AIMC Topic: Machine Learning

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Large-Scale Distributed Training of Transformers for Chemical Fingerprinting.

Journal of chemical information and modeling
Transformer models have become a popular choice for various machine learning tasks due to their often outstanding performance. Recently, transformers have been used in chemistry for classifying reactions, reaction prediction, physiochemical property ...

Noise-injected analog Ising machines enable ultrafast statistical sampling and machine learning.

Nature communications
Ising machines are a promising non-von-Neumann computational concept for neural network training and combinatorial optimization. However, while various neural networks can be implemented with Ising machines, their inability to perform fast statistica...

A Semi-Supervised Autoencoder-Based Approach for Protein Function Prediction.

IEEE journal of biomedical and health informatics
After the development of next-generation sequencing techniques, protein sequences are abundantly available. Determining the functional characteristics of these proteins is costly and time-consuming. The gap between the number of protein sequences and...

Evaluating the risk of hypertension in residents in primary care in Shanghai, China with machine learning algorithms.

Frontiers in public health
OBJECTIVE: The prevention of hypertension in primary care requires an effective and suitable hypertension risk assessment model. The aim of this study was to develop and compare the performances of three machine learning algorithms in predicting the ...

Botnet Attack Detection in IoT Using Machine Learning.

Computational intelligence and neuroscience
There are an increasing number of Internet of Things (IoT) devices connected to the network these days, and due to the advancement in technology, the security threads and cyberattacks, such as botnets, are emerging and evolving rapidly with high-risk...

Prevalence and Early Prediction of Diabetes Using Machine Learning in North Kashmir: A Case Study of District Bandipora.

Computational intelligence and neuroscience
Diabetes is one of the biggest health problems that affect millions of people across the world. Uncontrolled diabetes can increase the risk of heart attack, cancer, kidney damage, blindness, and other illnesses. Researchers are motivated to create a ...

Using modern risk engines and machine learning/artificial intelligence to predict diabetes complications: A focus on the BRAVO model.

Journal of diabetes and its complications
Management of diabetes requires a multifaceted approach of risk factor reduction; through management of risk factors such as glucose, blood pressure and cholesterol. Goals for these risk factors often vary and guidelines suggest that this is based on...

Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning.

Proceedings of the National Academy of Sciences of the United States of America
Infusing "chemical wisdom" should improve the data-driven approaches that rely exclusively on historical synthetic data for automatic retrosynthesis planning. For this purpose, we designed a chemistry-informed molecular graph (CIMG) to describe chemi...

Detecting and Analyzing Suicidal Ideation on Social Media Using Deep Learning and Machine Learning Models.

International journal of environmental research and public health
Individuals who suffer from suicidal ideation frequently express their views and ideas on social media. Thus, several studies found that people who are contemplating suicide can be identified by analyzing social media posts. However, finding and comp...

Comparison of Natural Language Processing of Clinical Notes With a Validated Risk-Stratification Tool to Predict Severe Maternal Morbidity.

JAMA network open
IMPORTANCE: Risk-stratification tools are routinely used in obstetrics to assist care teams in assessing and communicating risk associated with delivery. Electronic health record data and machine learning methods may offer a novel opportunity to impr...