AIMC Topic: Machine Learning

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An Online Nanoinformatics Platform Empowering Computational Modeling of Nanomaterials by Nanostructure Annotations and Machine Learning Toolkits.

Nano letters
Modern nanotechnology has generated numerous datasets from and studies on nanomaterials, with some available on nanoinformatics portals. However, these existing databases lack the digital data and tools suitable for machine learning studies. Here, ...

Unraveling the impact of therapeutic drug monitoring via machine learning for patients with sepsis.

Cell reports. Medicine
Clinical studies investigating the benefits of beta-lactam therapeutic drug monitoring (TDM) among critically ill patients are hindered by small patient groups, variability between studies, patient heterogeneity, and inadequate use of TDM. Accordingl...

Predicting the pulse of urban water demand: a machine learning approach to deciphering meteorological influences.

BMC research notes
OBJECTIVE: This study delves into the impact of urban meteorological elements-specifically, air temperature, relative humidity, and atmospheric pressure-on water consumption in Kamyaran city. Data on urban water consumption, temperature (in Celsius),...

Admission blood tests predicting survival of SARS-CoV-2 infected patients: a practical implementation of graph convolution network in imbalance dataset.

BMC infectious diseases
BACKGROUND: Predicting an individual's risk of death from COVID-19 is essential for planning and optimising resources. However, since the real-world mortality rate is relatively low, particularly in places like Hong Kong, this makes building an accur...

Modeling and predicting meat yield and growth performance using morphological features of narrow-clawed crayfish with machine learning techniques.

Scientific reports
In recent studies, artificial intelligence and machine learning methods give higher accuracy than other prediction methods in large data sets with complex structures. Instead of statistical methods, artificial intelligence, and machine learning are u...

Performance of machine learning algorithms for lung cancer prediction: a comparative approach.

Scientific reports
Due to the excessive growth of PM 2.5 in aerosol, the cases of lung cancer are increasing rapidly and are most severe among other types as the highest mortality rate. In most of the cases, lung cancer is detected with least symptoms at its later stag...

CREMP: Conformer-rotamer ensembles of macrocyclic peptides for machine learning.

Scientific data
Computational and machine learning approaches to model the conformational landscape of macrocyclic peptides have the potential to enable rational design and optimization. However, accurate, fast, and scalable methods for modeling macrocycle geometrie...

Guiding questions to avoid data leakage in biological machine learning applications.

Nature methods
Machine learning methods for extracting patterns from high-dimensional data are very important in the biological sciences. However, in certain cases, real-world applications cannot confirm the reported prediction performance. One of the main reasons ...

Applying interpretable machine learning in computational biology-pitfalls, recommendations and opportunities for new developments.

Nature methods
Recent advances in machine learning have enabled the development of next-generation predictive models for complex computational biology problems, thereby spurring the use of interpretable machine learning (IML) to unveil biological insights. However,...

Infrared spectroscopy coupled with machine learning algorithms for predicting the detailed milk mineral profile in dairy cattle.

Food chemistry
Milk minerals are not only essential components for human health, but they can be informative for milk quality and cow's health. Herein, we investigated the feasibility of Fourier Transformed mid Infrared (FTIR) spectroscopy for the prediction of a d...