AIMC Topic: Machine Learning

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Applications of machine learning in the chemical pathology laboratory.

Journal of clinical pathology
Machine learning (ML) is an area of artificial intelligence that provides computer programmes with the capacity to autodidact and learn new skills from experience, without continued human programming. ML algorithms can analyse large data sets quickly...

Prediction of African Swine Fever Virus Inhibitors by Molecular Docking-Driven Machine Learning Models.

Molecules (Basel, Switzerland)
African swine fever virus (ASFV) causes a highly contagious and severe hemorrhagic viral disease with high mortality in domestic pigs of all ages. Although the virus is harmless to humans, the ongoing ASFV epidemic could have severe economic conseque...

Development and validation of consensus machine learning-based models for the prediction of novel small molecules as potential anti-tubercular agents.

Molecular diversity
Tuberculosis (TB) is an infectious disease and the leading cause of death globally. The rapidly emerging cases of drug resistance among pathogenic mycobacteria have been a global threat urging the need of new drug discovery and development. However, ...

Phenotyping the Spectrum of Traumatic Brain Injury: A Review and Pathway to Standardization.

Journal of neurotrauma
It is widely appreciated that the spectrum of traumatic brain injury (TBI), mild through severe, contains distinct clinical presentations, variably referred to as subtypes, phenotypes, and/or clinical profiles. As part of the Brain Trauma Blueprint T...

Wide range of applications for machine-learning prediction models in orthopedic surgical outcome: a systematic review.

Acta orthopaedica
Background and purpose - Advancements in software and hardware have enabled the rise of clinical prediction models based on machine learning (ML) in orthopedic surgery. Given their growing popularity and their likely implementation in clinical practi...

Estimating heterogeneous survival treatment effect in observational data using machine learning.

Statistics in medicine
Methods for estimating heterogeneous treatment effect in observational data have largely focused on continuous or binary outcomes, and have been relatively less vetted with survival outcomes. Using flexible machine learning methods in the counterfact...

Machine learning-aided protein identification from multidimensional signatures.

Lab on a chip
The ability to determine the identity of specific proteins is a critical challenge in many areas of cellular and molecular biology, and in medical diagnostics. Here, we present a macine learning aided microfluidic protein characterisation strategy th...

Machine learning models for classification tasks related to drug safety.

Molecular diversity
In this review, we outline the current trends in the field of machine learning-driven classification studies related to ADME (absorption, distribution, metabolism and excretion) and toxicity endpoints from the past six years (2015-2021). The study fo...