AIMC Topic: Machine Learning

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Determinable and interpretable network representation for link prediction.

Scientific reports
As an intuitive description of complex physical, social, or brain systems, complex networks have fascinated scientists for decades. Recently, to abstract a network's topological and dynamical attributes, network representation has been a prevalent te...

Flexible learning of quantum states with generative query neural networks.

Nature communications
Deep neural networks are a powerful tool for characterizing quantum states. Existing networks are typically trained with experimental data gathered from the quantum state that needs to be characterized. But is it possible to train a neural network of...

High throughput screening of mesenchymal stem cell lines using deep learning.

Scientific reports
Mesenchymal stem cells (MSCs) are increasingly used as regenerative therapies for patients in the preclinical and clinical phases of various diseases. However, the main limitations of such therapies include functional heterogeneity and the lack of ap...

Technology readiness levels for machine learning systems.

Nature communications
The development and deployment of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. Lack of diligence can lead to technical debt, scope creep and misaligned objectives, model m...

Combining mass spectrometry and machine learning to discover bioactive peptides.

Nature communications
Peptides play important roles in regulating biological processes and form the basis of a multiplicity of therapeutic drugs. To date, only about 300 peptides in human have confirmed bioactivity, although tens of thousands have been reported in the lit...

Human knowledge models: Learning applied knowledge from the data.

PloS one
Artificial intelligence and machine learning have demonstrated remarkable results in science and applied work. However, present AI models, developed to be run on computers but used in human-driven applications, create a visible disconnect between AI ...

Artificial intelligence, machine learning, and deep learning in rhinology: a systematic review.

European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery
PURPOSE: This PRISMA-compliant systematic review aims to analyze the existing applications of artificial intelligence (AI), machine learning, and deep learning for rhinological purposes and compare works in terms of data pool size, AI systems, input ...

Machine Learning to Predict Homolytic Dissociation Energies of C-H Bonds: Calibration of DFT-based Models with Experimental Data.

Molecular informatics
Random Forest (RF) QSPR models were developed with a data set of homolytic bond dissociation energies (BDE) previously calculated by B3LYP/6-311++G(d,p)//DFTB for 2263 sp3C-H covalent bonds. The best set of attributes consisted in 114 descriptors of ...

A deep learning based model using RNN-LSTM for the Detection of Schizophrenia from EEG data.

Computers in biology and medicine
Normal life can be ensured for schizophrenic patients if diagnosed early. Electroencephalogram (EEG) carries information about the brain network connectivity which can be used to detect brain anomalies that are indicative of schizophrenia. Since deep...

Deep Learning: Predicting Environments From Short-Time Observations of Postural Balance.

IEEE transactions on bio-medical engineering
OBJECTIVE: This study introduces a deep learning approach to accurately predict challenging mechanical environments that possibly cause decreasing postural stability.