AIMC Topic: Protein Interaction Maps

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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...

DeepAdd: Protein function prediction from k-mer embedding and additional features.

Computational biology and chemistry
With the application of new high throughput sequencing technology, a large number of protein sequences is becoming available. Determination of the functional characteristics of these proteins by experiments is an expensive endeavor that requires a lo...

Identification of Latent Oncogenes with a Network Embedding Method and Random Forest.

BioMed research international
Oncogene is a special type of genes, which can promote the tumor initiation. Good study on oncogenes is helpful for understanding the cause of cancers. Experimental techniques in early time are quite popular in detecting oncogenes. However, their def...

DeepHE: Accurately predicting human essential genes based on deep learning.

PLoS computational biology
Accurately predicting essential genes using computational methods can greatly reduce the effort in finding them via wet experiments at both time and resource scales, and further accelerate the process of drug discovery. Several computational methods ...

Association study based on topological constraints of protein-protein interaction networks.

Scientific reports
The non-random interaction pattern of a protein-protein interaction network (PIN) is biologically informative, but its potentials have not been fully utilized in omics studies. Here, we propose a network-permutation-based association study (NetPAS) m...

Predicting protein subcellular location with network embedding and enrichment features.

Biochimica et biophysica acta. Proteins and proteomics
The subcellular location of a protein is highly related to its function. Identifying the location of a given protein is an essential step for investigating its related problems. Traditional experimental methods can produce solid determination. Howeve...

Exploring the mechanism of TCM formulae in the treatment of different types of coronary heart disease by network pharmacology and machining learning.

Pharmacological research
Traditional Chinese medicine (TCM) has long been used in the clinical treatment of coronary heart disease (CHD). TCM is characterized by syndrome-based medication, which is, using different TCM formulae for different syndromes. However, the underlyin...