AI Medical Compendium Topic:
Software

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Enhancing Text Generation via Parse Tree Embedding.

Computational intelligence and neuroscience
Natural language generation (NLG) is a core component of machine translation, dialogue systems, speech recognition, summarization, and so forth. The existing text generation methods tend to be based on recurrent neural language models (NLMs), which g...

Contrastive representation learning of inorganic materials to overcome lack of training datasets.

Chemical communications (Cambridge, England)
Data representation forms a feature space where forms data distribution that is one of the key factors determining the prediction accuracy of machine learning (ML). In particular, the data representation is crucial to handle small and biased training...

Critical features identification for chemical chronic toxicity based on mechanistic forecast models.

Environmental pollution (Barking, Essex : 1987)
Facing billions of tons of pollutants entering the ocean each year, aquatic toxicity is becoming a crucial endpoint for evaluating chemical adverse effects on ecosystems. Notably, huge amount of toxic chemicals at environmental relevant doses can cau...

Self-Supervised Video Representation Learning by Uncovering Spatio-Temporal Statistics.

IEEE transactions on pattern analysis and machine intelligence
This paper proposes a novel pretext task to address the self-supervised video representation learning problem. Specifically, given an unlabeled video clip, we compute a series of spatio-temporal statistical summaries, such as the spatial location and...

GaitSet: Cross-View Gait Recognition Through Utilizing Gait As a Deep Set.

IEEE transactions on pattern analysis and machine intelligence
Gait is a unique biometric feature that can be recognized at a distance; thus, it has broad applications in crime prevention, forensic identification, and social security. To portray a gait, existing gait recognition methods utilize either a gait tem...

Edge-Computing and Machine-Learning-Based Framework for Software Sensor Development.

Sensors (Basel, Switzerland)
The present research presents a framework that supports the development and operation of machine-learning (ML) algorithms to develop, maintain and manage the whole lifecycle of modeling software sensors related to complex chemical processes. Our moti...

A new active learning approach for adsorbate-substrate structural elucidation in silico.

Journal of molecular modeling
Adsorbate interactions with substrates (e.g. surfaces and nanoparticles) are fundamental for several technologies, such as functional materials, supramolecular chemistry, and solvent interactions. However, modeling these kinds of systems in silico, s...

DiaDeL: An Accurate Deep Learning-Based Model With Mutational Signatures for Predicting Metastasis Stage and Cancer Types.

IEEE/ACM transactions on computational biology and bioinformatics
Mutational signatures help identify cancer-associated genes that are being involved in tumorigenesis pathways. Hence, these pathways guide precision medicine approaches to find appropriate drugs and treatments. The pattern of mutations varies in diff...

Neural Network and Random Forest Models in Protein Function Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
Over the past decade, the demand for automated protein function prediction has increased due to the volume of newly sequenced proteins. In this paper, we address the function prediction task by developing an ensemble system automatically assigning Ge...

CapsProm: a capsule network for promoter prediction.

Computers in biology and medicine
Locating the promoter region in DNA sequences is of paramount importance in bioinformatics. This problem has been widely studied in the literature, but it has not yet been fully resolved. Some researchers have shown remarkable results using convoluti...