AIMC Topic: Machine Learning

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MV-H-RKM: A Multiple View-Based Hypergraph Regularized Restricted Kernel Machine for Predicting DNA-Binding Proteins.

IEEE/ACM transactions on computational biology and bioinformatics
DNA-binding proteins (DBPs) have a significant impact on many life activities, so identification of DBPs is a crucial issue. And it is greatly helpful to understand the mechanism of protein-DNA interactions. In traditional experimental methods, it is...

Transfer Learning Based Lightweight Ensemble Model for Imbalanced Breast Cancer Classification.

IEEE/ACM transactions on computational biology and bioinformatics
Automated classification of breast cancer can often save lives, as manual detection is usually time-consuming & expensive. Since the last decade, deep learning techniques have been most widely used for the automatic classification of breast cancer us...

Improving Biomedical Question Answering by Data Augmentation and Model Weighting.

IEEE/ACM transactions on computational biology and bioinformatics
Biomedical Question Answering aims to extract an answer to the given question from a biomedical context. Due to the strong professionalism of specific domain, it's more difficult to build large-scale datasets for specific domain question answering. E...

Machine learning-assisted data filtering and QSAR models for prediction of chemical acute toxicity on rat and mouse.

Journal of hazardous materials
Machine learning (ML) methods provide a new opportunity to build quantitative structure-activity relationship (QSAR) models for predicting chemicals' toxicity based on large toxicity data sets, but they are limited in insufficient model robustness du...

Synthetic biology, genetic circuits and machine learning: a new age of cancer therapy.

Molecular oncology
Synthetic biology has made it possible to rewire natural cellular responses to treat disease, notably demonstrated by chimeric antigen receptor (CAR) T cells as cancer immunotherapy. Building on the success of T-cell activation using synthetic recept...

Reconstruction and analysis of negatively buoyant jets with interpretable machine learning.

Marine pollution bulletin
In this paper, negatively inclined buoyant jets, which appear during the discharge of wastewater from processes such as desalination, are observed. A detailed numerical investigation is necessary to minimize harmful effects and assess environmental i...

Clinically explainable machine learning models for early identification of patients at risk of hospital-acquired urinary tract infection.

The Journal of hospital infection
BACKGROUND: Machine learning (ML) models for early identification of patients at risk of hospital-acquired urinary tract infection (HA-UTI) may enable timely and targeted preventive and therapeutic strategies. However, clinicians are often challenged...

Are Deep Neural Networks Adequate Behavioral Models of Human Visual Perception?

Annual review of vision science
Deep neural networks (DNNs) are machine learning algorithms that have revolutionized computer vision due to their remarkable successes in tasks like object classification and segmentation. The success of DNNs as computer vision algorithms has led to ...

Cell damage evaluation by intelligent imaging flow cytometry.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
Essential thrombocythemia (ET) is an uncommon situation in which the body produces too many platelets. This can cause blood clots anywhere in the body and results in various symptoms and even strokes or heart attacks. Removing excessive platelets usi...