AIMC Topic: Markov Chains

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Incorporating sparse labels into hidden Markov models using weighted likelihoods improves accuracy and interpretability in biologging studies.

PloS one
Ecologists often use a hidden Markov model to decode a latent process, such as a sequence of an animal's behaviours, from an observed biologging time series. Modern technological devices such as video recorders and drones now allow researchers to dir...

Hidden Markov model for acoustic pesticide exposure detection and hive identification in stingless bees.

PloS one
Pollinator populations are declining globally at an unprecedented rate, driven by factors such as pathogens, habitat loss, climate change, and the widespread application of pesticides. Although colony losses remain difficult to prevent, precision bee...

Modeling eye gaze velocity trajectories using GANs with spectral loss for enhanced fidelity.

Scientific reports
Accurate modeling of eye gaze dynamics is essential for advancement in human-computer interaction, neurological diagnostics, and cognitive research. Traditional generative models like Markov models often fail to capture the complex temporal dependenc...

Evolutionary Dynamics and Functional Differences in Clinically Relevant Pen β-Lactamases from spp.

Journal of chemical information and modeling
Antimicrobial resistance (AMR) is a global threat, with species contributing significantly to difficult-to-treat infections. The Pen family of β-lactamases are produced by all spp., and their mutation or overproduction leads to the resistance of β-...

Exploring species taxonomic kingdom using information entropy and nucleotide compositional features of coding sequences based on machine learning methods.

Methods (San Diego, Calif.)
The flow of genetic information from DNA to protein is governed by the central dogma of molecular biology. Genetic drift and mutations usually lead to changes in DNA composition, thereby affecting the coding sequences (CDS) that encode functional pro...

Uncertainty mapping and probabilistic tractography using Simulation-based Inference in diffusion MRI: A comparison with classical Bayes.

Medical image analysis
Simulation-Based Inference (SBI) has recently emerged as a powerful framework for Bayesian inference: Neural networks are trained on simulations from a forward model, and learn to rapidly estimate posterior distributions. We here present an SBI frame...

Identifying potential risk genes for clear cell renal cell carcinoma with deep reinforcement learning.

Nature communications
Clear cell renal cell carcinoma (ccRCC) is the most prevalent type of renal cell carcinoma. However, our understanding of ccRCC risk genes remains limited. This gap in knowledge poses challenges to the effective diagnosis and treatment of ccRCC. To a...

CHMMConvScaleNet: a hybrid convolutional neural network and continuous hidden Markov model with multi-scale features for sleep posture detection.

Scientific reports
Sleep posture, a vital aspect of sleep wellness, has become a crucial focus in sleep medicine. Studies show that supine posture can lead to a higher occurrence of obstructive sleep apnea, while lateral posture might reduce it. For bedridden patients,...

A Survey on Causal Reinforcement Learning.

IEEE transactions on neural networks and learning systems
While reinforcement learning (RL) achieves tremendous success in sequential decision-making problems of many domains, it still faces key challenges of data inefficiency and the lack of interpretability. Interestingly, many researchers have leveraged ...

Developing cholera outbreak forecasting through qualitative dynamics: Insights into Malawi case study.

Journal of theoretical biology
Cholera, an acute diarrheal disease, is a serious concern in developing and underdeveloped areas. A qualitative understanding of cholera epidemics aims to foresee transmission patterns based on reported data and mechanistic models. The mechanistic mo...