AIMC Topic: Machine Learning

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Automated Classification of Cellular Phenotypes Using Machine Learning in Cellprofiler and CellProfiler Analyst.

Methods in molecular biology (Clifton, N.J.)
Cell images provide a multitude of phenotypic information, which in its entirety the human eye can hardly perceive. Automated image analysis and machine learning approaches enable the unbiased identification and analysis of cellular mechanisms and as...

Parameter tuning in machine learning based on radiomics biomarkers of lung cancer.

Journal of X-ray science and technology
BACKGROUND: Lung cancer is one of the most common cancers, and early diagnosis and intervention can improve cancer cure rate.

Nonparametric Bayesian Regression and Classification on Manifolds, With Applications to 3D Cochlear Shapes.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Advanced shape analysis studies such as regression and classification need to be performed on curved manifolds, where often, there is a lack of standard statistical formulations. To overcome these limitations, we introduce a novel machine-learning me...

Machine Learning Prediction of Antimicrobial Peptides.

Methods in molecular biology (Clifton, N.J.)
Antibiotic resistance constitutes a global threat and could lead to a future pandemic. One strategy is to develop a new generation of antimicrobials. Naturally occurring antimicrobial peptides (AMPs) are recognized templates and some are already in c...

Classical and Machine Learning Methods for Protein - Ligand Binding Free Energy Estimation.

Current drug metabolism
Binding free energy estimation of drug candidates to their biomolecular target is one of the best quantitative estimators in computer-aided drug discovery. Accurate binding free energy estimation is still a challengeable task even after decades of re...

Progress and challenges for the machine learning-based design of fit-for-purpose monoclonal antibodies.

mAbs
Although the therapeutic efficacy and commercial success of monoclonal antibodies (mAbs) are tremendous, the design and discovery of new candidates remain a time and cost-intensive endeavor. In this regard, progress in the generation of data describi...

A machine learning technology to improve the risk of non-invasive prenatal tests.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Timely and accurate diagnosis of genetic diseases can lead to proper action and prevention of irreparable events.

Leukemia classification using the deep learning method of CNN.

Journal of X-ray science and technology
BACKGROUND: Processing Low-Intensity Medical Images (LI-MI) is difficult as outcomes are varied when it comes to manual examination, which is also a time-consuming process.

Mitigating Racial Bias in Machine Learning.

The Journal of law, medicine & ethics : a journal of the American Society of Law, Medicine & Ethics
When applied in the health sector, AI-based applications raise not only ethical but legal and safety concerns, where algorithms trained on data from majority populations can generate less accurate or reliable results for minorities and other disadvan...

Artificial Intelligence for Precision Oncology.

Advances in experimental medicine and biology
Precision oncology is an innovative approach to cancer care in which diagnosis, prognosis, and treatment are informed by the individual patient's genetic and molecular profile. The rapid development of novel high-throughput omics technologies in rece...