Menopause

Predicting breast cancer risk using personal health data and machine learning models.

Among women, breast cancer is a leading cause of death. Breast cancer risk predictions can inform screening and preventative actions. Previous works found that adding inputs to the widely-used Gail model improved its ability to predict breast cancer risk. However, these models used simple statistical architectures and the additional inputs were derived from costly and / or invasive procedures. By ...

iEnhancer-ECNN: identifying enhancers and their strength using ensembles of convolutional neural networks.

BACKGROUND: Enhancers are non-coding DNA fragments which are crucial in gene regulation (e.g. transcription and translation). Having high locational variation and free scattering in 98% of non-encoding genomes, enhancer identification is, therefore, more complicated than other genetic factors. To address this biological issue, several in silico studies have been done to identify and classify enhan...

Predicting the individual risk of psychosis conversion in at-risk mental state (ARMS): a multivariate model reveals the influence of nonpsychotic prodromal symptoms.

To improve the prediction of the individual risk of conversion to psychosis in UHR subjects, by considering all CAARMS' symptoms at first presentation...

Secretion of equine chorionic gonadotropin and its association with supplementary corpus luteum formation and progesterone concentration in Hokkaido native pony recipient mares.

The objectives of this study were to determine the plasma profile of equine chorionic gonadotropin (eCG) and its association with the formation of sup...

Diagnostic and Gradation Model of Osteoporosis Based on Improved Deep U-Net Network.

The measurement of bone mineral density for osteoporosis has always been the focus of researchers because it plays an important role in bone disease d...

The exploration of feature extraction and machine learning for predicting bone density from simple spine X-ray images in a Korean population.

OBJECTIVE: Osteoporosis is hard to detect before it manifests symptoms and complications. In this study, we evaluated machine learning models for iden...

Exploring the limitations of biophysical propensity scales coupled with machine learning for protein sequence analysis.

Machine learning (ML) is ubiquitous in bioinformatics, due to its versatility. One of the most crucial aspects to consider while training a ML model i...

The Identification of Postovulation Infertility with the Measurement of Early Luteal Phase (Peak Day +3) Progesterone Production.

UNLABELLED: This study reports on 632 cycles from 105 women who were using the CREIGHTON MODEL Fertility™ System to avoid pregnancy and had either a s...

Sperm Tail Defects and Abnormal Testicular Blood Flow in a Beagle Dog: A Case Report.

A 5-year-old male Beagle dog produced ejaculates with a high percentage of spermatozoa with abnormal morphology, especially sperm tail defects. Althou...

Antibiotic therapy in patients with high prostate-specific antigen: Is it worth considering? A systematic review.

: To address the question of whether antibiotic therapy can obviate the need for prostate biopsy (PBx) in patients presenting with high prostate-speci...

The Use of Transdermal Estrogen in Castrate-resistant, Steroid-refractory Prostate Cancer.

BACKGROUND: Androgen-deprivation therapy is the mainstay of treatment for metastatic prostate cancer. Corticosteroids and estrogens are also useful ag...

Efficacy and safety of degarelix in patients with prostate cancer: Results from a phase III study in China.

OBJECTIVE: To establish non-inferiority of gonadotropin-releasing hormone degarelix compared with goserelin in suppressing and maintaining castrate te...

Artificial neural network to estimate micro-architectural properties of cortical bone using ultrasonic attenuation: A 2-D numerical study.

The goal of this study is to estimate micro-architectural parameters of cortical porosity such as pore diameter (φ), pore density (ρ) and porosity (ν)...

Identification of Vertebral Fractures by Convolutional Neural Networks to Predict Nonvertebral and Hip Fractures: A Registry-based Cohort Study of Dual X-ray Absorptiometry.

Background Detection of vertebral fractures (VFs) aids in management of osteoporosis and targeting of fracture prevention therapies. Purpose To determ...

econvRBP: Improved ensemble convolutional neural networks for RNA binding protein prediction directly from sequence.

RNA binding proteins (RBPs) determine RNA process from synthesis to decay, which play a key role in RNA transport, translation and degradation. Theref...

Locomotion Mode Recognition With Robotic Transtibial Prosthesis in Inter-Session and Inter-Day Applications.

Locomotion mode recognition across multiple sessions and days is an indispensable step towards the practical use of the robotic transtibial prosthesis...

A deep convolutional neural network for the estimation of gas chromatographic retention indices.

A deep convolutional neural network was used for the estimation of gas chromatographic retention indices on non-polar (polydimethylsiloxane and polydi...

Deep learning driven QSAR model for environmental toxicology: Effects of endocrine disrupting chemicals on human health.

Over 80,000 endocrine-disrupting chemicals (EDCs) are considered emerging contaminants (ECs), which are of great concern due to their effects on human...

Screening and Assessment of Bone Health in Indian Women Using an Indigenous ELISA of Human Osteocalcin a Bone Turnover Marker.

Osteoporosis a major public health problem of the elderly, is associated with substantial morbidity and socio economic burden. The aim of the study wa...

Analysis of the Deleterious Single Nucleotide Polymorphisms Impact on Estrogen Receptor Alpha-p53 Interaction: A Machine Learning Approach.

Breast cancer is a leading cancer type and one of the major health issues faced by women around the world. Some of its major risk factors include body...