AIMC Topic: Machine Learning

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Speed-resolved perfusion imaging using multi-exposure laser speckle contrast imaging and machine learning.

Journal of biomedical optics
SIGNIFICANCE: Laser speckle contrast imaging (LSCI) gives a relative measure of microcirculatory perfusion. However, due to the limited information in single-exposure LSCI, models are inaccurate for skin tissue due to complex effects from e.g. static...

Optimization of biocementation responses by artificial neural network and random forest in comparison to response surface methodology.

Environmental science and pollution research international
In this article, the optimization of the specific urease activity (SUA) and the calcium carbonate (CaCO) using microbially induced calcite precipitation (MICP) was compared to optimization using three algorithms based on machine learning: random fore...

Automatic Classification of Mass Shape and Margin on Mammography with Artificial Intelligence: Deep CNN Versus Radiomics.

Journal of digital imaging
The purpose of this study is to test the feasibility for deep CNN-based artificial intelligence methods for automatic classification of the mass margin and shape, while radiomic feature-based machine learning methods were also implemented in this stu...

Bibliometric and visual analysis of machine learning-based research in acute kidney injury worldwide.

Frontiers in public health
BACKGROUND: Acute kidney injury (AKI) is a serious clinical complication associated with adverse short-term and long-term outcomes. In recent years, with the rapid popularization of electronic health records and artificial intelligence machine learni...

Important Correlates of Purpose in Life in a Diverse Population-Based Cohort: A Machine Learning Approach.

The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry
BACKGROUND: Purpose-in-life (PiL) refers to the tendency to derive meaning and purpose from daily life experiences. Individuals with higher PiL were more likely to have better physical, mental, and cognitive health in prospective studies. Here, we ai...

Development of machine learning models based on molecular fingerprints for selection of small molecule inhibitors against JAK2 protein.

Journal of computational chemistry
Janus kinase 2 (JAK2) is emerging as a potential therapeutic target for many inflammatory diseases such as myeloproliferative disorders (MPD), cancer and rheumatoid arthritis (RA). In this study, we have collected experimental data of JAK2 protein co...

Using Machine Learning To Predict Partition Coefficient (Log ) and Distribution Coefficient (Log ) with Molecular Descriptors and Liquid Chromatography Retention Time.

Journal of chemical information and modeling
During preclinical evaluations of drug candidates, several physicochemical (p-chem) properties are measured and employed as metrics to estimate drug efficacy in vivo. Two such p-chem properties are the octanol-water partition coefficient, Log , and d...

Evolutionary-scale prediction of atomic-level protein structure with a language model.

Science (New York, N.Y.)
Recent advances in machine learning have leveraged evolutionary information in multiple sequence alignments to predict protein structure. We demonstrate direct inference of full atomic-level protein structure from primary sequence using a large langu...

Customer Analysis Using Machine Learning-Based Classification Algorithms for Effective Segmentation Using Recency, Frequency, Monetary, and Time.

Sensors (Basel, Switzerland)
Customer segmentation has been a hot topic for decades, and the competition among businesses makes it more challenging. The recently introduced Recency, Frequency, Monetary, and Time (RFMT) model used an agglomerative algorithm for segmentation and a...