AIMC Topic: Software

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Application of deep learning image assessment software VeriSee™ for diabetic retinopathy screening.

Journal of the Formosan Medical Association = Taiwan yi zhi
PURPOSE: To develop a deep learning image assessment software VeriSee™ and to validate its accuracy in grading the severity of diabetic retinopathy (DR).

Revealing cytotoxic substructures in molecules using deep learning.

Journal of computer-aided molecular design
In drug development, late stage toxicity issues of a compound are the main cause of failure in clinical trials. In silico methods are therefore of high importance to guide the early design process to reduce time, costs and animal testing. Technical a...

ChemOS: An orchestration software to democratize autonomous discovery.

PloS one
The current Edisonian approach to discovery requires up to two decades of fundamental and applied research for materials technologies to reach the market. Such a slow and capital-intensive turnaround calls for disruptive strategies to expedite innova...

Active Intent Disambiguation for Shared Control Robots.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Assistive shared-control robots have the potential to transform the lives of millions of people afflicted with severe motor impairments. The usefulness of shared-control robots typically relies on the underlying autonomy's ability to infer the user's...

Evaluating severity of white matter lesions from computed tomography images with convolutional neural network.

Neuroradiology
PURPOSE: Severity of white matter lesion (WML) is typically evaluated on magnetic resonance images (MRI), yet the more accessible, faster, and less expensive method is computed tomography (CT). Our objective was to study whether WML can be automatica...

Robosample: A rigid-body molecular simulation program based on robot mechanics.

Biochimica et biophysica acta. General subjects
BACKGROUND: Compared with all-atom molecular dynamics (MD), constrained MD methods allow for larger time steps, potentially reducing computational cost. For this reason, there has been continued interest in improving constrained MD algorithms to incr...

Deep learning for survival outcomes.

Statistics in medicine
Deep learning is a class of machine learning algorithms that are popular for building risk prediction models. When observations are censored, the outcomes are only partially observed and standard deep learning algorithms cannot be directly applied. W...

FF-QuantSC: accurate quantification of fetal fraction by a neural network model.

Molecular genetics & genomic medicine
BACKGROUND: Noninvasive prenatal testing (NIPT) is one of the most commonly employed clinical measures for screening of fetal aneuploidy. Fetal Fraction (ff) has been demonstrated to be one of the key factors affecting the performance of NIPT. Accura...

DeLTA: Automated cell segmentation, tracking, and lineage reconstruction using deep learning.

PLoS computational biology
Microscopy image analysis is a major bottleneck in quantification of single-cell microscopy data, typically requiring human oversight and curation, which limit both accuracy and throughput. To address this, we developed a deep learning-based image an...

PredDBP-Stack: Prediction of DNA-Binding Proteins from HMM Profiles using a Stacked Ensemble Method.

BioMed research international
DNA-binding proteins (DBPs) play vital roles in all aspects of genetic activities. However, the identification of DBPs by using wet-lab experimental approaches is often time-consuming and laborious. In this study, we develop a novel computational met...