AIMC Topic: Machine Learning

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Unraveling surface-enhanced Raman spectroscopy results through chemometrics and machine learning: principles, progress, and trends.

Analytical and bioanalytical chemistry
Surface-enhanced Raman spectroscopy (SERS) has gained increasing attention because it provides rich chemical information and high sensitivity, being applicable in many scientific fields including medical diagnosis, forensic analysis, food control, an...

NLS: An accurate and yet easy-to-interpret prediction method.

Neural networks : the official journal of the International Neural Network Society
Over the last years, the predictive power of supervised machine learning (ML) has undergone impressive advances, achieving the status of state of the art and super-human level in some applications. However, the employment rate of ML models in real-li...

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning.

Journal of visualized experiments : JoVE
The quantitative analysis of subcellular organelles such as mitochondria in cell fluorescence microscopy images is a demanding task because of the inherent challenges in the segmentation of these small and morphologically diverse structures. In this ...

Improving Accuracy and Transferability of Machine Learning Chemical Activation Energies by Adding Electronic Structure Information.

Journal of chemical information and modeling
Predicting chemical activation energies is one of the longstanding and important challenges in computational chemistry. Recent advances have shown that machine learning can be used to create tools to predict them. Such tools can significantly decreas...

A Fusion-Assisted Multi-Stream Deep Learning and ESO-Controlled Newton-Raphson-Based Feature Selection Approach for Human Gait Recognition.

Sensors (Basel, Switzerland)
The performance of human gait recognition (HGR) is affected by the partial obstruction of the human body caused by the limited field of view in video surveillance. The traditional method required the bounding box to recognize human gait in the video ...

Predicting anterior cruciate ligament failure load with T* relaxometry and machine learning as a prospective imaging biomarker for revision surgery.

Scientific reports
Non-invasive methods to document healing anterior cruciate ligament (ACL) structural properties could potentially identify patients at risk for revision surgery. The objective was to evaluate machine learning models to predict ACL failure load from m...

Preface: Artificial intelligence (AI), machine learning ML) and digital pathology integration are the next major chapter in our diagnostic pathology and laboratory medicine arena.

Seminars in diagnostic pathology
This timely captivating topic is organized and presented in this special issue of the journal of Seminar in diagnostic pathology. This special issue will be dedicated to the utilization of machine learning within the digital pathology and laboratory ...

IPC prediction of patent documents using neural network with attention for hierarchical structure.

PloS one
International patent classifications (IPCs) are assigned to patent documents; however, since the procedure for assigning classifications is manually done by the patent examiner, it takes a lot of time and effort to select some IPCs from about 70,000 ...

Development and External Validation of a Machine Learning Model for Prediction of Lymph Node Metastasis in Patients with Prostate Cancer.

European urology oncology
BACKGROUND: Pelvic lymph node dissection (PLND) is the gold standard for diagnosis of lymph node involvement (LNI) in patients with prostate cancer. The Roach formula, Memorial Sloan Kettering Cancer Center (MSKCC) calculator, and Briganti 2012 nomog...

Establishment of extensive artificial intelligence models for kinase inhibitor prediction: Identification of novel PDGFRB inhibitors.

Computers in biology and medicine
Identifying hit compounds is an important step in drug development. Unfortunately, this process continues to be a challenging task. Several machine learning models have been generated to aid in simplifying and improving the prediction of candidate co...