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A Hybrid Method to Predict Postoperative Survival of Lung Cancer Using Improved SMOTE and Adaptive SVM.

Computational and mathematical methods in medicine
Predicting postoperative survival of lung cancer patients (LCPs) is an important problem of medical decision-making. However, the imbalanced distribution of patient survival in the dataset increases the difficulty of prediction. Although the syntheti...

Development of a single retention time prediction model integrating multiple liquid chromatography systems: Application to new psychoactive substances.

Analytica chimica acta
Database-driven suspect screening has proven to be a useful tool to detect new psychoactive substances (NPS) outside the scope of targeted screening; however, the lack of retention times specific to a liquid chromatography (LC) system can result in a...

Deep learning enables fast and dense single-molecule localization with high accuracy.

Nature methods
Single-molecule localization microscopy (SMLM) has had remarkable success in imaging cellular structures with nanometer resolution, but standard analysis algorithms require sparse emitters, which limits imaging speed and labeling density. Here, we ov...

Weakly supervised underwater fish segmentation using affinity LCFCN.

Scientific reports
Estimating fish body measurements like length, width, and mass has received considerable research due to its potential in boosting productivity in marine and aquaculture applications. Some methods are based on manual collection of these measurements ...

Interpretation of Electrocardiogram Heartbeat by CNN and GRU.

Computational and mathematical methods in medicine
The diagnosis of electrocardiogram (ECG) is extremely onerous and inefficient, so it is necessary to use a computer-aided diagnosis of ECG signals. However, it is still a challenging problem to design high-accuracy ECG algorithms suitable for the med...

CIDO ontology updates and secondary analysis of host responses to COVID-19 infection based on ImmPort reports and literature.

Journal of biomedical semantics
BACKGROUND: With COVID-19 still in its pandemic stage, extensive research has generated increasing amounts of data and knowledge. As many studies are published within a short span of time, we often lose an integrative and comprehensive picture of hos...

Deep Convolutional Neural Networks Help Scoring Tandem Mass Spectrometry Data in Database-Searching Approaches.

Journal of proteome research
Spectrum annotation is a challenging task due to the presence of unexpected peptide fragmentation ions as well as the inaccuracy of the detectors of the spectrometers. We present a deep convolutional neural network, called Slider, which learns an opt...

Accelerating the pace of ecotoxicological assessment using artificial intelligence.

Ambio
Species Sensitivity Distribution (SSD) is a key metric for understanding the potential ecotoxicological impacts of chemicals. However, SSDs have been developed to estimate for only handful of chemicals due to the scarcity of experimental toxicity dat...

CBDPS 1.0: A Python GUI Application for Machine Learning Models to Predict Bitter-Tasting Children's Oral Medicines.

Chemical & pharmaceutical bulletin
Bitter tastes are innately aversive and are thought to help protect animals from consuming poisons. Children are extremely sensitive to drug tastes, and their compliance is especially poor with bitter medicine. Therefore, judging whether a drug is bi...

Automatic Polyp Segmentation in Colonoscopy Images Using a Modified Deep Convolutional Encoder-Decoder Architecture.

Sensors (Basel, Switzerland)
Colorectal cancer has become the third most commonly diagnosed form of cancer, and has the second highest fatality rate of cancers worldwide. Currently, optical colonoscopy is the preferred tool of choice for the diagnosis of polyps and to avert colo...