AIMC Topic: Algorithms

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A Deep Learning Algorithm to Identify Anatomical Landmarks on Computed Tomography of the Temporal Bone.

The journal of international advanced otology
BACKGROUND: Petrous temporal bone cone-beam computed tomography scans help aid diagnosis and accurate identification of key operative landmarks in temporal bone and mastoid surgery. Our primary objective was to determine the accuracy of using a deep ...

DeepAFP: An effective computational framework for identifying antifungal peptides based on deep learning.

Protein science : a publication of the Protein Society
Fungal infections have become a significant global health issue, affecting millions worldwide. Antifungal peptides (AFPs) have emerged as a promising alternative to conventional antifungal drugs due to their low toxicity and low propensity for induci...

FG-BERT: a generalized and self-supervised functional group-based molecular representation learning framework for properties prediction.

Briefings in bioinformatics
Artificial intelligence-based molecular property prediction plays a key role in molecular design such as bioactive molecules and functional materials. In this study, we propose a self-supervised pretraining deep learning (DL) framework, called functi...

ProsperousPlus: a one-stop and comprehensive platform for accurate protease-specific substrate cleavage prediction and machine-learning model construction.

Briefings in bioinformatics
Proteases contribute to a broad spectrum of cellular functions. Given a relatively limited amount of experimental data, developing accurate sequence-based predictors of substrate cleavage sites facilitates a better understanding of protease functions...

GOWDL: gene ontology-driven wide and deep learning model for cell typing of scRNA-seq data.

Briefings in bioinformatics
Single-cell RNA-sequencing (scRNA-seq) allows for obtaining genomic and transcriptomic profiles of individual cells. That data make it possible to characterize tissues at the cell level. In this context, one of the main analyses exploiting scRNA-seq ...

Sequence-based prediction model of protein crystallization propensity using machine learning and two-level feature selection.

Briefings in bioinformatics
Protein crystallization is crucial for biology, but the steps involved are complex and demanding in terms of external factors and internal structure. To save on experimental costs and time, the tendency of proteins to crystallize can be initially det...

PredLLPS_PSSM: a novel predictor for liquid-liquid protein separation identification based on evolutionary information and a deep neural network.

Briefings in bioinformatics
The formation of biomolecular condensates by liquid-liquid phase separation (LLPS) has become a universal mechanism for spatiotemporal coordination of biological activities in cells and has been widely observed to directly regulate the key cellular p...

HEAP: a task adaptive-based explainable deep learning framework for enhancer activity prediction.

Briefings in bioinformatics
Enhancers are crucial cis-regulatory elements that control gene expression in a cell-type-specific manner. Despite extensive genetic and computational studies, accurately predicting enhancer activity in different cell types remains a challenge, and t...