AIMC Topic: Algorithms

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An end-to-end multi-task system of automatic lesion detection and anatomical localization in whole-body bone scintigraphy by deep learning.

Bioinformatics (Oxford, England)
SUMMARY: Limited by spatial resolution and visual contrast, bone scintigraphy interpretation is susceptible to subjective factors, which considerably affects the accuracy and repeatability of lesion detection and anatomical localization. In this work...

Deep learning of protein sequence design of protein-protein interactions.

Bioinformatics (Oxford, England)
MOTIVATION: As more data of experimentally determined protein structures are becoming available, data-driven models to describe protein sequence-structure relationships become more feasible. Within this space, the amino acid sequence design of protei...

New Horizons: Artificial Intelligence for Digital Breast Tomosynthesis.

Radiographics : a review publication of the Radiological Society of North America, Inc
The use of digital breast tomosynthesis (DBT) in breast cancer screening has become widely accepted, facilitating increased cancer detection and lower recall rates compared with those achieved by using full-field digital mammography (DM). However, th...

A deep learning-based recognition for dangerous objects imaged in X-ray security inspection device.

Journal of X-ray science and technology
Several limitations in algorithms and datasets in the field of X-ray security inspection result in the low accuracy of X-ray image inspection. In the literature, there have been rare studies proposed and datasets prepared for the topic of dangerous o...

Prediction of Cancer Treatment Using Advancements in Machine Learning.

Recent patents on anti-cancer drug discovery
Many cancer patients die due to their treatment failing because of their disease's resistance to chemotherapy and other forms of radiation therapy. Resistance may develop at any stage of therapy, even at the beginning. Several factors influence curre...

Modeling of the Acute Lymphoblastic Leukemia Detection by Convolutional Neural Networks (CNNs).

Current medical imaging
BACKGROUND: The techniques differed in many of the literature on the detection of Acute Lymphocytic Leukemia from the blood smear pictures, as the cases of infection in the world and the Kingdom of Saudi Arabia were increasing and the causes of this ...

A Machine Learning-Based Approach Using Multi-omics Data to Predict Metabolic Pathways.

Methods in molecular biology (Clifton, N.J.)
The integrative method approaches are continuously evolving to provide accurate insights from the data that is received through experimentation on various biological systems. Multi-omics data can be integrated with predictive machine learning algorit...

Synthetic Biology Meets Machine Learning.

Methods in molecular biology (Clifton, N.J.)
This chapter outlines the myriad applications of machine learning (ML) in synthetic biology, specifically in engineering cell and protein activity, and metabolic pathways. Though by no means comprehensive, the chapter highlights several prominent com...

Prediction of Breast Cancer Through Random Forest.

Current medical imaging
BACKGROUND: 8% of women are diagnosed with breast cancer. (BC) BC is the second most common cause of death in both developed and undeveloped countries. BC is characterized by the mutation of genes, constant pain, changes in the size, color (redness),...

Application of Machine Learning Technology in the Prediction of ADME- Related Pharmacokinetic Parameters.

Current medicinal chemistry
BACKGROUND: As an important determinant in drug discovery, the accurate analysis and acquisition of pharmacokinetic parameters are very important for the clinical application of drugs. At present, the research and development of new drugs mainly obta...