AIMC Topic: Bayes Theorem

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Clinically Applicable Deep Learning Algorithm Using Quantitative Proteomic Data.

Journal of proteome research
Deep learning (DL), a type of machine learning approach, is a powerful tool for analyzing large sets of data that are derived from biomedical sciences. However, it remains unknown whether DL is suitable for identifying contributing factors, such as b...

Bayesian Computation through Cortical Latent Dynamics.

Neuron
Statistical regularities in the environment create prior beliefs that we rely on to optimize our behavior when sensory information is uncertain. Bayesian theory formalizes how prior beliefs can be leveraged and has had a major impact on models of per...

Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery.

Chemical reviews
Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides opportunities for the discovery and development of innovative drugs. Various machine learning approaches have recently (re)emerged, some of which may be ...

Evaluation and Identification of the Neuroprotective Compounds of Xiaoxuming Decoction by Machine Learning: A Novel Mode to Explore the Combination Rules in Traditional Chinese Medicine Prescription.

BioMed research international
Xiaoxuming decoction (XXMD), a classic traditional Chinese medicine (TCM) prescription, has been used as a therapeutic in the treatment of stroke in clinical practice for over 1200 years. However, the pharmacological mechanisms of XXMD have not yet b...

Wearable IoT Smart-Log Patch: An Edge Computing-Based Bayesian Deep Learning Network System for Multi Access Physical Monitoring System.

Sensors (Basel, Switzerland)
According to the survey on various health centres, smart log-based multi access physical monitoring system determines the health conditions of humans and their associated problems present in their lifestyle. At present, deficiency in significant nutr...

Predicting asthma attacks in primary care: protocol for developing a machine learning-based prediction model.

BMJ open
INTRODUCTION: Asthma is a long-term condition with rapid onset worsening of symptoms ('attacks') which can be unpredictable and may prove fatal. Models predicting asthma attacks require high sensitivity to minimise mortality risk, and high specificit...

Development of Big Data Predictive Analytics Model for Disease Prediction using Machine learning Technique.

Journal of medical systems
Now days, health prediction in modern life becomesvery much essential. Big data analysis plays a crucial role to predict future status of healthand offerspreeminenthealth outcome to people. Heart disease is a prevalent disease cause's death around th...

Compositionally-warped Gaussian processes.

Neural networks : the official journal of the International Neural Network Society
The Gaussian process (GP) is a nonparametric prior distribution over functions indexed by time, space, or other high-dimensional index set. The GP is a flexible model yet its limitation is given by its very nature: it can only model Gaussian marginal...

Development of pedestrian crash prediction model for a developing country using artificial neural network.

International journal of injury control and safety promotion
Urban intersections in India constitute a significant share of pedestrian fatalities. However, model-based prediction of pedestrian fatalities is still in a nascent stage in India. This study proposes an artificial neural network (ANN) technique to d...