AIMC Topic: Machine Learning

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Tapping on the Black Box: How Is the Scoring Power of a Machine-Learning Scoring Function Dependent on the Training Set?

Journal of chemical information and modeling
In recent years, protein-ligand interaction scoring functions derived through machine-learning are repeatedly reported to outperform conventional scoring functions. However, several published studies have questioned that the superior performance of m...

Collective effects of long-range DNA methylations predict gene expressions and estimate phenotypes in cancer.

Scientific reports
DNA methylation of various genomic regions has been found to be associated with gene expression in diverse biological contexts. However, most genome-wide studies have focused on the effect of (1) methylation in cis, not in trans and (2) a single CpG,...

Nonlinear dynamics based machine learning: Utilizing dynamics-based flexibility of nonlinear circuits to implement different functions.

PloS one
The core element of machine learning is a flexible, universal function approximator that can be trained and fit into the data. One of the main challenges in modern machine learning is to understand the role of nonlinearity and complexity in these uni...

Pragmatics, Theory of Mind and executive functions in schizophrenia: Disentangling the puzzle using machine learning.

PloS one
OBJECTIVE: Schizophrenia is associated with a severe impairment in the communicative-pragmatic domain. Recent research has tried to disentangle the relationship between communicative impairment and other domains usually impaired in schizophrenia, i.e...

Review and comparative analysis of machine learning-based phage virion protein identification methods.

Biochimica et biophysica acta. Proteins and proteomics
Phage virion protein (PVP) identification plays key role in elucidating relationships between phages and hosts. Moreover, PVP identification can facilitate the design of related biochemical entities. Recently, several machine learning approaches have...

Machine-learning-based computed tomography radiomic analysis for histologic subtype classification of thymic epithelial tumours.

European journal of radiology
PURPOSE: To evaluate the performance of machine-learning-based computed tomography (CT) radiomic analysis to differentiate high-risk thymic epithelial tumours (TETs) from low-risk TETs according to the WHO classification.

Medical Named Entity Extraction from Chinese Resident Admit Notes Using Character and Word Attention-Enhanced Neural Network.

International journal of environmental research and public health
The resident admit notes (RANs) in electronic medical records (EMRs) is first-hand information to study the patient's condition. Medical entity extraction of RANs is an important task to get disease information for medical decision-making. For Chines...

Application of Raw Accelerometer Data and Machine-Learning Techniques to Characterize Human Movement Behavior: A Systematic Scoping Review.

Journal of physical activity & health
BACKGROUND: Application of machine learning for classifying human behavior is increasingly common as access to raw accelerometer data improves. The aims of this scoping review are (1) to examine if machine-learning techniques can accurately identify ...

Big data in IBD: big progress for clinical practice.

Gut
IBD is a complex multifactorial inflammatory disease of the gut driven by extrinsic and intrinsic factors, including host genetics, the immune system, environmental factors and the gut microbiome. Technological advancements such as next-generation se...