AIMC Topic: Machine Learning

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Machine learning combined with multi-omics to identify immune-related LncRNA signature as biomarkers for predicting breast cancer prognosis.

Scientific reports
This study developed an immune-related long non-coding RNAs (lncRNAs)-based prognostic signature by integrating multi-omics data and machine learning algorithms to predict survival and therapeutic responses in breast cancer patients. Utilizing transc...

Prediction of suicide using web based voice recordings analyzed by artificial intelligence.

Scientific reports
The integration of machine learning (ML) and deep learning models in suicide risk assessment has advanced significantly in recent years. In this study, we utilized ML in a case-control design, we predicted completed suicides using publicly available,...

Multi-omics analysis identifies SNP-associated immune-related signatures by integrating Mendelian randomization and machine learning in hepatocellular carcinoma.

Scientific reports
Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death globally, characterized by high morbidity and poor prognosis. The complex molecular and immune landscape of HCC makes accurate patient stratification and personalized treatment...

Sperm metabolomic signatures of asthenozoospermia and teratozoospermia in Chinese reproductive-age men.

Scientific reports
Asthenozoospermia and teratozoospermia are common causes of male infertility. Despite their prevalence, the underlying metabolic mechanisms remain poorly understood. In this study, we conducted targeted metabolomic profiling of sperm samples from 131...

CNN based method for classifying cervical cancer cells in pap smear images.

Scientific reports
The absence of reliable early treatment serves as one of the main causes of cervical cancer. Hence, it is crucial to detect cervical cancer early. The biggest challenge in diagnosing cervical cancer early is that it is asymptomatic until it develops ...

Intelligent brain tumor detection using hybrid finetuned deep transfer features and ensemble machine learning algorithms.

Scientific reports
Brain tumours (BTs) are severe neurological disorders. They affect more than 308,000 people each year worldwide. The mortality rate is over 251,000 deaths annually (IARC, 2020 reports). Detecting BTs is complex because they vary in nature. Early diag...

A robust machine learning approach to predicting remission and stratifying risk in rheumatoid arthritis patients treated with bDMARDs.

Scientific reports
Rheumatoid arthritis (RA) is a chronic autoimmune disease affecting millions worldwide, leading to inflammation, joint damage, and reduced quality of life. Although biological disease-modifying antirheumatic drugs (bDMARDs) are effective, they are co...

Leveraging pathological markers of lower grade glioma to predict the occurrence of secondary epilepsy, a retrospective study.

Scientific reports
Epilepsy is a common manifestation in patients with lower grade glioma (LGG), often presenting as the initial symptom in approximately 70% of cases. This study aimed to identify clinical and pathological markers for epileptic seizures in patients wit...

Integrating AI predictive analytics with naturopathic and yoga-based interventions in a data-driven preventive model to improve maternal mental health and pregnancy outcomes.

Scientific reports
Maternal mental health during pregnancy is a crucial area of research due to its profound impact on both maternal and child well-being. This paper proposes a comprehensive approach to predicting and monitoring psychological health risks in pregnant w...

Multiomic integration reveals subtype-specific predictors of neoadjuvant treatment response in breast cancer.

Science advances
Neoadjuvant therapy has been widely used in breast cancer, but treatment response varies among individuals. We conducted multiomic profiling on tumor samples from 149 Chinese patients with breast cancer across ERHER2, ERHER2, and ERHER2 subtypes, cat...