AIMC Topic: Algorithms

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A similarity-based deep learning approach for determining the frequencies of drug side effects.

Briefings in bioinformatics
The side effects of drugs present growing concern attention in the healthcare system. Accurately identifying the side effects of drugs is very important for drug development and risk assessment. Some computational models have been developed to predic...

Assessing deep learning methods in cis-regulatory motif finding based on genomic sequencing data.

Briefings in bioinformatics
Identifying cis-regulatory motifs from genomic sequencing data (e.g. ChIP-seq and CLIP-seq) is crucial in identifying transcription factor (TF) binding sites and inferring gene regulatory mechanisms for any organism. Since 2015, deep learning (DL) me...

Representation of molecules for drug response prediction.

Briefings in bioinformatics
The rapid development of machine learning and deep learning algorithms in the recent decade has spurred an outburst of their applications in many research fields. In the chemistry domain, machine learning has been widely used to aid in drug screening...

Machine learning methods, databases and tools for drug combination prediction.

Briefings in bioinformatics
Combination therapy has shown an obvious efficacy on complex diseases and can greatly reduce the development of drug resistance. However, even with high-throughput screens, experimental methods are insufficient to explore novel drug combinations. In ...

Graph representation learning in bioinformatics: trends, methods and applications.

Briefings in bioinformatics
Graph is a natural data structure for describing complex systems, which contains a set of objects and relationships. Ubiquitous real-life biomedical problems can be modeled as graph analytics tasks. Machine learning, especially deep learning, succeed...

Application of Natural Language Processing to Learn Insights on the Clinician's Lived Experience of Electronic Health Records.

Studies in health technology and informatics
We interviewed six clinicians to learn about their lived experience using electronic health records (EHR, Allscripts users) using a semi-structured interview guide in an academic medical center in New York City from October to November 2016. Each par...

Nonconvex Sparse Regularization for Deep Neural Networks and Its Optimality.

Neural computation
Recent theoretical studies proved that deep neural network (DNN) estimators obtained by minimizing empirical risk with a certain sparsity constraint can attain optimal convergence rates for regression and classification problems. However, the sparsit...

EDCNN: identification of genome-wide RNA-binding proteins using evolutionary deep convolutional neural network.

Bioinformatics (Oxford, England)
MOTIVATION: RNA-binding proteins (RBPs) are a group of proteins associated with RNA regulation and metabolism, and play an essential role in mediating the maturation, transport, localization and translation of RNA. Recently, Genome-wide RNA-binding e...

DULoc: quantitatively unmixing protein subcellular location patterns in immunofluorescence images based on deep learning features.

Bioinformatics (Oxford, England)
MOTIVATION: Knowledge of subcellular locations of proteins is of great significance for understanding their functions. The multi-label proteins that simultaneously reside in or move between more than one subcellular structure usually involve with com...

Towards gender equity in artificial intelligence and machine learning applications in dermatology.

Journal of the American Medical Informatics Association : JAMIA
There has been increased excitement around the use of machine learning (ML) and artificial intelligence (AI) in dermatology for the diagnosis of skin cancers and assessment of other dermatologic conditions. As these technologies continue to expand, i...