AIMC Topic: Computational Biology

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Why imaging data alone is not enough: AI-based integration of imaging, omics, and clinical data.

European journal of nuclear medicine and molecular imaging
Artificial intelligence (AI) is currently regaining enormous interest due to the success of machine learning (ML), and in particular deep learning (DL). Image analysis, and thus radiomics, strongly benefits from this research. However, effectively an...

DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences.

PLoS computational biology
Identification of drug-target interactions (DTIs) plays a key role in drug discovery. The high cost and labor-intensive nature of in vitro and in vivo experiments have highlighted the importance of in silico-based DTI prediction approaches. In severa...

Deep Neural Architectures for Highly Imbalanced Data in Bioinformatics.

IEEE transactions on neural networks and learning systems
In the postgenome era, many problems in bioinformatics have arisen due to the generation of large amounts of imbalanced data. In particular, the computational classification of precursor microRNA (pre-miRNA) involves a high imbalance in the classes. ...

Ultrasonic exposure parameters screening in permeability of mycobacterium smegmatis cytoderm induced by cavitation based on artificial neural network identification.

Ultrasonics sonochemistry
The low intensity ultrasound has been adopted by researchers to enhance the bactericidal effect against bacteria in vitro and in vivo. Although the mechanism is not completely understood, one dominant opinion is that the permeability increases becaus...

Identification of leukemia stem cell expression signatures through Monte Carlo feature selection strategy and support vector machine.

Cancer gene therapy
Acute myeloid leukemia (AML) is a type of blood cancer characterized by the rapid growth of immature white blood cells from the bone marrow. Therapy resistance resulting from the persistence of leukemia stem cells (LSCs) are found in numerous patient...

Applications of deep learning for the analysis of medical data.

Archives of pharmacal research
Over the past decade, deep learning has demonstrated superior performances in solving many problems in various fields of medicine compared with other machine learning methods. To understand how deep learning has surpassed traditional machine learning...

Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk.

Nature genetics
We address the challenge of detecting the contribution of noncoding mutations to disease with a deep-learning-based framework that predicts the specific regulatory effects and the deleterious impact of genetic variants. Applying this framework to 1,7...

Gene shaving using a sensitivity analysis of kernel based machine learning approach, with applications to cancer data.

PloS one
BACKGROUND: Gene shaving (GS) is an essential and challenging tools for biomedical researchers due to the large number of genes in human genome and the complex nature of biological networks. Most GS methods are not applicable to non-linear and multi-...