AIMC Topic: Pathology, Molecular

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Digital/Computational Technology for Molecular Cytology Testing: A Short Technical Note with Literature Review.

Acta cytologica
This short article describes the method of digital cytopathology using Z-stack scanning with or without extended focusing. This technology is suitable to observe such thick clusters as adenocarcinoma on cytologic specimens. Artificial intelligence (A...

Predicting antimicrobial resistance in Pseudomonas aeruginosa with machine learning-enabled molecular diagnostics.

EMBO molecular medicine
Limited therapy options due to antibiotic resistance underscore the need for optimization of current diagnostics. In some bacterial species, antimicrobial resistance can be unambiguously predicted based on their genome sequence. In this study, we seq...

DeePathology: Deep Multi-Task Learning for Inferring Molecular Pathology from Cancer Transcriptome.

Scientific reports
Despite great advances, molecular cancer pathology is often limited to the use of a small number of biomarkers rather than the whole transcriptome, partly due to computational challenges. Here, we introduce a novel architecture of Deep Neural Network...

An integrated molecular diagnostic report for heart transplant biopsies using an ensemble of diagnostic algorithms.

The Journal of heart and lung transplantation : the official publication of the International Society for Heart Transplantation
BACKGROUND: We previously reported a microarray-based diagnostic system for heart transplant endomyocardial biopsies (EMBs), using either 3-archetype (3AA) or 4-archetype (4AA) unsupervised algorithms to estimate rejection. In the present study we ex...

Deep transfer learning-based hologram classification for molecular diagnostics.

Scientific reports
Lens-free digital in-line holography (LDIH) is a promising microscopic tool that overcomes several drawbacks (e.g., limited field of view) of traditional lens-based microcopy. However, extensive computation is required to reconstruct object images fr...

Role of machine learning in molecular pathology for breast cancer: A review on gene expression profiling and RNA sequencing application.

Critical reviews in oncology/hematology
INTRODUCTION: Breast cancer is the most prevalent cancer among women, with growing incidence and mortality rates. Regardless of remarkable progress in cancer research, breast cancer remains a major concern due to its complex nature. These factors und...

General Applicability of Existing College of American Pathologists Accreditation Requirements to Clinical Implementation of Machine Learning-Based Methods in Molecular Oncology Testing.

Archives of pathology & laboratory medicine
CONTEXT.—: The College of American Pathologists (CAP) accreditation requirements for clinical laboratory testing help ensure laboratories implement and maintain systems and processes that are associated with quality. Machine learning (ML)-based model...

[Next-generation diagnostic pathology].

Zhonghua bing li xue za zhi = Chinese journal of pathology
With the technological progresses and applications of human genome sequencing, bioinformatics analysis and data mining, and molecular pathology and artificial intelligence-assisted pathological diagnosis, the development of clinical medicine is movin...

Obtaining Knowledge in Pathology Reports Through a Natural Language Processing Approach With Classification, Named-Entity Recognition, and Relation-Extraction Heuristics.

JCO clinical cancer informatics
PURPOSE: Robust institutional tumor banks depend on continuous sample curation or else subsequent biopsy or resection specimens are overlooked after initial enrollment. Curation automation is hindered by semistructured free-text clinical pathology no...