AIMC Topic: Machine Learning

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Lilikoi V2.0: a deep learning-enabled, personalized pathway-based R package for diagnosis and prognosis predictions using metabolomics data.

GigaScience
BACKGROUND: previously we developed Lilikoi, a personalized pathway-based method to classify diseases using metabolomics data. Given the new trends of computation in the metabolomics field, it is important to update Lilikoi software.

Early prediction of mortality risk among patients with severe COVID-19, using machine learning.

International journal of epidemiology
BACKGROUND: Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 infection, has been spreading globally. We aimed to develop a clinical model to predict the outcome of patients with severe COVID-19 infection ...

Reflection on modern methods: generalized linear models for prognosis and intervention-theory, practice and implications for machine learning.

International journal of epidemiology
Prediction and causal explanation are fundamentally distinct tasks of data analysis. In health applications, this difference can be understood in terms of the difference between prognosis (prediction) and prevention/treatment (causal explanation). Ne...

Intersections of machine learning and epidemiological methods for health services research.

International journal of epidemiology
The field of health services research is broad and seeks to answer questions about the health care system. It is inherently interdisciplinary, and epidemiologists have made crucial contributions. Parametric regression techniques remain standard pract...

Ensemble modelling in descriptive epidemiology: burden of disease estimation.

International journal of epidemiology
Ensemble modelling is a quantitative method that combines information from multiple individual models and has shown great promise in statistical machine learning. Ensemble models have a theoretical claim to being models that make the 'best' predictio...

Reflection on modern methods: when worlds collide-prediction, machine learning and causal inference.

International journal of epidemiology
Causal inference requires theory and prior knowledge to structure analyses, and is not usually thought of as an arena for the application of prediction modelling. However, contemporary causal inference methods, premised on counterfactual or potential...

Deep neural networks identify signaling mechanisms of ErbB-family drug resistance from a continuous cell morphology space.

Cell reports
It is well known that the development of drug resistance in cancer cells can lead to changes in cell morphology. Here, we describe the use of deep neural networks to analyze this relationship, demonstrating that complex cell morphologies can encode s...

Visualizing population structure with variational autoencoders.

G3 (Bethesda, Md.)
Dimensionality reduction is a common tool for visualization and inference of population structure from genotypes, but popular methods either return too many dimensions for easy plotting (PCA) or fail to preserve global geometry (t-SNE and UMAP). Here...

Domain-specific introduction to machine learning terminology, pitfalls and opportunities in CRISPR-based gene editing.

Briefings in bioinformatics
The use of machine learning (ML) has become prevalent in the genome engineering space, with applications ranging from predicting target site efficiency to forecasting the outcome of repair events. However, jargon and ML-specific accuracy measures hav...