AIMC Topic: Computational Biology

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Obtaining dual-energy computed tomography (CT) information from a single-energy CT image for quantitative imaging analysis of living subjects by using deep learning.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Computed tomographic (CT) is a fundamental imaging modality to generate cross-sectional views of internal anatomy in a living subject or interrogate material composition of an object, and it has been routinely used in clinical applications and nondes...

Multilevel Self-Attention Model and its Use on Medical Risk Prediction.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Various deep learning models have been developed for different healthcare predictive tasks using Electronic Health Records and have shown promising performance. In these models, medical codes are often aggregated into visit representation without con...

Addressing the Credit Assignment Problem in Treatment Outcome Prediction using Temporal Difference Learning.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Mental health patients often undergo a variety of treatments before finding an effective one. Improved prediction of treatment response can shorten the duration of trials. A key challenge of applying predictive modeling to this problem is that often ...

Recent Advancement in Predicting Subcellular Localization of Mycobacterial Protein with Machine Learning Methods.

Medicinal chemistry (Shariqah (United Arab Emirates))
Mycobacterium tuberculosis (MTB) can cause the terrible tuberculosis (TB), which is reported as one of the most dreadful epidemics. Although many biochemical molecular drugs have been developed to cope with this disease, the drug resistance-especiall...

Application of Machine Learning Methods in Predicting Nuclear Receptors and their Families.

Medicinal chemistry (Shariqah (United Arab Emirates))
Nuclear receptors (NRs) are a superfamily of ligand-dependent transcription factors that are closely related to cell development, differentiation, reproduction, homeostasis, and metabolism. According to the alignments of the conserved domains, NRs ar...

A Web-Based Protocol for Interprotein Contact Prediction by Deep Learning.

Methods in molecular biology (Clifton, N.J.)
Identifying residue-residue contacts in protein-protein interactions or complex is crucial for understanding protein and cell functions. DCA (direct-coupling analysis) methods shed some light on this, but they need many sequence homologs to yield acc...

iATP: A Sequence Based Method for Identifying Anti-tubercular Peptides.

Medicinal chemistry (Shariqah (United Arab Emirates))
BACKGROUND: Tuberculosis is one of the biggest threats to human health. Recent studies have demonstrated that anti-tubercular peptides are promising candidates for the discovery of new anti-tubercular drugs. Since experimental methods are still labor...

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

Bioinformatics (Oxford, England)
MOTIVATION: Over the past two decades, a circular form of RNA (circular RNA), produced through alternative splicing, has become the focus of scientific studies due to its major role as a microRNA (miRNA) activity modulator and its association with va...

HUNER: improving biomedical NER with pretraining.

Bioinformatics (Oxford, England)
MOTIVATION: Several recent studies showed that the application of deep neural networks advanced the state-of-the-art in named entity recognition (NER), including biomedical NER. However, the impact on performance and the robustness of improvements cr...

Deep representation learning for domain adaptable classification of infrared spectral imaging data.

Bioinformatics (Oxford, England)
MOTIVATION: Applying infrared microscopy in the context of tissue diagnostics heavily relies on computationally preprocessing the infrared pixel spectra that constitute an infrared microscopic image. Existing approaches involve physical models, which...