AIMC Topic: Software

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OPUS-Rota3: Improving Protein Side-Chain Modeling by Deep Neural Networks and Ensemble Methods.

Journal of chemical information and modeling
Side-chain modeling is critical for protein structure prediction since the uniqueness of the protein structure is largely determined by its side-chain packing conformation. In this paper, differing from most approaches that rely on rotamer library sa...

Moving targets in drug discovery.

Scientific reports
Drug Discovery is a lengthy and costly process and has faced a period of declining productivity within the last two decades resulting in increasing importance of integrative data-driven approaches. In this paper, data mining and integration is levera...

DeepLMS: a deep learning predictive model for supporting online learning in the Covid-19 era.

Scientific reports
Coronavirus (Covid-19) pandemic has imposed a complete shut-down of face-to-face teaching to universities and schools, forcing a crash course for online learning plans and technology for students and faculty. In the midst of this unprecedented crisis...

DeepSIBA: chemical structure-based inference of biological alterations using deep learning.

Molecular omics
Predicting whether a chemical structure leads to a desired or adverse biological effect can have a significant impact for in silico drug discovery. In this study, we developed a deep learning model where compound structures are represented as graphs ...

An Automated Planning Model for HRI: Use Cases on Social Assistive Robotics.

Sensors (Basel, Switzerland)
Using Automated Planning for the high level control of robotic architectures is becoming very popular thanks mainly to its capability to define the tasks to perform in a declarative way. However, classical planning tasks, even in its basic standard P...

Machine Learning Methods in Drug Discovery.

Molecules (Basel, Switzerland)
The advancements of information technology and related processing techniques have created a fertile base for progress in many scientific fields and industries. In the fields of drug discovery and development, machine learning techniques have been use...

A convolutional neural network segments yeast microscopy images with high accuracy.

Nature communications
The identification of cell borders ('segmentation') in microscopy images constitutes a bottleneck for large-scale experiments. For the model organism Saccharomyces cerevisiae, current segmentation methods face challenges when cells bud, crowd, or exh...

Deep-learning-assisted detection and segmentation of rib fractures from CT scans: Development and validation of FracNet.

EBioMedicine
BACKGROUND: Diagnosis of rib fractures plays an important role in identifying trauma severity. However, quickly and precisely identifying the rib fractures in a large number of CT images with increasing number of patients is a tough task, which is al...

Better understanding and prediction of antiviral peptides through primary and secondary structure feature importance.

Scientific reports
The emergence of viral epidemics throughout the world is of concern due to the scarcity of available effective antiviral therapeutics. The discovery of new antiviral therapies is imperative to address this challenge, and antiviral peptides (AVPs) rep...

Global gene network exploration based on explainable artificial intelligence approach.

PloS one
In recent years, personalized gene regulatory networks have received significant attention, and interpretation of the multilayer networks has been a critical issue for a comprehensive understanding of gene regulatory systems. Although several statist...