AIMC Topic: Machine Learning

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An interpretable deep learning model for classifying adaptor protein complexes from sequence information.

Methods (San Diego, Calif.)
Adaptor proteins (APs) are a family of proteins that aids in intracellular membrane trafficking, and their impairments or defects are closely related to various disorders. Traditional methods to identify and classify APs require time and complex tech...

CLADE 2.0: Evolution-Driven Cluster Learning-Assisted Directed Evolution.

Journal of chemical information and modeling
Directed evolution, a revolutionary biotechnology in protein engineering, optimizes protein fitness by searching an astronomical mutational space via expensive experiments. The cluster learning-assisted directed evolution (CLADE) efficiently explores...

DENVIS: Scalable and High-Throughput Virtual Screening Using Graph Neural Networks with Atomic and Surface Protein Pocket Features.

Journal of chemical information and modeling
Computational methods for virtual screening can dramatically accelerate early-stage drug discovery by identifying potential hits for a specified target. Docking algorithms traditionally use physics-based simulations to address this challenge by estim...

Data-driven emergence of convolutional structure in neural networks.

Proceedings of the National Academy of Sciences of the United States of America
Exploiting data invariances is crucial for efficient learning in both artificial and biological neural circuits. Understanding how neural networks can discover appropriate representations capable of harnessing the underlying symmetries of their input...

GAIN: A Gated Adaptive Feature Interaction Network for Click-Through Rate Prediction.

Sensors (Basel, Switzerland)
CTR (Click-Through Rate) prediction has attracted more and more attention from academia and industry for its significant contribution to revenue. In the last decade, learning feature interactions have become a mainstream research direction, and dozen...

Prediction Models for Railway Track Geometry Degradation Using Machine Learning Methods: A Review.

Sensors (Basel, Switzerland)
Keeping railway tracks in good operational condition is one of the most important tasks for railway owners. As a result, railway companies have to conduct track inspections periodically, which is costly and time-consuming. Due to the rapid developmen...

Predictive Models for Knee Pain in Middle-Aged and Elderly Individuals Based on Machine Learning Methods.

Computational and mathematical methods in medicine
AIM: This study used machine learning methods to develop a prediction model for knee pain in middle-aged and elderly individuals.

How to incorporate biological insights into network models and why it matters.

The Journal of physiology
Due to the staggering complexity of the brain and its neural circuitry, neuroscientists rely on the analysis of mathematical models to elucidate its function. From Hodgkin and Huxley's detailed description of the action potential in 1952 to today, ne...

Validation study of machine-learning chest radiograph software in primary and emergency medicine.

Clinical radiology
AIM: To evaluate the performance of a machine learning based algorithm tool for chest radiographs (CXRs), applied to a consecutive cohort of historical clinical cases, in comparison to expert chest radiologists.

Survival Prediction of Children Undergoing Hematopoietic Stem Cell Transplantation Using Different Machine Learning Classifiers by Performing Chi-Square Test and Hyperparameter Optimization: A Retrospective Analysis.

Computational and mathematical methods in medicine
Bone marrow transplant (BMT) is an effective surgical treatment for bone marrow-related disorders. However, several associated risk factors can impair long-term survival after BMT. Machine learning (ML) technologies have been proven useful in surviva...