AIMC Topic: Machine Learning

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Transfer learning based deep CNN for segmentation and detection of mitoses in breast cancer histopathological images.

Microscopy (Oxford, England)
Segmentation and detection of mitotic nuclei is a challenging task. To address this problem, a Transfer Learning based fast and accurate system is proposed. To give the classifier a balanced dataset, this work exploits the concept of Transfer Learnin...

BacPaCS-Bacterial Pathogenicity Classification via Sparse-SVM.

Bioinformatics (Oxford, England)
MOTIVATION: Bacterial infections are a major cause of illness worldwide. However, most bacterial strains pose no threat to human health and may even be beneficial. Thus, developing powerful diagnostic bioinformatic tools that differentiate pathogenic...

Bastion3: a two-layer ensemble predictor of type III secreted effectors.

Bioinformatics (Oxford, England)
MOTIVATION: Type III secreted effectors (T3SEs) can be injected into host cell cytoplasm via type III secretion systems (T3SSs) to modulate interactions between Gram-negative bacterial pathogens and their hosts. Due to their relevance in pathogen-hos...

Are we at a crossroads or a plateau? Radiomics and machine learning in abdominal oncology imaging.

Abdominal radiology (New York)
Advances in radiomics and machine learning have driven a technology boom in the automated analysis of radiology images. For the past several years, expectations have been nearly boundless for these new technologies to revolutionize radiology image an...

Human-level performance in 3D multiplayer games with population-based reinforcement learning.

Science (New York, N.Y.)
Reinforcement learning (RL) has shown great success in increasingly complex single-agent environments and two-player turn-based games. However, the real world contains multiple agents, each learning and acting independently to cooperate and compete w...

Dissecting celastrol with machine learning to unveil dark pharmacology.

Chemical communications (Cambridge, England)
By coalescing bespoke machine learning and bioinformatics analyses with cell-based assays, we unveil the pharmacology of celastrol. Celastrol is a direct modulator of the progesterone and cannabinoid receptors, and its effects correlate with the anti...

A novel machine learning-derived decision tree including uPA/PAI-1 for breast cancer care.

Clinical chemistry and laboratory medicine
Background uPA and PAI-1 are breast cancer biomarkers that evaluate the benefit of chemotherapy (CT) for HER2-negative, estrogen receptor-positive, low or intermediate grade patients. Our objectives were to observe clinical routine use of uPA/PAI-1 a...

Survey of Machine Learning Techniques for Prediction of the Isoform Specificity of Cytochrome P450 Substrates.

Current drug metabolism
BACKGROUND: Determination or prediction of the Absorption, Distribution, Metabolism, and Excretion (ADME) properties of drug candidates and drug-induced toxicity plays crucial roles in drug discovery and development. Metabolism is one of the most com...