AIMC Topic: Machine Learning

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Breast cancer detection employing stacked ensemble model with convolutional features.

Cancer biomarkers : section A of Disease markers
Breast cancer is a major cause of female deaths, especially in underdeveloped countries. It can be treated if diagnosed early and chances of survival are high if treated appropriately and timely. For timely and accurate automated diagnosis, machine l...

Machine Learning Strategies for Improved Phenotype Prediction in Underrepresented Populations.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Precision medicine models often perform better for populations of European ancestry due to the over-representation of this group in the genomic datasets and large-scale biobanks from which the models are constructed. As a result, prediction models ma...

Artificial intelligence as a new answer to old challenges in maternal-fetal medicine and obstetrics.

Technology and health care : official journal of the European Society for Engineering and Medicine
BACKGROUND: Following the latest trends in the development of artificial intelligence (AI), the possibility of processing an immense amount of data has created a breakthrough in the medical field. Practitioners can now utilize AI tools to advance dia...

Artificial Intelligence for the Management of Breast Cancer: An Overview.

Current drug discovery technologies
Breast cancer is a severe global health problem, and early detection, accurate diagnosis, and personalized treatment is the key to improving patient outcomes. Artificial intelligence (AI) and machine learning (ML) have emerged as promising breast can...

Identification of Diagnosis and Typological Characteristics Associated with Ferroptosis for Ulcerative Colitis Bioinformatics and Machine Learning.

Endocrine, metabolic & immune disorders drug targets
OBJECTIVE: To investigate and validate ferroptosis genes (FRGs) in ulcerative colitis (UC) for diagnostic, subtype, and biological agent reactivity, with the goal of providing a foundation for the identification of novel therapeutic targets and the r...

Patterns of Gene Expression Profiles Associated with Colorectal Cancer in Colorectal Mucosa by Using Machine Learning Methods.

Combinatorial chemistry & high throughput screening
BACKGROUND: Colorectal cancer (CRC) has a very high incidence and lethality rate and is one of the most dangerous cancer types. Timely diagnosis can effectively reduce the incidence of colorectal cancer. Changes in para-cancerous tissues may serve as...

Determination of Novel SARS-CoV-2 Inhibitors by Combination of Machine Learning and Molecular Modeling Methods.

Medicinal chemistry (Shariqah (United Arab Emirates))
INTRODUCTION: Within the scope of the project, this study aimed to find novel inhibitors by combining computational methods. In order to design inhibitors, it was aimed to produce molecules similar to the RdRp inhibitor drug Favipiravir by using the ...

Prediction of Complications and Prognostication in Perioperative Medicine: A Systematic Review and PROBAST Assessment of Machine Learning Tools.

Anesthesiology
BACKGROUND: The utilization of artificial intelligence and machine learning as diagnostic and predictive tools in perioperative medicine holds great promise. Indeed, many studies have been performed in recent years to explore the potential. The purpo...

Evidence from Machine Learning, Diagnostic Hub Genes in Sepsis and Diagnostic Models based on Xgboost Models, Novel Molecular Models for the Diagnosis of Sepsis.

Current medicinal chemistry
BACKGROUND: Systemic multi-organ dysfunction resulting from dysregulated immune responses in the host triggered by microbial infection or other factors is a major cause of death in sepsis, and secretory pathways play an important role in it.

Hybrid CNN-LSTM for Predicting Diabetes: A Review.

Current diabetes reviews
BACKGROUND: Diabetes is a common and deadly chronic disease caused by high blood glucose levels that can cause heart problems, neurological damage, and other illnesses. Through the early detection of diabetes, patients can live healthier lives. Many ...