AIMC Topic: Machine Learning

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Improving Measures of Chemical Structural Similarity Using Machine Learning on Chemical-Genetic Interactions.

Journal of chemical information and modeling
A common strategy for identifying molecules likely to possess a desired biological activity is to search large databases of compounds for high structural similarity to a query molecule that demonstrates this activity, under the assumption that struct...

The use of a next-generation sequencing-derived machine-learning risk-prediction model (OncoCast-MPM) for malignant pleural mesothelioma: a retrospective study.

The Lancet. Digital health
BACKGROUND: Current risk stratification for patients with malignant pleural mesothelioma based on disease stage and histology is inadequate. For some individuals with early-stage epithelioid tumours, a good prognosis by current guidelines can progres...

In silico saturation mutagenesis of cancer genes.

Nature
Despite the existence of good catalogues of cancer genes, identifying the specific mutations of those genes that drive tumorigenesis across tumour types is still a largely unsolved problem. As a result, most mutations identified in cancer genes acros...

Prediction of treatment outcome in burning mouth syndrome patients using machine learning based on clinical data.

Scientific reports
The purpose of this study is to apply a machine learning approach to predict whether patients with burning mouth syndrome (BMS) respond to the initial approach and clonazepam therapy based on clinical data. Among the patients with the primary type of...

Detecting suicidal risk using MMPI-2 based on machine learning algorithm.

Scientific reports
Minnesota Multiphasic Personality Inventory-2 (MMPI-2) is a widely used tool for early detection of psychological maladjustment and assessing the level of adaptation for a large group in clinical settings, schools, and corporations. This study aims t...

Heatstroke predictions by machine learning, weather information, and an all-population registry for 12-hour heatstroke alerts.

Nature communications
This study aims to develop and validate prediction models for the number of all heatstroke cases, and heatstrokes of hospital admission and death cases per city per 12 h, using multiple weather information and a population-based database for heatstro...

Data-driven identification of complex disease phenotypes.

Journal of the Royal Society, Interface
Disease interaction in multimorbid patients is relevant to treatment and prognosis, yet poorly understood. In the present work, we combine approaches from network science, machine learning and computational phenotyping to assess interactions between ...

Machine learning predicts treatment sensitivity in multiple myeloma based on molecular and clinical information coupled with drug response.

PloS one
Providing treatment sensitivity stratification at the time of cancer diagnosis allows better allocation of patients to alternative treatment options. Despite many clinical and biological risk markers having been associated with variable survival in c...

The current and future uses of machine learning in ecosystem service research.

The Science of the total environment
Machine learning (ML) expands traditional data analysis and presents a range of opportunities in ecosystem service (ES) research, offering rapid processing of 'big data' and enabling significant advances in data description and predictive modelling. ...

What's in a trauma? Using machine learning to unpack what makes an event traumatic.

Journal of affective disorders
What differentiates a trauma from an event that is merely upsetting? Wildly different definitions of trauma have been used in both formal (psychiatric) and informal (cultural, colloquial) settings. Yet there is a dearth of empirical work examining th...