AIMC Topic: Machine Learning

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A Machine Learning Approach to Identify a Circulating MicroRNA Signature for Alzheimer Disease.

The journal of applied laboratory medicine
BACKGROUND: Accurate diagnosis of Alzheimer disease (AD) involving less invasive molecular procedures and at reasonable cost is an unmet medical need. We identified a serum miRNA signature for AD that is less invasive than a measure in cerebrospinal ...

Studies for Bacterystic Evaluation against of 2-Naphthoic Acid Analogues.

Current topics in medicinal chemistry
BACKGROUND: Staphylococcus aureus is a gram-positive spherical bacterium commonly present in nasal fossae and in the skin of healthy people; however, in high quantities, it can lead to complications that compromise health. The pathologies involved in...

Machine Learning as a Proposal for a Better Application of Food Nanotechnology Regulation in the European Union.

Current topics in medicinal chemistry
AIMS: Given the current gaps of scientific knowledge and the need of efficient application of food law, this paper makes an analysis of principles of European food law for the appropriateness of applying biological activity Machine Learning predictio...

Robust-ODAL: Learning from heterogeneous health systems without sharing patient-level data.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Electronic Health Records (EHR) contain extensive patient data on various health outcomes and risk predictors, providing an efficient and wide-reaching source for health research. Integrated EHR data can provide a larger sample size of the population...

AnomiGAN: Generative Adversarial Networks for Anonymizing Private Medical Data.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Typical personal medical data contains sensitive information about individuals. Storing or sharing the personal medical data is thus often risky. For example, a short DNA sequence can provide information that can identify not only an individual, but ...

Addressing the Credit Assignment Problem in Treatment Outcome Prediction using Temporal Difference Learning.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Mental health patients often undergo a variety of treatments before finding an effective one. Improved prediction of treatment response can shorten the duration of trials. A key challenge of applying predictive modeling to this problem is that often ...

Development of Genome-Derived Tumor Type Prediction to Inform Clinical Cancer Care.

JAMA oncology
IMPORTANCE: Diagnosing the site of origin for cancer is a pillar of disease classification that has directed clinical care for more than a century. Even in an era of precision oncologic practice, in which treatment is increasingly informed by the pre...

Automatic extraction of cancer registry reportable information from free-text pathology reports using multitask convolutional neural networks.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: We implement 2 different multitask learning (MTL) techniques, hard parameter sharing and cross-stitch, to train a word-level convolutional neural network (CNN) specifically designed for automatic extraction of cancer data from unstructured...

Using Machine Learning to Identify True Somatic Variants from Next-Generation Sequencing.

Clinical chemistry
BACKGROUND: Molecular profiling has become essential for tumor risk stratification and treatment selection. However, cancer genome complexity and technical artifacts make identification of real variants a challenge. Currently, clinical laboratories r...