AIMC Topic: Computational Biology

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Machine-Learning Prediction of Tumor Antigen Immunogenicity in the Selection of Therapeutic Epitopes.

Cancer immunology research
Current tumor neoantigen calling algorithms primarily rely on epitope/major histocompatibility complex (MHC) binding affinity predictions to rank and select for potential epitope targets. These algorithms do not predict for epitope immunogenicity usi...

LDAPred: A Method Based on Information Flow Propagation and a Convolutional Neural Network for the Prediction of Disease-Associated lncRNAs.

International journal of molecular sciences
Long non-coding RNAs (lncRNAs) play a crucial role in the pathogenesis and development of complex diseases. Predicting potential lncRNA-disease associations can improve our understanding of the molecular mechanisms of human diseases and help identify...

econvRBP: Improved ensemble convolutional neural networks for RNA binding protein prediction directly from sequence.

Methods (San Diego, Calif.)
RNA binding proteins (RBPs) determine RNA process from synthesis to decay, which play a key role in RNA transport, translation and degradation. Therefore, exploring RBPs' function from the amino acid sequence using computational methods has become on...

Designing combination therapies with modeling chaperoned machine learning.

PLoS computational biology
Chemotherapy resistance is a major challenge to the effective treatment of cancer. Thus, a systematic pipeline for the efficient identification of effective combination treatments could bring huge biomedical benefit. In order to facilitate rational d...

Predicting Drug-Target Interaction Using a Novel Graph Neural Network with 3D Structure-Embedded Graph Representation.

Journal of chemical information and modeling
We propose a novel deep learning approach for predicting drug-target interaction using a graph neural network. We introduce a distance-aware graph attention algorithm to differentiate various types of intermolecular interactions. Furthermore, we extr...

PTPD: predicting therapeutic peptides by deep learning and word2vec.

BMC bioinformatics
*: Background In the search for therapeutic peptides for disease treatments, many efforts have been made to identify various functional peptides from large numbers of peptide sequence databases. In this paper, we propose an effective computational mo...

Protein model accuracy estimation based on local structure quality assessment using 3D convolutional neural network.

PloS one
In protein tertiary structure prediction, model quality assessment programs (MQAPs) are often used to select the final structural models from a pool of candidate models generated by multiple templates and prediction methods. The 3-dimensional convolu...

Prediction and Experimental Confirmation of Novel Peripheral Cannabinoid-1 Receptor Antagonists.

Journal of chemical information and modeling
Small molecules targeting peripheral CB1 receptors have therapeutic potential in a variety of disorders including obesity-related, hormonal, and metabolic abnormalities, while avoiding the psychoactive effects in the central nervous system. We applie...

Deep learning extends de novo protein modelling coverage of genomes using iteratively predicted structural constraints.

Nature communications
The inapplicability of amino acid covariation methods to small protein families has limited their use for structural annotation of whole genomes. Recently, deep learning has shown promise in allowing accurate residue-residue contact prediction even f...