AIMC Topic: Machine Learning

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A machine-learning-powered spectral-dominant multimodal soft wearable system for long-term and early-stage diagnosis of plant stresses.

Science advances
Addressing the global malnutrition crisis requires precise and timely diagnostics of plant stresses to enhance the quality and yield of nutrient-rich crops, such as tomatoes. Soft wearable sensors offer a promising approach by continuously monitoring...

Neurons throughout the brain embed robust signatures of their anatomical location into spike trains.

eLife
Neurons in the brain are known to encode diverse information through their spiking activity, primarily reflecting external stimuli and internal states. However, whether individual neurons also embed information about their own anatomical location wit...

Machine learning-based comparison of transperineal vs. transrectal biopsy for prostate cancer diagnosis: evaluating procedural effectiveness.

The Canadian journal of urology
BACKGROUND: Transrectal (TR) and transperineal (TP) biopsies are commonly used methods for diagnosing prostate cancer. However, their comparative effectiveness in conjunction with machine learning (ML) techniques remains underexplored. This study aim...

Machine learning for the prediction of spontaneous preterm birth using early second and third trimester maternal blood gene expression: A cautionary tale.

PloS one
Spontaneous preterm birth (sPTB) remains a significant global health challenge and a leading cause of neonatal mortality and morbidity. Despite advancements in neonatal care, the prediction of sPTB remains elusive, in part due to complex etiologies a...

Machine learning application to predict binding affinity between peptide containing non-canonical amino acids and HLA-A0201.

PloS one
Class Ι major histocompatibility complexes (MHC-Ι), encoded by the highly polymorphic HLA-A, HLA-B, and HLA-C genes in humans, are expressed on all nucleated cells. Both self and foreign proteins are processed to peptides of 8-10 amino acids, loaded ...

Comparison of machine learning and validation methods for high-dimensional accelerometer data to detect foot lesions in dairy cattle.

PloS one
Lameness is one of the major production diseases affecting dairy cattle. It is associated with negative welfare in affected cattle, economic losses at the farm level, and adverse effects on sustainability. Prompt identification of lameness is necessa...

3Mont: A multi-omics integrative tool for breast cancer subtype stratification.

PloS one
Breast Cancer (BRCA) is a heterogeneous disease, and it is one of the most prevalent cancer types among women. Developing effective treatment strategies that address diverse types of BRCA is crucial. Notably, among different BRCA molecular sub-types,...

Body movements as biomarkers: Machine Learning-based prediction of HPA axis reactivity to stress.

Psychoneuroendocrinology
Body movements and posture provide valuable insights into stress responses, yet their relationship with endocrine biomarkers of the stress response remains underexplored. This study investigates whether movement patterns during the Trier Social Stres...

Deep transfer learning radiomics combined with explainable machine learning for preoperative thymoma risk prediction based on CT.

European journal of radiology
OBJECTIVE: To develop and validate a computerized tomography (CT)‑based deep transfer learning radiomics model combined with explainable machine learning for preoperative risk prediction of thymoma.

Using Machine Learning to Match Clients and Therapy Providers: Evaluating Clinical Quality and Cost of Care.

Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research
OBJECTIVES: Matching clients in need of mental healthcare with providers who will deliver high quality treatment presents a substantial challenge. Machine learning models hold potential for predicting the best pairings from a multitude of data points...