AI Medical Compendium Journal:
Methods (San Diego, Calif.)

Showing 71 to 80 of 183 articles

DNN-PNN: A parallel deep neural network model to improve anticancer drug sensitivity.

Methods (San Diego, Calif.)
With the rapid development of deep learning techniques and large-scale genomics database, it is of great potential to apply deep learning to the prediction task of anticancer drug sensitivity, which can effectively improve the identification efficien...

Identification of adaptor proteins using the ANOVA feature selection technique.

Methods (San Diego, Calif.)
The adaptor proteins play a crucially important role in regulating lymphocyte activation. Rapid and efficient identification of adaptor proteins is essential for understanding their functions. However, biochemical methods require not only expensive e...

An interpretable deep learning model for classifying adaptor protein complexes from sequence information.

Methods (San Diego, Calif.)
Adaptor proteins (APs) are a family of proteins that aids in intracellular membrane trafficking, and their impairments or defects are closely related to various disorders. Traditional methods to identify and classify APs require time and complex tech...

MultiscaleDTA: A multiscale-based method with a self-attention mechanism for drug-target binding affinity prediction.

Methods (San Diego, Calif.)
The task of predicting drug-target affinity (DTA) plays an increasingly important role in the early stage of in silico drug discovery and development. Currently, a variety of machine learning-based methods have been presented for DTA prediction and a...

A model for predicting ncRNA-protein interactions based on graph neural networks and community detection.

Methods (San Diego, Calif.)
Non-coding RNA (ncRNA) s play an considerable role in the current biological sciences, such as gene transcription, gene expression, etc. Exploring the ncRNA-protein interactions(NPI) is of great significance, while some experimental techniques are ve...

AntiMF: A deep learning framework for predicting anticancer peptides based on multi-view feature extraction.

Methods (San Diego, Calif.)
In recent years, anticancer peptides have emerged as a new viable option in cancer therapy, with the ability to overcome the considerable side effects and poor outcomes of standard cancer therapies. Accurate anticancer peptide identification can faci...

Identification of DNA-binding proteins via Multi-view LSSVM with independence criterion.

Methods (San Diego, Calif.)
DNA-binding proteins actively participate in life activities such as DNA replication, recombination, gene expression and regulation and play a prominent role in these processes. As DNA-binding proteins continue to be discovered and increase, it is im...

GCHN-DTI: Predicting drug-target interactions by graph convolution on heterogeneous networks.

Methods (San Diego, Calif.)
Determining the interaction of drug and target plays a key role in the process of drug development and discovery. The calculation methods can predict new interactions and speed up the process of drug development. In recent studies, the network-based ...

Automated filtering of genome-wide large deletions through an ensemble deep learning framework.

Methods (San Diego, Calif.)
Computational methods based on whole genome linked-reads and short-reads have been successful in genome assembly and detection of structural variants (SVs). Numerous variant callers that rely on linked-reads and short reads can detect genetic variati...