AI Medical Compendium Topic

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Markov Chains

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Compressing neural networks via formal methods.

Neural networks : the official journal of the International Neural Network Society
Advancements in Neural Networks have led to larger models, challenging implementation on embedded devices with memory, battery, and computational constraints. Consequently, network compression has flourished, offering solutions to reduce operations a...

NPEX: Never give up protein exploration with deep reinforcement learning.

Journal of molecular graphics & modelling
Elucidating unknown structures of proteins, such as metastable states, is critical in designing therapeutic agents. Protein structure exploration has been performed using advanced computational methods, especially molecular dynamics and Markov chain ...

Effective training of nanopore callers for epigenetic marks with limited labelled data.

Open biology
Nanopore sequencing platforms combined with supervised machine learning (ML) have been effective at detecting base modifications in DNA such as 5-methylcytosine (5mC) and N6-methyladenine (6mA). These ML-based nanopore callers have typically been tra...

A Kernelized Classification Approach for Cancer Recognition Using Markovian Analysis of DNA Structure Patterns as Feature Mining.

Cell biochemistry and biophysics
Nucleotide-based molecules called DNA and RNA are essential for several biological processes that affect both normal and cancerous cells. They contain the critical genetic material needed for normal cell growth and functioning. The DNA structure patt...

Protocol-based control for semi-Markov reaction-diffusion neural networks.

Neural networks : the official journal of the International Neural Network Society
This paper addresses the asynchronous control problem for semi-Markov reaction-diffusion neural networks (SMRDNNs) under probabilistic event-triggered protocol (PETP) scheduling. A semi-Markov process with a deterministic switching rule is introduced...

Deep network embedding with dimension selection.

Neural networks : the official journal of the International Neural Network Society
Network embedding is a general-purpose machine learning technique that converts network data from non-Euclidean space to Euclidean space, facilitating downstream analyses for the networks. However, existing embedding methods are often optimization-ba...

RevGraphVAMP: A protein molecular simulation analysis model combining graph convolutional neural networks and physical constraints.

Methods (San Diego, Calif.)
Molecular dynamics simulation is a crucial research domain within the life sciences, focusing on comprehending the mechanisms of biomolecular interactions at atomic scales. Protein simulation, as a critical subfield, often utilizes MD for implementat...

Trajectory Analysis in Single-Particle Tracking: From Mean Squared Displacement to Machine Learning Approaches.

International journal of molecular sciences
Single-particle tracking is a powerful technique to investigate the motion of molecules or particles. Here, we review the methods for analyzing the reconstructed trajectories, a fundamental step for deciphering the underlying mechanisms driving the m...

Method for remaining useful life prediction of rolling bearings based on deep reinforcement learning.

The Review of scientific instruments
In contemporary industrial systems, the prediction of remaining useful life (RUL) is recognized as a valuable maintenance strategy for health management due to its ability to monitor equipment operational status in real time and ensure the safety of ...

Understanding Learning from EEG Data: Combining Machine Learning and Feature Engineering Based on Hidden Markov Models and Mixed Models.

Neuroinformatics
Theta oscillations, ranging from 4-8 Hz, play a significant role in spatial learning and memory functions during navigation tasks. Frontal theta oscillations are thought to play an important role in spatial navigation and memory. Electroencephalograp...