Obstetrics/Gynecology


Eugenics 2.0: We're at the dawn of choosing embryos by health, height, and more

#artificialintelligence

Nathan Treff was diagnosed with type 1 diabetes at 24. It's a disease that runs in families, but it has complex causes. More than one gene is involved. And the environment plays a role too. So you don't know who will get it.


Eugenics 2.0: We're at the Dawn of Choosing Embryos by Health, Height, and More

MIT Technology Review

Nathan Treff was diagnosed with type 1 diabetes at 24. It's a disease that runs in families, but it has complex causes. More than one gene is involved. And the environment plays a role too. So you don't know who will get it.


Predicting Breast Cancer Using Apache Spark Machine Learning Logistic Regression

@machinelearnbot

Then we use another map transformation, which will apply the ParseObs function to transform each Array of Double in the RDD into an Array of Cancer Observation objects. The toDF() method transforms the RDD of Array[[Cancer Observation]] into a Dataframe with the Cancer Observation class schema. Below the data is split into a training data set and a test data set, 70% of the data is used to train the model, and 30% will be used for testing. In this blog post, we showed you how to get started using Apache Spark's machine learning Logistic Regression for classification.


Proposed machine learning-based framework predicts FGR pregnancies with high accuracy

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Using three-dimensional (3D) magnetic resonance imaging (MRI), the research team characterized the shape, volume, morphometry and texture of placentas during pregnancy and, using a novel framework, predicted with high accuracy which pregnancies would be complicated by fetal growth restriction (FGR). The Children's research team acquired 124 fetal scans from 80 pregnancies beginning at the 18th gestational week and continuing through the 39th gestational week. Forty-six women had normal pregnancies and healthy fetuses while 34 women's pregnancies were complicated by FGR, defined by estimated fetal weight that fell below the 10th percentile for gestational age. The proposed machine learning-based framework distinguished healthy pregnancies from FGR pregnancies with 86 percent accuracy and 87 percent specificity.


You have a lot to teach your grandkids, and that might explain menopause

Popular Science

Using a computational model, the researchers found that older women's ability to "grandmother"--that is, devote their resources to grandchildren--and use their cognitive abilities to support their offspring may have been crucial to the evolution of menopause Existing hypotheses suggest that menopause protects humans from risky pregnancies at an older age (the Maternal Hypothesis) or might allow older mothers to invest their energy in supporting the survival of their grandchildren (the Grandmother Hypothesis). For example, to test whether the model supported the Grandmother Hypothesis, they removed the variables allowing the woman to offer more support to children who had more children of their own. When the neural network's model prevented women from caring for their grandchildren--or assumed that cognitive resources didn't affect offspring's skills--menopause did not evolve and women continued reproducing into old age. Aime acknowledges that there are some limitations to the team's neural network model--for example, it only simulates women, so in future studies, she hopes to expand the model to include men.


Unsupervised feature construction and knowledge extraction from genome-wide assays of breast cancer with denoising autoencoders. - PubMed - NCBI

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While both supervised learning algorithms and unsupervised clustering algorithms have been successfully applied to biological data, they are either dependent on known biology or limited to discerning the most significant signals in the data. We evaluate the performance of DAs by applying them to a large collection of breast cancer gene expression data. Results show that DAs successfully construct features that contain both clinical and molecular information. In summary, we demonstrate that DAs effectively extract key biological principles from gene expression data and summarize them into constructed features with convenient properties.


Predicting Breast Cancer Using Apache Spark Machine Learning Logistic Regression

#artificialintelligence

Then we use another map transformation, which will apply the ParseObs function to transform each Array of Double in the RDD into an Array of Cancer Observation objects. The toDF() method transforms the RDD of Array[[Cancer Observation]] into a Dataframe with the Cancer Observation class schema. Below the data is split into a training data set and a test data set, 70% of the data is used to train the model, and 30% will be used for testing. In this blog post, we showed you how to get started using Apache Spark's machine learning Logistic Regression for classification.


Google's Machine Learning, Imaging Analytics Flag Breast Cancer

#artificialintelligence

"We envision that algorithm such as ours could improve the efficiency and consistency of pathologists," said Stumpe and Peng. "For example, pathologists could reduce their false negative rates (percentage of undetected tumors) by reviewing the top ranked predicted tumor regions including up to 8 false positive regions per slide. As another example, these algorithms could enable pathologists to easily and accurately measure tumor size, a factor that is associated with prognosis."


Google uses AI to help diagnose breast cancer

#artificialintelligence

Google announced Friday that it has achieved state-of-the-art results in using artificial intelligence to identify breast cancer. Google used a flavor of artificial intelligence called deep learning to analyze thousands of slides of cancer cells provided by a Dutch university. With 230,000 new cases of breast cancer every year in the United States, Google (GOOGL, Tech30) hopes its technology will help pathologists better treat patients. His university provided the slides for Google's research.


Google uses AI to help diagnose breast cancer

#artificialintelligence

Google announced Friday that it has achieved state-of-the-art results in using artificial intelligence to identify breast cancer. Google used a flavor of artificial intelligence called deep learning to analyze thousands of slides of cancer cells provided by a Dutch university. With 230,000 new cases of breast cancer every year in the United States, Google (GOOGL, Tech30) hopes its technology will help pathologists better treat patients. His university provided the slides for Google's research.