AIMC Topic: Machine Learning

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A Machine Learning-Based Approach Using Multi-omics Data to Predict Metabolic Pathways.

Methods in molecular biology (Clifton, N.J.)
The integrative method approaches are continuously evolving to provide accurate insights from the data that is received through experimentation on various biological systems. Multi-omics data can be integrated with predictive machine learning algorit...

Computational Methods and Deep Learning for Elucidating Protein Interaction Networks.

Methods in molecular biology (Clifton, N.J.)
Protein interactions play a critical role in all biological processes, but experimental identification of protein interactions is a time- and resource-intensive process. The advances in next-generation sequencing and multi-omics technologies have gre...

Synthetic Biology Meets Machine Learning.

Methods in molecular biology (Clifton, N.J.)
This chapter outlines the myriad applications of machine learning (ML) in synthetic biology, specifically in engineering cell and protein activity, and metabolic pathways. Though by no means comprehensive, the chapter highlights several prominent com...

Prediction of Breast Cancer Through Random Forest.

Current medical imaging
BACKGROUND: 8% of women are diagnosed with breast cancer. (BC) BC is the second most common cause of death in both developed and undeveloped countries. BC is characterized by the mutation of genes, constant pain, changes in the size, color (redness),...

Artificial Intelligence in Efficient Diabetes Care.

Current diabetes reviews
Diabetes is a chronic disease that is not easily curable but can be managed efficiently. Artificial Intelligence is a powerful tool that may help in diabetes prediction, continuous glucose monitoring, Insulin injection guidance, and other areas of di...

Application of Machine Learning Technology in the Prediction of ADME- Related Pharmacokinetic Parameters.

Current medicinal chemistry
BACKGROUND: As an important determinant in drug discovery, the accurate analysis and acquisition of pharmacokinetic parameters are very important for the clinical application of drugs. At present, the research and development of new drugs mainly obta...

Machine-learning Algorithms for Ischemic Heart Disease Prediction: A Systematic Review.

Current cardiology reviews
PURPOSE: This review aims to summarize and evaluate the most accurate machinelearning algorithm used to predict ischemic heart disease.

Comparative Analysis Between Machine Learning Algorithms and Conventional Regression in Predicting the Prognosis of Patients with Basilar Invagination: A Retrospective Cohort Study.

Turkish neurosurgery
AIM: To identify predictors of basilar invagination (BI) prognosis and compare diagnostic properties between logistic modeling and machine learning methods.

The quest for the missing links in fatty liver genetics: Deep learning to the rescue!

Cell reports. Medicine
Park, MacLean, et al. conduct an exome-wide association study of liver fat content in the Penn Medicine BioBank. By leveraging machine learning-assisted analysis of clinical CT scans to quantify steatosis, they uncover previously undescribed liver fa...

Advancing cardiovascular medicine with machine learning: Progress, potential, and perspective.

Cell reports. Medicine
Recent advances in machine learning (ML) have made it possible to analyze high-dimensional and complex data-such as free text, images, waveforms, videos, and sound-in an automated manner by successfully learning complex associations within these data...