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Protein Interaction Maps

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Characterization of Antiphospholipid Syndrome Atherothrombotic Risk by Unsupervised Integrated Transcriptomic Analyses.

Arteriosclerosis, thrombosis, and vascular biology
OBJECTIVE: Our aim was to characterize distinctive clinical antiphospholipid syndrome phenotypes and identify novel microRNA (miRNA)-mRNA-intracellular signaling regulatory networks in monocytes linked to cardiovascular disease. Approach and Results:...

Inter-protein residue covariation information unravels physically interacting protein dimers.

BMC bioinformatics
BACKGROUND: Predicting physical interaction between proteins is one of the greatest challenges in computational biology. There are considerable various protein interactions and a huge number of protein sequences and synthetic peptides with unknown in...

Ensembling of Gene Clusters Utilizing Deep Learning and Protein-Protein Interaction Information.

IEEE/ACM transactions on computational biology and bioinformatics
Cluster ensemble techniques aim to combine the outputs of multiple clustering algorithms to obtain a single consensus partitioning. The current paper reports about the development of a cluster ensemble based technique combining the concepts of multio...

Deep Learning Benchmarks on L1000 Gene Expression Data.

IEEE/ACM transactions on computational biology and bioinformatics
Gene expression data can offer deep, physiological insights beyond the static coding of the genome alone. We believe that realizing this potential requires specialized, high-capacity machine learning methods capable of using underlying biological str...

DeepciRGO: functional prediction of circular RNAs through hierarchical deep neural networks using heterogeneous network features.

BMC bioinformatics
BACKGROUND: Circular RNAs (circRNAs) are special noncoding RNA molecules with closed loop structures. Compared with the traditional linear RNA, circRNA is more stable and not easily degraded. Many studies have shown that circRNAs are involved in the ...

Quantitative Structure-Mutation-Activity Relationship Tests (QSMART) model for protein kinase inhibitor response prediction.

BMC bioinformatics
BACKGROUND: Protein kinases are a large family of druggable proteins that are genomically and proteomically altered in many human cancers. Kinase-targeted drugs are emerging as promising avenues for personalized medicine because of the differential r...

Matrix Factorization-based Technique for Drug Repurposing Predictions.

IEEE journal of biomedical and health informatics
Classical drug design methodologies are hugely costly and time-consuming, with approximately 85% of the new proposed molecules failing in the first three phases of the FDA drug approval process. Thus, strategies to find alternative indications for al...

Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients.

Nature communications
Cancer patient classification using predictive biomarkers for anti-cancer drug responses is essential for improving therapeutic outcomes. However, current machine-learning-based predictions of drug response often fail to identify robust translational...

A Comprehensive Analysis of MicroRNAs in Human Osteoporosis.

Frontiers in endocrinology
MicroRNAs (miRNAs) are single-stranded RNA molecules that control gene expression in various processes, such as cancers, Alzheimer's disease, and bone metabolic diseases. However, the regulatory roles of miRNAs in osteoporosis have not been systemati...

Robust edge-based biomarker discovery improves prediction of breast cancer metastasis.

BMC bioinformatics
BACKGROUND: The abundance of molecular profiling of breast cancer tissues entailed active research on molecular marker-based early diagnosis of metastasis. Recently there is a surging interest in combining gene expression with gene networks such as p...