AIMC Topic: Machine Learning

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Investigations into the use of machine learning to predict drug dosage form design to obtain desired release profiles for 3D printed oral medicines.

Pharmaceutical development and technology
Three-dimensional (3D) printing, digitalization, and artificial intelligence (AI) are gaining increasing interest in modern medicine. All three aspects are combined in personalized medicine where 3D-printed dosage forms are advantageous because of th...

Small hand-designed convolutional neural networks outperform transfer learning in automated cell shape detection in confluent tissues.

PloS one
Mechanical cues such as stresses and strains are now recognized as essential regulators in many biological processes like cell division, gene expression or morphogenesis. Studying the interplay between these mechanical cues and biological responses r...

Artificial intelligence for compound pharmacokinetics prediction.

Current opinion in structural biology
Optimisation of compound pharmacokinetics (PK) is an integral part of drug discovery and development. Animal in vivo PK data as well as human and animal in vitro systems are routinely utilised to evaluate PK in humans. In recent years machine learnin...

Landslide susceptibility prediction improvements based on a semi-integrated supervised machine learning model.

Environmental science and pollution research international
Differences in model application effectiveness, insufficient numbers of disaster samples, and unreasonable selection of non-hazard samples are common problems in landslide susceptibility studies. Therefore, in this paper, we propose a semi-integrated...

Predict, diagnose, and treat chronic kidney disease with machine learning: a systematic literature review.

Journal of nephrology
OBJECTIVES: In this systematic review we aimed at assessing how artificial intelligence (AI), including machine learning (ML) techniques have been deployed to predict, diagnose, and treat chronic kidney disease (CKD). We systematically reviewed the a...

Integrating Computation, Experiment, and Machine Learning in the Design of Peptide-Based Supramolecular Materials and Systems.

Angewandte Chemie (International ed. in English)
Interest in peptide-based supramolecular materials has grown extensively since the 1980s and the application of computational methods has paralleled this. These methods contribute to the understanding of experimental observations based on interaction...

A machine learning q-RASPR approach for efficient predictions of the specific surface area of perovskites.

Molecular informatics
In this study, the specific surface area of various perovskites was modeled using a novel quantitative read-across structure-property relationship (q-RASPR) approach, which clubs both Read-Across (RA) and quantitative structure-property relationship ...

Evaluation of a decided sample size in machine learning applications.

BMC bioinformatics
BACKGROUND: An appropriate sample size is essential for obtaining a precise and reliable outcome of a study. In machine learning (ML), studies with inadequate samples suffer from overfitting of data and have a lower probability of producing true effe...