AIMC Topic: Algorithms

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De Novo Identification and Visualization of Important Cell Populations for Classic Hodgkin Lymphoma Using Flow Cytometry and Machine Learning.

American journal of clinical pathology
OBJECTIVES: Automated classification of flow cytometry data has the potential to reduce errors and accelerate flow cytometry interpretation. We desired a machine learning approach that is accurate, is intuitively easy to understand, and highlights th...

Mol2Context-vec: learning molecular representation from context awareness for drug discovery.

Briefings in bioinformatics
With the rapid development of proteomics and the rapid increase of target molecules for drug action, computer-aided drug design (CADD) has become a basic task in drug discovery. One of the key challenges in CADD is molecular representation. High-qual...

Learning to SMILES: BAN-based strategies to improve latent representation learning from molecules.

Briefings in bioinformatics
Computational methods have become indispensable tools to accelerate the drug discovery process and alleviate the excessive dependence on time-consuming and labor-intensive experiments. Traditional feature-engineering approaches heavily rely on expert...

Prediction of RBP binding sites on circRNAs using an LSTM-based deep sequence learning architecture.

Briefings in bioinformatics
Circular RNAs (circRNAs) are widely expressed in highly diverged eukaryotes. Although circRNAs have been known for many years, their function remains unclear. Interaction with RNA-binding protein (RBP) to influence post-transcriptional regulation is ...

De novo generation of dual-target ligands using adversarial training and reinforcement learning.

Briefings in bioinformatics
Artificial intelligence, such as deep generative methods, represents a promising solution to de novo design of molecules with the desired properties. However, generating new molecules with biological activities toward two specific targets remains an ...

RefRGim: an intelligent reference panel reconstruction method for genotype imputation with convolutional neural networks.

Briefings in bioinformatics
Genotype imputation is a statistical method for estimating missing genotypes from a denser haplotype reference panel. Existing methods usually performed well on common variants, but they may not be ideal for low-frequency and rare variants. Previous ...

XOmiVAE: an interpretable deep learning model for cancer classification using high-dimensional omics data.

Briefings in bioinformatics
The lack of explainability is one of the most prominent disadvantages of deep learning applications in omics. This 'black box' problem can undermine the credibility and limit the practical implementation of biomedical deep learning models. Here we pr...

Drug sensitivity prediction from cell line-based pharmacogenomics data: guidelines for developing machine learning models.

Briefings in bioinformatics
The goal of precision oncology is to tailor treatment for patients individually using the genomic profile of their tumors. Pharmacogenomics datasets such as cancer cell lines are among the most valuable resources for drug sensitivity prediction, a cr...