AIMC Topic: Computational Biology

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Computational Hit Finding: An Industry Perspective.

Journal of medicinal chemistry
Computational hit finding, particularly virtual screening, has been a mainstay of drug discovery campaigns for decades, providing a cost-efficient complement to wet experiments. Innovation in this space slowed considerably as these approaches converg...

Genome-resolved metagenomics from short-read sequencing data in the era of artificial intelligence.

Functional & integrative genomics
Genome-resolved metagenomics is a computational method that enables researchers to reconstruct microbial genomes from a given sample directly. This process involves three major steps, i.e. (i) preprocessing of the reads (ii) metagenome assembly, and ...

Deep-Learning-Based Integration of Sequence and Structure Information for Efficiently Predicting miRNA-Drug Associations.

Journal of chemical information and modeling
Extensive research has shown that microRNAs (miRNAs) play a crucial role in cancer progression, treatment, and drug resistance. They have been recognized as promising potential therapeutic targets for overcoming drug resistance in cancer treatment. H...

Predicting bacterial phenotypic traits through improved machine learning using high-quality, curated datasets.

Communications biology
Predicting prokaryotic phenotypes-observable traits that govern functionality, adaptability, and interactions-holds significant potential for fields such as biotechnology, environmental sciences, and evolutionary biology. In this study, we leverage m...

Predicting Protein Function in the AI and Big Data Era.

Biochemistry
It is an exciting time for researchers working to link proteins to their functions. Most techniques for extracting functional information from genomic sequences were developed several years ago, with major progress driven by the availability of big d...

A deep learning-based method for predicting the frequency classes of drug side effects based on multi-source similarity fusion.

Bioinformatics (Oxford, England)
MOTIVATION: Drug side effects refer to harmful or adverse reactions that occur during drug use, unrelated to the therapeutic purpose. A core issue in drug side effect prediction is determining the frequency of these drug side effects in the populatio...

DeepAllo: allosteric site prediction using protein language model (pLM) with multitask learning.

Bioinformatics (Oxford, England)
MOTIVATION: Allostery, the process by which binding at one site perturbs a distant site, is being rendered as a key focus in the field of drug development with its substantial impact on protein function. The identification of allosteric pockets (site...

CrossAttOmics: multiomics data integration with cross-attention.

Bioinformatics (Oxford, England)
MOTIVATION: Advances in high throughput technologies enabled large access to various types of omics. Each omics provides a partial view of the underlying biological process. Integrating multiple omics layers would help have a more accurate diagnosis....

The signed two-space proximity model for learning representations in protein-protein interaction networks.

Bioinformatics (Oxford, England)
MOTIVATION: Accurately predicting complex protein-protein interactions (PPIs) is crucial for decoding biological processes, from cellular functioning to disease mechanisms. However, experimental methods for determining PPIs are computationally expens...

Multimodal CustOmics: A unified and interpretable multi-task deep learning framework for multimodal integrative data analysis in oncology.

PLoS computational biology
Characterizing cancer presents a delicate challenge as it involves deciphering complex biological interactions within the tumor's microenvironment. Clinical trials often provide histology images and molecular profiling of tumors, which can help under...