AIMC Topic: Computational Biology

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MolLM: a unified language model for integrating biomedical text with 2D and 3D molecular representations.

Bioinformatics (Oxford, England)
MOTIVATION: The current paradigm of deep learning models for the joint representation of molecules and text primarily relies on 1D or 2D molecular formats, neglecting significant 3D structural information that offers valuable physical insight. This n...

CODEX: COunterfactual Deep learning for the in silico EXploration of cancer cell line perturbations.

Bioinformatics (Oxford, England)
MOTIVATION: High-throughput screens (HTS) provide a powerful tool to decipher the causal effects of chemical and genetic perturbations on cancer cell lines. Their ability to evaluate a wide spectrum of interventions, from single drugs to intricate dr...

Unveil cis-acting combinatorial mRNA motifs by interpreting deep neural network.

Bioinformatics (Oxford, England)
SUMMARY: Cis-acting mRNA elements play a key role in the regulation of mRNA stability and translation efficiency. Revealing the interactions of these elements and their impact plays a crucial role in understanding the regulation of the mRNA translati...

Predicting protein functions using positive-unlabeled ranking with ontology-based priors.

Bioinformatics (Oxford, England)
UNLABELLED: Automated protein function prediction is a crucial and widely studied problem in bioinformatics. Computationally, protein function is a multilabel classification problem where only positive samples are defined and there is a large number ...

A machine-learning-based alternative to phylogenetic bootstrap.

Bioinformatics (Oxford, England)
MOTIVATION: Currently used methods for estimating branch support in phylogenetic analyses often rely on the classic Felsenstein's bootstrap, parametric tests, or their approximations. As these branch support scores are widely used in phylogenetic ana...

Enhancing Hi-C contact matrices for loop detection with Capricorn: a multiview diffusion model.

Bioinformatics (Oxford, England)
MOTIVATION: High-resolution Hi-C contact matrices reveal the detailed three-dimensional architecture of the genome, but high-coverage experimental Hi-C data are expensive to generate. Simultaneously, chromatin structure analyses struggle with extreme...

Identifying new cancer genes based on the integration of annotated gene sets via hypergraph neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: Identifying cancer genes remains a significant challenge in cancer genomics research. Annotated gene sets encode functional associations among multiple genes, and cancer genes have been shown to cluster in hallmark signaling pathways and ...

Study of prognostic splicing factors in cancer using machine learning approaches.

Human molecular genetics
Splicing factors (SFs) are the major RNA-binding proteins (RBPs) and key molecules that regulate the splicing of mRNA molecules through binding to mRNAs. The expression of splicing factors is frequently deregulated in different cancer types, causing ...

How has the AI boom impacted algorithmic biology?

Cell systems
This Voices piece will highlight the impact of artificial intelligence on algorithm development among computational biologists. How has worldwide focus on AI changed the path of research in computational biology? What is the impact on the algorithmic...

DEAttentionDTA: protein-ligand binding affinity prediction based on dynamic embedding and self-attention.

Bioinformatics (Oxford, England)
MOTIVATION: Predicting protein-ligand binding affinity is crucial in new drug discovery and development. However, most existing models rely on acquiring 3D structures of elusive proteins. Combining amino acid sequences with ligand sequences and bette...