28, 2021
Protein structure prediction is a way to bridge the sequence-structure gap, one of the main challenges in computational biology and chemistry. Predicting any protein's accurate structure is of paramount importance for the scientific community, as the...
Predicting Infrared Spectra with Message Passing Neural Networks.
Journal of chemical information and modeling
May 28, 2021
Infrared (IR) spectroscopy remains an important tool for chemical characterization and identification. Chemprop-IR has been developed as a software package for the prediction of IR spectra through the use of machine learning. This work serves the dua...
Enhancing CRISPR-Cas9 gRNA efficiency prediction by data integration and deep learning.
Nature communications
May 28, 2021
The design of CRISPR gRNAs requires accurate on-target efficiency predictions, which demand high-quality gRNA activity data and efficient modeling. To advance, we here report on the generation of on-target gRNA activity data for 10,592 SpCas9 gRNAs. ...
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