AIMC Topic: Machine Learning

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AdaSampling for Positive-Unlabeled and Label Noise Learning With Bioinformatics Applications.

IEEE transactions on cybernetics
Class labels are required for supervised learning but may be corrupted or missing in various applications. In binary classification, for example, when only a subset of positive instances is labeled whereas the remaining are unlabeled, positive-unlabe...

Identification of Three Rheumatoid Arthritis Disease Subtypes by Machine Learning Integration of Synovial Histologic Features and RNA Sequencing Data.

Arthritis & rheumatology (Hoboken, N.J.)
OBJECTIVE: In this study, we sought to refine histologic scoring of rheumatoid arthritis (RA) synovial tissue by training with gene expression data and machine learning.

Recognition of protein allosteric states and residues: Machine learning approaches.

Journal of computational chemistry
Allostery is a process by which proteins transmit the effect of perturbation at one site to a distal functional site upon certain perturbation. As an intrinsically global effect of protein dynamics, it is difficult to associate protein allostery with...

A hybrid approach to increase the informedness of CE-based data using locus-specific thresholding and machine learning.

Forensic science international. Genetics
The interpretation of genetic profiles require a robust and reliable method to discriminate true allelic information from noise, regardless of the instrumentation or methods used. Traditionally, static peak detection thresholds (analytical thresholds...

Adversarial Threshold Neural Computer for Molecular de Novo Design.

Molecular pharmaceutics
In this article, we propose the deep neural network Adversarial Threshold Neural Computer (ATNC). The ATNC model is intended for the de novo design of novel small-molecule organic structures. The model is based on generative adversarial network archi...

The Engineering of Chemical Synthesis: Humans and Machines Working in Harmony.

Angewandte Chemie (International ed. in English)
"Chemical synthesis has previously tended to rely heavily on robust labour-intensive processes. We have been advocating a machine-assisted approach to synthesis for many years. To replace a bench chemist with a machine is not only unrealistic but imp...

A survey on Barrett's esophagus analysis using machine learning.

Computers in biology and medicine
This work presents a systematic review concerning recent studies and technologies of machine learning for Barrett's esophagus (BE) diagnosis and treatment. The use of artificial intelligence is a brand new and promising way to evaluate such disease. ...

Assessment of Beer Quality Based on a Robotic Pourer, Computer Vision, and Machine Learning Algorithms Using Commercial Beers.

Journal of food science
UNLABELLED: Sensory attributes of beer are directly linked to perceived foam-related parameters and beer color. The aim of this study was to develop an objective predictive model using machine learning modeling to assess the intensity levels of senso...

MetStabOn-Online Platform for Metabolic Stability Predictions.

International journal of molecular sciences
Metabolic stability is an important parameter to be optimized during the complex process of designing new active compounds. Tuning this parameter with the simultaneous maintenance of a desired compound's activity is not an easy task due to the extrem...

Abnormal brain structure as a potential biomarker for venous erectile dysfunction: evidence from multimodal MRI and machine learning.

European radiology
OBJECTIVES: To investigate the cerebral structural changes related to venous erectile dysfunction (VED) and the relationship of these changes to clinical symptoms and disorder duration and distinguish patients with VED from healthy controls using a m...