AIMC Topic: Workflow

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Artificial Intelligence in Radiotherapy Treatment Planning: Present and Future.

Technology in cancer research & treatment
Treatment planning is an essential step of the radiotherapy workflow. It has become more sophisticated over the past couple of decades with the help of computer science, enabling planners to design highly complex radiotherapy plans to minimize the no...

Building Machine-Learning Scoring Functions for Structure-Based Prediction of Intermolecular Binding Affinity.

Methods in molecular biology (Clifton, N.J.)
Molecular docking enables large-scale prediction of whether and how small molecules bind to a macromolecular target. Machine-learning scoring functions are particularly well suited to predict the strength of this interaction. Here we describe how to ...

Machine Learning: Advanced Image Segmentation Using ilastik.

Methods in molecular biology (Clifton, N.J.)
Segmentation is one of the most ubiquitous problems in biological image analysis. Here we present a machine learning-based solution to it as implemented in the open source ilastik toolkit. We give a broad description of the underlying theory and demo...

Can the Student Outperform the Master? A Plan Comparison Between Pinnacle Auto-Planning and Eclipse knowledge-Based RapidPlan Following a Prostate-Bed Plan Competition.

Technology in cancer research & treatment
PURPOSE: Pinnacle Auto-Planning and Eclipse RapidPlan are 2 major commercial automated planning engines that are fundamentally different: Auto-Planning mimics real planners in the iterative optimization, while RapidPlan generates static dose objectiv...

Information Adapted Machine Learning Models for Prediction in Clinical Workflow.

Studies in health technology and informatics
BACKGROUND: In a database of electronic health records, the amount of available information varies widely between patients. In a real-time prediction scenario, a machine learning model may receive limited information for some patients.

An enhanced workflow for variant interpretation in UniProtKB/Swiss-Prot improves consistency and reuse in ClinVar.

Database : the journal of biological databases and curation
Personalized genomic medicine depends on integrated analyses that combine genetic and phenotypic data from individual patients with reference knowledge of the functional and clinical significance of sequence variants. Sources of this reference knowle...

Precision immunoprofiling to reveal diagnostic signatures for latent tuberculosis infection and reactivation risk stratification.

Integrative biology : quantitative biosciences from nano to macro
Latent tuberculosis infection (LTBI) is estimated in nearly one quarter of the world's population, and of those immunocompetent and infected ~10% will proceed to active tuberculosis (TB). Current diagnostics cannot definitively identify LTBI and prov...

Using Drug Expression Profiles and Machine Learning Approach for Drug Repurposing.

Methods in molecular biology (Clifton, N.J.)
The cost of new drug development has been increasing, and repurposing known medications for new indications serves as an important way to hasten drug discovery. One promising approach to drug repositioning is to take advantage of machine learning (ML...

A Drug Repurposing Method Based on Drug-Drug Interaction Networks and Using Energy Model Layouts.

Methods in molecular biology (Clifton, N.J.)
Complex network representations of reported drug-drug interactions foster computational strategies that can infer pharmacological functions which, in turn, create incentives for drug repositioning. Here, we use Gephi (a platform for complex network v...

Impact of Deep Learning Assistance on the Histopathologic Review of Lymph Nodes for Metastatic Breast Cancer.

The American journal of surgical pathology
Advances in the quality of whole-slide images have set the stage for the clinical use of digital images in anatomic pathology. Along with advances in computer image analysis, this raises the possibility for computer-assisted diagnostics in pathology ...