AIMC Topic: Machine Learning

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Combined mechanistic modeling and machine-learning approaches in systems biology - A systematic literature review.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Mechanistic-based Model simulations (MM) are an effective approach commonly employed, for research and learning purposes, to better investigate and understand the inherent behavior of biological systems. Recent advancements ...

Integrating Nearest Neighbors with Neural Network Models for Treatment Effect Estimation.

International journal of neural systems
Treatment effect estimation is of high-importance for both researchers and practitioners across many scientific and industrial domains. The abundance of observational data makes them increasingly used by researchers for the estimation of causal effec...

Stacked ensemble machine learning for porosity and absolute permeability prediction of carbonate rock plugs.

Scientific reports
This study employs a stacked ensemble machine learning approach to predict carbonate rocks' porosity and absolute permeability with various pore-throat distributions and heterogeneity. Our dataset consists of 2D slices from 3D micro-CT images of four...

DNA protein binding recognition based on lifelong learning.

Computers in biology and medicine
In recent years, research in the field of bioinformatics has focused on predicting the raw sequences of proteins, and some scholars consider DNA-binding protein prediction as a classification task. Many statistical and machine learning-based methods ...

Deep Learning for Identifying Promising Drug Candidates in Drug-Phospholipid Complexes.

Molecules (Basel, Switzerland)
Drug-phospholipid complexing is a promising formulation technology for improving the low bioavailability of active pharmaceutical ingredients (APIs). However, identifying whether phospholipid and candidate drug can form a complex through in vitro tes...

Stochastic representation of many-body quantum states.

Nature communications
The quantum many-body problem is ultimately a curse of dimensionality: the state of a system with many particles is determined by a function with many dimensions, which rapidly becomes difficult to efficiently store, evaluate and manipulate numerical...

The comparison data forest: A new comparison data approach to determine the number of factors in exploratory factor analysis.

Behavior research methods
Developing psychological assessment instruments often involves exploratory factor analyses, during which one must determine the number of factors to retain. Several factor-retention criteria have emerged that can infer this number from empirical data...

Identifying Young Adults at High Risk for Weight Gain Using Machine Learning.

The Journal of surgical research
INTRODUCTION: Weight gain among young adults continues to increase. Identifying adults at high risk for weight gain and intervening before they gain weight could have a major public health impact. Our objective was to develop and test electronic heal...

TransSleep: Transitioning-Aware Attention-Based Deep Neural Network for Sleep Staging.

IEEE transactions on cybernetics
Sleep staging is essential for sleep assessment and plays a vital role as a health indicator. Many recent studies have devised various machine/deep learning methods for sleep staging. However, two key challenges hinder the practical use of those meth...

A Snapshot-Stacked Ensemble and Optimization Approach for Vehicle Breakdown Prediction.

Sensors (Basel, Switzerland)
Predicting breakdowns is becoming one of the main goals for vehicle manufacturers so as to better allocate resources, and to reduce costs and safety issues. At the core of the utilization of vehicle sensors is the fact that early detection of anomali...