AIMC Topic: Models, Biological

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CRBPDL: Identification of circRNA-RBP interaction sites using an ensemble neural network approach.

PLoS computational biology
Circular RNAs (circRNAs) are non-coding RNAs with a special circular structure produced formed by the reverse splicing mechanism. Increasing evidence shows that circular RNAs can directly bind to RNA-binding proteins (RBP) and play an important role ...

Learning robust perceptive locomotion for quadrupedal robots in the wild.

Science robotics
Legged robots that can operate autonomously in remote and hazardous environments will greatly increase opportunities for exploration into underexplored areas. Exteroceptive perception is crucial for fast and energy-efficient locomotion: Perceiving th...

Harnessing Artificial Intelligence to assess the impact of nonpharmaceutical interventions on the second wave of the Coronavirus Disease 2019 pandemic across the world.

Scientific reports
In the present paper, we aimed to determine the influence of various non-pharmaceutical interventions (NPIs) enforced during the first wave of COVID-19 across countries on the spreading rate of COVID-19 during the second wave. For this purpose, we to...

Comparison of ARIMA and LSTM for prediction of hemorrhagic fever at different time scales in China.

PloS one
OBJECTIVES: This study intends to build and compare two kinds of forecasting models at different time scales for hemorrhagic fever incidence in China.

The use of machine learning to discover regulatory networks controlling biological systems.

Molecular cell
Biological systems are composed of a vast web of multiscale molecular interactors and interactions. High-throughput technologies, both bulk and single cell, now allow for investigation of the properties and quantities of these interactors. Computatio...

Mini-batch optimization enables training of ODE models on large-scale datasets.

Nature communications
Quantitative dynamic models are widely used to study cellular signal processing. A critical step in modelling is the estimation of unknown model parameters from experimental data. As model sizes and datasets are steadily growing, established paramete...

Interpretable machine learning for high-dimensional trajectories of aging health.

PLoS computational biology
We have built a computational model for individual aging trajectories of health and survival, which contains physical, functional, and biological variables, and is conditioned on demographic, lifestyle, and medical background information. We combine ...

Tunable stiffness in fish robotics: mechanisms and advantages.

Bioinspiration & biomimetics
One of the emerging themes of fish-inspired robotics is flexibility. Adding flexibility to the body, joints, or fins of fish-inspired robots can significantly improve thrust and/or efficiency during locomotion. However, the optimal stiffness depends ...

The automatic parameter-exploration with a machine-learning-like approach: Powering the evolutionary modeling on the origin of life.

PLoS computational biology
The origin of life involved complicated evolutionary processes. Computer modeling is a promising way to reveal relevant mechanisms. However, due to the limitation of our knowledge on prebiotic chemistry, it is usually difficult to justify parameter-s...

Proof of concept and development of a couple-based machine learning model to stratify infertile patients with idiopathic infertility.

Scientific reports
We aimed to develop and evaluate a machine learning model that can stratify infertile/fertile couples on the basis of their bioclinical signature helping the management of couples with unexplained infertility. Fertile and infertile couples were recru...