AIMC Topic: Algorithms

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A convolutional autoencoder approach for mining features in cellular electron cryo-tomograms and weakly supervised coarse segmentation.

Journal of structural biology
Cellular electron cryo-tomography enables the 3D visualization of cellular organization in the near-native state and at submolecular resolution. However, the contents of cellular tomograms are often complex, making it difficult to automatically isola...

Natural language processing of clinical notes for identification of critical limb ischemia.

International journal of medical informatics
BACKGROUND: Critical limb ischemia (CLI) is a complication of advanced peripheral artery disease (PAD) with diagnosis based on the presence of clinical signs and symptoms. However, automated identification of cases from electronic health records (EHR...

Novel real-time tumor-contouring method using deep learning to prevent mistracking in X-ray fluoroscopy.

Radiological physics and technology
Robustness to obstacles is the most important factor necessary to achieve accurate tumor tracking without fiducial markers. Some high-density structures, such as bone, are enhanced on X-ray fluoroscopic images, which cause tumor mistracking. Tumor tr...

Deep Convolutional Neural Networks Enable Discrimination of Heterogeneous Digital Pathology Images.

EBioMedicine
Pathological evaluation of tumor tissue is pivotal for diagnosis in cancer patients and automated image analysis approaches have great potential to increase precision of diagnosis and help reduce human error. In this study, we utilize several computa...

Stable solution to l -based robust inductive matrix completion and its application in linking long noncoding RNAs to human diseases.

BMC medical genomics
BACKGROUNDS: A large number of long intergenic non-coding RNAs (lincRNAs) are linked to a broad spectrum of human diseases. The disease association with many other lincRNAs still remain as puzzle. Validation of such links between the two entities thr...

CNN-BLPred: a Convolutional neural network based predictor for β-Lactamases (BL) and their classes.

BMC bioinformatics
BACKGROUND: The β-Lactamase (BL) enzyme family is an important class of enzymes that plays a key role in bacterial resistance to antibiotics. As the newly identified number of BL enzymes is increasing daily, it is imperative to develop a computationa...

Identifying term relations cross different gene ontology categories.

BMC bioinformatics
BACKGROUND: The Gene Ontology (GO) is a community-based bioinformatics resource that employs ontologies to represent biological knowledge and describes information about gene and gene product function. GO includes three independent categories: molecu...

Dependency-based long short term memory network for drug-drug interaction extraction.

BMC bioinformatics
BACKGROUND: Drug-drug interaction extraction (DDI) needs assistance from automated methods to address the explosively increasing biomedical texts. In recent years, deep neural network based models have been developed to address such needs and they ha...

A sparse autoencoder-based deep neural network for protein solvent accessibility and contact number prediction.

BMC bioinformatics
BACKGROUND: Direct prediction of the three-dimensional (3D) structures of proteins from one-dimensional (1D) sequences is a challenging problem. Significant structural characteristics such as solvent accessibility and contact number are essential for...

2D-EM clustering approach for high-dimensional data through folding feature vectors.

BMC bioinformatics
BACKGROUND: Clustering methods are becoming widely utilized in biomedical research where the volume and complexity of data is rapidly increasing. Unsupervised clustering of patient information can reveal distinct phenotype groups with different under...