AI Medical Compendium Topic:
Supervised Machine Learning

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Advances in Predictions of Oral Bioavailability of Candidate Drugs in Man with New Machine Learning Methodology.

Molecules (Basel, Switzerland)
Oral bioavailability (F) is an essential determinant for the systemic exposure and dosing regimens of drug candidates. F is determined by numerous processes, and computational predictions of human estimates have so far shown limited results. We descr...

Autonomous Construction of Phase Diagrams of Block Copolymers by Theory-Assisted Active Machine Learning.

ACS macro letters
Equilibrium phase diagrams serve as blueprints for rational design of nanostructured materials of block copolymers, but their construction is time-consuming and requires profound expertise. Herein, by virtue of the knowledge of self-consistent field ...

Beyond Tripeptides Two-Step Active Machine Learning for Very Large Data sets.

Journal of chemical theory and computation
Self-assembling peptide nanostructures have been shown to be of great importance in nature and have presented many promising applications, for example, in medicine as drug-delivery vehicles, biosensors, and antivirals. Being very promising candidates...

Epistemic Autonomy: Self-supervised Learning in the Mammalian Hippocampus.

Trends in cognitive sciences
Biological cognition is based on the ability to autonomously acquire knowledge, or epistemic autonomy. Such self-supervision is largely absent in artificial neural networks (ANN) because they depend on externally set learning criteria. Yet training A...

Semi-Automated Data Processing and Semi-Supervised Machine Learning for the Detection and Classification of Water-Column Fish Schools and Gas Seeps with a Multibeam Echosounder.

Sensors (Basel, Switzerland)
Multibeam echosounders are widely used for 3D bathymetric mapping, and increasingly for water column studies. However, they rapidly collect huge volumes of data, which poses a challenge for water column data processing that is often still manual and ...

Study on Human Activity Recognition Using Semi-Supervised Active Transfer Learning.

Sensors (Basel, Switzerland)
In recent years, various studies have begun to use deep learning models to conduct research in the field of human activity recognition (HAR). However, there has been a severe lag in the absolute development of such models since training deep learning...

Uncertainty-aware temporal self-learning (UATS): Semi-supervised learning for segmentation of prostate zones and beyond.

Artificial intelligence in medicine
Various convolutional neural network (CNN) based concepts have been introduced for the prostate's automatic segmentation and its coarse subdivision into transition zone (TZ) and peripheral zone (PZ). However, when targeting a fine-grained segmentatio...

Protein Complexes Detection Based on Semi-Supervised Network Embedding Model.

IEEE/ACM transactions on computational biology and bioinformatics
A protein complex is a group of associated polypeptide chains which plays essential roles in the biological process. Given a graph representing protein-protein interactions (PPI) network, it is critical but non-trivial to detect protein complexes, th...

A Hybrid Supervised Approach to Human Population Identification Using Genomics Data.

IEEE/ACM transactions on computational biology and bioinformatics
Single nucleotide polymorphisms (SNPs) are one type of genetic variations and each SNP represents a difference in a single DNA building block, namely a nucleotide. Previous research demonstrated that SNPs can be used to identify the correct source po...