AIMC Topic: Machine Learning

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CaSTLe - Classification of single cells by transfer learning: Harnessing the power of publicly available single cell RNA sequencing experiments to annotate new experiments.

PloS one
Single-cell RNA sequencing (scRNA-seq) is an emerging technology for profiling the gene expression of thousands of cells at the single cell resolution. Currently, the labeling of cells in an scRNA-seq dataset is performed by manually characterizing c...

Infant Brain Development Prediction With Latent Partial Multi-View Representation Learning.

IEEE transactions on medical imaging
The early postnatal period witnesses rapid and dynamic brain development. However, the relationship between brain anatomical structure and cognitive ability is still unknown. Currently, there is no explicit model to characterize this relationship in ...

Using a Natural Language Processing and Machine Learning Algorithm Program to Analyze Inter-Radiologist Report Style Variation and Compare Variation Between Radiologists When Using Highly Structured Versus More Free Text Reporting.

Current problems in diagnostic radiology
PURPOSE: To use a natural language processing and machine learning algorithm to evaluate inter-radiologist report variation and compare variation between radiologists using highly structured versus more free text reporting.

Sequence-based analysis and prediction of lantibiotics: A machine learning approach.

Computational biology and chemistry
Lantibiotics, an important group of ribosomally synthesized peptides, represent an important arsenal of novel promising antimicrobials showing high potency in fighting against the prevalence of antibiotic resistance among microbial pathogens. However...

Diagnosis of T-cell-mediated kidney rejection in formalin-fixed, paraffin-embedded tissues using RNA-Seq-based machine learning algorithms.

Human pathology
Molecular diagnosis is being increasingly used in transplant pathology to render more objective and quantitative determinations that also provide mechanistic and prognostic insights. This study performed RNA-Seq on biopsies from kidneys with stable f...

relSCAN - A system for extracting chemical-induced disease relation from biomedical literature.

Journal of biomedical informatics
This paper proposes an effective and robust approach for Chemical-Induced Disease (CID) relation extraction from PubMed articles. The study was performed on the Chemical Disease Relation (CDR) task of BioCreative V track-3 corpus. The proposed system...

Combining High Speed ELM Learning with a Deep Convolutional Neural Network Feature Encoding for Predicting Protein-RNA Interactions.

IEEE/ACM transactions on computational biology and bioinformatics
Emerging evidence has shown that RNA plays a crucial role in many cellular processes, and their biological functions are primarily achieved by binding with a variety of proteins. High-throughput biological experiments provide a lot of valuable inform...

The Rational Discovery of a Tau Aggregation Inhibitor.

Biochemistry
Intrinsically disordered proteins play vital roles in biology, and their dysfunction contributes to many major disease states. These proteins remain challenging targets for rational ligand discovery or drug design because they are highly dynamic and ...