AIMC Topic: Software

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Localizing and Classifying Adaptive Targets with Trend Filtered Regression.

Molecular biology and evolution
Identifying genomic locations of natural selection from sequence data is an ongoing challenge in population genetics. Current methods utilizing information combined from several summary statistics typically assume no correlation of summary statistics...

TreeGrafter: phylogenetic tree-based annotation of proteins with Gene Ontology terms and other annotations.

Bioinformatics (Oxford, England)
SUMMARY: TreeGrafter is a new software tool for annotating protein sequences using pre-annotated phylogenetic trees. Currently, the tool provides annotations to Gene Ontology (GO) terms, and PANTHER family and subfamily. The approach is generalizable...

LigVoxel: inpainting binding pockets using 3D-convolutional neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: Structure-based drug discovery methods exploit protein structural information to design small molecules binding to given protein pockets. This work proposes a purely data driven, structure-based approach for imaging ligands as spatial fie...

Victors: a web-based knowledge base of virulence factors in human and animal pathogens.

Nucleic acids research
Virulence factors (VFs) are molecules that allow microbial pathogens to overcome host defense mechanisms and cause disease in a host. It is critical to study VFs for better understanding microbial pathogenesis and host defense mechanisms. Victors (ht...

A general approach for improving deep learning-based medical relation extraction using a pre-trained model and fine-tuning.

Database : the journal of biological databases and curation
The automatic extraction of meaningful relations from biomedical literature or clinical records is crucial in various biomedical applications. Most of the current deep learning approaches for medical relation extraction require large-scale training d...

Exploring the Scoring Function Space.

Methods in molecular biology (Clifton, N.J.)
In the analysis of protein-ligand interactions, two abstractions have been widely employed to build a systematic approach to analyze these complexes: protein and chemical spaces. The pioneering idea of the protein space dates back to 1970, and the ch...

Machine Learning to Predict Binding Affinity.

Methods in molecular biology (Clifton, N.J.)
Recent progress in the development of scientific libraries with machine-learning techniques paved the way for the implementation of integrated computational tools to predict ligand-binding affinity. The prediction of binding affinity uses the atomic ...

Building Machine-Learning Scoring Functions for Structure-Based Prediction of Intermolecular Binding Affinity.

Methods in molecular biology (Clifton, N.J.)
Molecular docking enables large-scale prediction of whether and how small molecules bind to a macromolecular target. Machine-learning scoring functions are particularly well suited to predict the strength of this interaction. Here we describe how to ...

Machine Learning: Advanced Image Segmentation Using ilastik.

Methods in molecular biology (Clifton, N.J.)
Segmentation is one of the most ubiquitous problems in biological image analysis. Here we present a machine learning-based solution to it as implemented in the open source ilastik toolkit. We give a broad description of the underlying theory and demo...