AIMC Topic: Machine Learning

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Predicting Risks of Machine Translations of Public Health Resources by Developing Interpretable Machine Learning Classifiers.

International journal of environmental research and public health
We aimed to develop machine learning classifiers as a risk-prevention mechanism to help medical professionals with little or no knowledge of the patient's languages in order to predict the likelihood of clinically significant mistakes or incomprehens...

Predicting and characterizing a cancer dependency map of tumors with deep learning.

Science advances
Genome-wide loss-of-function screens have revealed genes essential for cancer cell proliferation, called cancer dependencies. It remains challenging to link cancer dependencies to the molecular compositions of cancer cells or to unscreened cell lines...

Estimating the conditional probability of developing human papilloma virus related oropharyngeal cancer by combining machine learning and inverse Bayesian modelling.

PLoS computational biology
The epidemic increase in the incidence of Human Papilloma Virus (HPV) related Oropharyngeal Squamous Cell Carcinomas (OPSCCs) in several countries worldwide represents a significant public health concern. Although gender neutral HPV vaccination progr...

Informed training set design enables efficient machine learning-assisted directed protein evolution.

Cell systems
Directed evolution of proteins often involves a greedy optimization in which the mutation in the highest-fitness variant identified in each round of single-site mutagenesis is fixed. The efficiency of such a single-step greedy walk depends on the ord...

Combining machine learning and quantum mechanics yields more chemically aware molecular descriptors for medicinal chemistry applications.

Journal of computational chemistry
Molecular interaction fields (MIFs), describing molecules in terms of their ability to interact with any chemical entity, are one of the most established and versatile concepts in drug discovery. Improvement of this molecular description is highly de...

Using machine learning to improve survival prediction after heart transplantation.

Journal of cardiac surgery
BACKGROUND: This study investigates the use of modern machine learning (ML) techniques to improve prediction of survival after orthotopic heart transplantation (OHT).

Computational pharmaceutics - A new paradigm of drug delivery.

Journal of controlled release : official journal of the Controlled Release Society
In recent decades pharmaceutics and drug delivery have become increasingly critical in the pharmaceutical industry due to longer time, higher cost, and less productivity of new molecular entities (NMEs). However, current formulation development still...

ABLE: Attention based learning for enzyme classification.

Computational biology and chemistry
Classifying proteins into their respective enzyme class is an interesting question for researchers for a variety of reasons. The open source Protein Data Bank (PDB) contains more than 1,60,000 structures, with more being added everyday. This paper pr...

Machine learning approach to measurement of criticism: The core dimension of expressed emotion.

Journal of family psychology : JFP : journal of the Division of Family Psychology of the American Psychological Association (Division 43)
Expressed emotion (EE), a measure of the family's emotional climate, is a fundamental measure in caregiving research. A core dimension of EE is the level of criticism expressed by the caregiver to the care recipient, with a high level of criticism a ...

Nasopharyngeal metabolomics and machine learning approach for the diagnosis of influenza.

EBioMedicine
BACKGROUND: Respiratory virus infections are significant causes of morbidity and mortality, and may induce host metabolite alterations by infecting respiratory epithelial cells. We investigated the use of liquid chromatography quadrupole time-of-flig...