AIMC Topic: Machine Learning

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Nondestructive Detection for Egg Freshness Based on Hyperspectral Scattering Image Combined with Ensemble Learning.

Sensors (Basel, Switzerland)
Scattering hyperspectral technology is a nondestructive testing method with many advantages. Here, we propose a method to improve the accuracy of egg freshness, research the influence of incident angles of light source on the accuracy, and explain it...

Transfer learning enables the molecular transformer to predict regio- and stereoselective reactions on carbohydrates.

Nature communications
Organic synthesis methodology enables the synthesis of complex molecules and materials used in all fields of science and technology and represents a vast body of accumulated knowledge optimally suited for deep learning. While most organic reactions i...

A machine learning Automated Recommendation Tool for synthetic biology.

Nature communications
Synthetic biology allows us to bioengineer cells to synthesize novel valuable molecules such as renewable biofuels or anticancer drugs. However, traditional synthetic biology approaches involve ad-hoc engineering practices, which lead to long develop...

Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism.

Nature communications
Through advanced mechanistic modeling and the generation of large high-quality datasets, machine learning is becoming an integral part of understanding and engineering living systems. Here we show that mechanistic and machine learning models can be c...

Using Item Response Theory for Explainable Machine Learning in Predicting Mortality in the Intensive Care Unit: Case-Based Approach.

Journal of medical Internet research
BACKGROUND: Supervised machine learning (ML) is being featured in the health care literature with study results frequently reported using metrics such as accuracy, sensitivity, specificity, recall, or F1 score. Although each metric provides a differe...

One Algorithm May Not Fit All: How Selection Bias Affects Machine Learning Performance.

Radiographics : a review publication of the Radiological Society of North America, Inc
Machine learning (ML) algorithms have demonstrated high diagnostic accuracy in identifying and categorizing disease on radiologic images. Despite the results of initial research studies that report ML algorithm diagnostic accuracy similar to or excee...

Artificial Intelligence System to Determine Risk of T1 Colorectal Cancer Metastasis to Lymph Node.

Gastroenterology
BACKGROUND & AIMS: In accordance with guidelines, most patients with T1 colorectal cancers (CRC) undergo surgical resection with lymph node dissection, despite the low incidence (∼10%) of metastasis to lymph nodes. To reduce unnecessary surgical rese...

Patient generated health data and electronic health record integration in oncologic surgery: A call for artificial intelligence and machine learning.

Journal of surgical oncology
In this review, we aim to assess the current state of science in relation to the integration of patient-generated health data (PGHD) and patient-reported outcomes (PROs) into routine clinical care with a focus on surgical oncology populations. We wil...

Genome-wide association study-based deep learning for survival prediction.

Statistics in medicine
Informative and accurate survival prediction with individualized dynamic risk profiles over time is critical for personalized disease prevention and clinical management. The massive genetic data, such as SNPs from genome-wide association studies (GWA...

ChemGrapher: Optical Graph Recognition of Chemical Compounds by Deep Learning.

Journal of chemical information and modeling
In drug discovery, knowledge of the graph structure of chemical compounds is essential. Many thousands of scientific articles and patents in chemistry and pharmaceutical sciences have investigated chemical compounds, but in many cases, the details of...