AIMC Topic: Machine Learning

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Secure messaging telehealth billing in the digital age: moving beyond time-based metrics.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: We proposed adopting billing models for secure messaging (SM) telehealth services that move beyond time-based metrics, focusing on the complexity and clinical expertise involved in patient care.

Identification of miR-20a as a Diagnostic and Prognostic Biomarker in Colorectal Cancer: MicroRNA Sequencing and Machine Learning Analysis.

MicroRNA (Shariqah, United Arab Emirates)
INTRODUCTION: The differential expression of miRNAs, a key regulator in many cell signaling pathways, has been studied in various malignancies and may have an important role in cancer progression, including colorectal cancer (CRC).

MolPredictX: A Pioneer Mobile App Version for Online Biological Activity Predictions by Machine Learning Models.

Methods in molecular biology (Clifton, N.J.)
MolPredictX is a free-access web tool in which it is possible to analyze the prediction of biological activity of chemical molecules. MolPredictX has been available online to the general public for just over a year and has now gone through its first ...

Machine Learning in Early Prediction of Metabolism of Drugs.

Methods in molecular biology (Clifton, N.J.)
Machine learning (ML) has increasingly been applied to predict properties of drugs. Particularly, metabolism can be predicted with ML methods, which can be exploited during drug discovery and development. The prediction of metabolism is a crucial bot...

Applicability Domain for Trustable Predictions.

Methods in molecular biology (Clifton, N.J.)
In the rapidly evolving landscape of artificial intelligence (AI) and machine learning (ML), understanding and correctly applying the concept of the applicability domain (AD) has emerged as an essential part. This chapter begins with an introduction ...

Automated Workflows for Data Curation and Machine Learning to Develop Quantitative Structure-Activity Relationships.

Methods in molecular biology (Clifton, N.J.)
The recent advancements in machine learning and the new availability of large chemical datasets made the development of tools and protocols for computational chemistry a topic of high interest. In this chapter a standard procedure to develop Quantita...

The Evolution of Nucleic Acid-Based Diagnosis Methods from the (pre-)CRISPR to CRISPR era and the Associated Machine/Deep Learning Approaches in Relevant RNA Design.

Methods in molecular biology (Clifton, N.J.)
Nucleic acid tests (NATs) are considered as gold standard in molecular diagnosis. To meet the demand for onsite, point-of-care, specific and sensitive, trace and genotype detection of pathogens and pathogenic variants, various types of NATs have been...

Generative Modeling of RNA Sequence Families with Restricted Boltzmann Machines.

Methods in molecular biology (Clifton, N.J.)
In this chapter, we discuss the potential application of Restricted Boltzmann machines (RBM) to model sequence families of structured RNA molecules. RBMs are a simple two-layer machine learning model able to capture intricate sequence dependencies in...

Machine Learning for RNA Design: LEARNA.

Methods in molecular biology (Clifton, N.J.)
Machine learning algorithms, and in particular deep learning approaches, have recently garnered attention in the field of molecular biology due to remarkable results. In this chapter, we describe machine learning approaches specifically developed for...

Development of machine learning-based quantitative structure-activity relationship models for predicting plasma half-lives of drugs in six common food animal species.

Toxicological sciences : an official journal of the Society of Toxicology
Plasma half-life is a crucial pharmacokinetic parameter for estimating extralabel withdrawal intervals of drugs to ensure the safety of food products derived from animals. This study focuses on developing a quantitative structure-activity relationshi...