AIMC Topic: Algorithms

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KenDTI: An Ensemble Model for Predicting Drug-Target Interaction by Integrating Multi-Source Information.

IEEE/ACM transactions on computational biology and bioinformatics
The identification of drug-target interactions (DTIs) is an essential step in the process of drug discovery. As experimental validation suffers from high cost and low success rate, various computational models have been exploited to infer potential D...

Deep Bidirectional Classification Model for COVID-19 Disease Infected Patients.

IEEE/ACM transactions on computational biology and bioinformatics
In December of 2019, a novel coronavirus (COVID-19) appeared in Wuhan city, China and has been reported in many countries with millions of people infected within only four months. Chest computed Tomography (CT) has proven to be a useful supplement to...

DeepDRBP-2L: A New Genome Annotation Predictor for Identifying DNA-Binding Proteins and RNA-Binding Proteins Using Convolutional Neural Network and Long Short-Term Memory.

IEEE/ACM transactions on computational biology and bioinformatics
DNA-binding proteins (DBPs) and RNA-binding proteins (RBPs) are two kinds of crucial proteins, which are associated with various cellule activities and some important diseases. Accurate identification of DBPs and RBPs facilitate both theoretical rese...

Semi-Supervised Topological Analysis for Elucidating Hidden Structures in High-Dimensional Transcriptome Datasets.

IEEE/ACM transactions on computational biology and bioinformatics
Topological data analysis (TDA) is a powerful method for reducing data dimensionality, mining underlying data relationships, and intuitively representing the data structure. The Mapper algorithm is one such tool that projects high-dimensional data to...

Discriminant Projection Shared Dictionary Learning for Classification of Tumors Using Gene Expression Data.

IEEE/ACM transactions on computational biology and bioinformatics
With a variety of tumor subtypes, personalized treatments need to identify the subtype of a tumor as accurately as possible. The development of DNA microarrays provides an opportunity to predict tumor classification. One strategy is to use gene expre...

Rapid Reconstruction of Time-varying Gene Regulatory Networks with Limited Main Memory.

IEEE/ACM transactions on computational biology and bioinformatics
Reconstruction of time-varying gene regulatory networks underlying a time-series gene expression data is a fundamental challenge in the computational systems biology. The challenge increases multi-fold if the target networks need to be constructed fo...

Hybrid Machine Learning Models for Predicting Types of Human T-cell Lymphotropic Virus.

IEEE/ACM transactions on computational biology and bioinformatics
Life threatening diseases like adult T-cell leukemia, neurodegenerative diseases, and demyelinating diseases such as HTLV-1 based myelopathy/tropical spastic paraparesis (HAM/TSP), hypocalcaemia, and bone lesions are caused by a group of human retrov...

A Convolutional Neural Network System to Discriminate Drug-Target Interactions.

IEEE/ACM transactions on computational biology and bioinformatics
Biological targets are most commonly proteins such as enzymes, ion channels, and receptors. They are anything within a living organism to bind with some other entities (like an endogenous ligand or a drug), resulting in change in their behaviors or f...

Artificial intelligence in orthopedic implant model classification: a systematic review.

Skeletal radiology
Although artificial intelligence models have demonstrated high accuracy in identifying specific orthopedic implant models from imaging, which is an important and time-consuming task, the scope of prior works and performance of prior models have not b...

Present Limitations of Artificial Intelligence in the Emergency Setting - Performance Study of a Commercial, Computer-Aided Detection Algorithm for Pulmonary Embolism.

RoFo : Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin
PURPOSE:  Since artificial intelligence is transitioning from an experimental stage to clinical implementation, the aim of our study was to evaluate the performance of a commercial, computer-aided detection algorithm of computed tomography pulmonary ...