Practice Management

Combining artificial intelligence: deep learning with Hi-C data to predict the functional effects of non-coding variants.

MOTIVATION: Although genome-wide association studies (GWASs) have identified thousands of variants for various traits, the causal variants and the mechanisms underlying the significant loci are largely unknown. In this study, we aim to predict non-coding variants that may functionally affect translation initiation through long-range chromatin interaction.

MLCDForest: multi-label classification with deep forest in disease prediction for long non-coding RNAs.

The long non-coding RNAs (lncRNAs) are subject of intensive recent studies due to its association with various human diseases. It is desirable to build the artificial intelligence-based models for prediction of diseases or tissues based on the lncRNAs data, which will be useful in disease diagnosis and therapy. The accuracy and robustness of existing models based on the machine learning techniques...

DeepATT: a hybrid category attention neural network for identifying functional effects of DNA sequences.

Quantifying DNA properties is a challenging task in the broad field of human genomics. Since the vast majority of non-coding DNA is still poorly under...

[Intelligent fault diagnosis of medical equipment based on long short term memory network].

In order to solve the current problems in medical equipment maintenance, this study proposed an intelligent fault diagnosis method for medical equipme...

Merged Affinity Network Association Clustering: Joint multi-omic/clinical clustering to identify disease endotypes.

Although clinical and laboratory data have long been used to guide medical practice, this information is rarely integrated with multi-omic data to ide...

Bridging across functional models: The OFC as a value-making neural network.

Many functions have been attributed to the orbitofrontal cortex (OFC)-some classical roles, such as signaling the value of action outcomes, being chal...

DeepCPP: a deep neural network based on nucleotide bias information and minimum distribution similarity feature selection for RNA coding potential prediction.

The development of deep sequencing technologies has led to the discovery of novel transcripts. Many in silico methods have been developed to assess th...

Predicting regulatory variants using a dense epigenomic mapped CNN model elucidated the molecular basis of trait-tissue associations.

Assessing the causal tissues of human complex diseases is important for the prioritization of trait-associated genetic variants. Yet, the biological u...

Multimodal Automatic Coding of Client Behavior in Motivational Interviewing.

Motivational Interviewing (MI) is defined as a collaborative conversation style that evokes the client's own intrinsic reasons for behavioral change. ...

DeepLGP: a novel deep learning method for prioritizing lncRNA target genes.

MOTIVATION: Although long non-coding RNAs (lncRNAs) have limited capacity for encoding proteins, they have been verified as biomarkers in the occurren...

Predicting the pathogenicity of protein coding mutations using Natural Language Processing.

DNA-Sequencing of tumor cells has revealed thousands of genetic mutations. However, cancer is caused by only some of them. Identifying mutations that ...

Evaluating Performance and Interpretability of Machine Learning Methods for Predicting Delirium in Gerontopsychiatric Patients.

Delirium is an acute mental disturbance that particularly occurs during hospital stay. Current clinical assessment instruments include the Delirium Ob...

Character-Level Neural Language Modelling in the Clinical Domain.

Word embeddings have become the predominant representation scheme on a token-level for various clinical natural language processing (NLP) tasks. More ...

Supervised Learning for the ICD-10 Coding of French Clinical Narratives.

Automatic detection of ICD-10 codes in clinical documents has become a necessity. In this article, after a brief reminder of the existing work, we pre...

Incorporating natural language processing to improve classification of axial spondyloarthritis using electronic health records.

OBJECTIVES: To develop classification algorithms that accurately identify axial SpA (axSpA) patients in electronic health records, and compare the per...

circDeep: deep learning approach for circular RNA classification from other long non-coding RNA.

MOTIVATION: Over the past two decades, a circular form of RNA (circular RNA), produced through alternative splicing, has become the focus of scientifi...

Recent Advances on the Semi-Supervised Learning for Long Non-Coding RNA-Protein Interactions Prediction: A Review.

In recent years, more and more evidence indicates that long non-coding RNA (lncRNA) plays a significant role in the development of complex biological ...

School of Block-Review of Blockchain for the Radiologists.

Blockchain, the underlying technology for Bitcoin, is a distributed digital ledger technology that enables record verification by many independent par...

Statistical and Machine Learning Methods for eQTL Analysis.

An immense amount of observable diversity exists for all traits and across global populations. In the post-genomic era, equipped with efficient sequen...

Multiplexing of Information about Self and Others in Hippocampal Ensembles.

In addition to coding a subject's location in space, the hippocampus has been suggested to code social information, including the spatial position of ...