Accurately predicting chronological age from DNA methylation patterns is crucial for advancing biological age estimation. However, this task is made challenging by Epigenetic Correlation Drift (ECD) and Heterogeneity Among CpGs (HAC), which reflect the dynamic relationship between methylation and age across different life stages. To address these issues, we propose a novel two-phase algorithm. The first phase employs similarity searching to cluster methylation profiles by age group, while the second phase uses Explainable Boosting Machines (EBM) for precise, group-specific prediction. Our method not only improves prediction accuracy but also reveals key age-related CpG sites, detects age-specific changes in aging rates, and identifies pairwise interactions between CpG sites. Experimental results show that our approach outperforms traditional epigenetic clocks and machine learning models, offering a more accurate and interpretable solution for biological age estimation with significant implications for aging research.

New AI-powered tests tell consumers their biological age by determining the speed at which organs, cells and tissues decline. Startups are launching at-home tests that collect blood, urine or cheek swabs to analyze changes in the'epigenome,' the machinery that helps read the DNA code. Tally Health, one of these companies, recently presented 13 years of research showing that epigenetic changes can be safely reversed in mice to improve the function of tissues, akin to reinstalling cellular software. And the firm believes the same can be done with humans. Elysium also has a biological age test that provides'science-backed recommendations' to help consumers improve bodily functions, hoping to turn back time.

For most of us, when we think of schizophrenia, our minds go back to the movie Sybil starring Sally Field and her multiple personalities. Whether Sybil had the disorder is debatable, but 1% of the world's population diagnosed with schizophrenia suffer from hallucinations, delusion, and cognitive deficits. "Schizophrenia is a devastating disease," said Robert Waterland, PhD, professor of pediatrics-nutrition at Baylor College of Medicine. "Although genetic and environmental components seem to be involved in the condition, current evidence only explains a small portion of cases, suggesting that other factors, such as epigenetic, also could be important." Waterland and his colleagues at the Baylor College of Medicine have now developed an innovative strategy that promises the ability for early diagnosis of schizophrenia.

Summary: Blood tests revealed specific epigenetic biomarkers for schizophrenia. Researchers applied machine learning to analyze the CoRSIVs region of the human genome to identify the schizophrenia biomarkers. Testing the model with an independent data set revealed the AI technology can detect schizophrenia with 80% accuracy. An innovative strategy that analyzes a region of the genome offers the possibility of early diagnosis of schizophrenia, reports a team led by researchers at Baylor College of Medicine. The strategy applied a machine learning algorithm called SPLS-DA to analyze specific regions of the human genome called CoRSIVs, hoping to reveal epigenetic markers for the condition.

A new blood test that can detect more than 50 types of cancer has been revealed by researchers in the latest study to offer hope for early detection. The test is based on DNA that is shed by tumours and found circulating in the blood. More specifically, it focuses on chemical changes to this DNA, known as methylation patterns. Researchers say the test can not only tell whether someone has cancer, but can also shed light on the type of cancer they have. Dr Geoffrey Oxnard of Boston's Dana-Farber Cancer Institute, part of Harvard Medical School, said the test was now being explored in clinical trials.

A blood test developed and checked using blood samples from 4000 people can accurately detect more than 50 cancer types, often before any symptoms appear. It was most accurate at identifying 12 especially dangerous types, including pancreatic cancers that are usually diagnosed only at a very late stage. Many groups around the world are trying to develop blood tests for cancer, often referred to as "liquid biopsies". Michael Seiden at US Oncology, a company involved in cancer care, and his team explored several ways of testing for cancer based on sequencing the DNA that dying cells release into the bloodstream. The team found that looking at methylation patterns at around a million sites was the most promising.

Abstract--A Deep Autoencoder based content retrieval algorithm is proposed for prediction and differentiation of cancer types based on the presence of epigenetic patterns of DNA methylation identified in genetic regions known as CpG islands. The developed deep learning system uses a CpG island state classification subsystem to complete sets of missing/incomplete island data in given human cell lines, and is then pipelined with an intricate set of statistical and signal processing methods to accurately predict the presence of cancer and further differentiate the type and cell of origin in the event of a positive result. The proposed system was trained with previously reported data derived from four case groups of cancer cell lines, achieving overall Sensitivity of 88.24%, Specificity of 83.33%, Accuracy of 84.75% and Matthews Correlation Coefficient of 0.687. The ability to predict and differentiate cancer types using epigenetic events as the identifying patterns was demonstrated in previously reported data sets from breast, lung, lymphoblastic leukemia and urological cancer cell lines, allowing the pipelined system to be robust and adjustable to other cancer cell lines or epigenetic events. Significant progress has been made in understanding crucial regulatory mechanisms responsible for the development and progression of cancer at a cellular and molecular level, through genetic alterations such as DNA mutations and disruptions in epigenetic mechanisms including DNA methylation and histone modifications [1]. Cancer rates have been progressively increasing, with the latest statistics from Cancer Research UK to have reported more than 350,000 new cases diagnosed in the UK [2], of which more than 40% could have been prevented. Cancer research has been significantly progressing with advances in more effective treatments and screening methods, however there is still a pressing need for more targeted methods to be available for monitoring of cancer progression and prevention of treatment resistance that would help control the disease and improve survival rates.

When University of Delaware molecular biologist Adam Marsh was studying the DNA of worms living in Antarctica's frigid seas to understand how the organisms managed to survive--and thrive--in the extremely harsh polar environment, he never imagined his work might one day have a human connection. But it turns out that the genome of these Antarctic worms is very similar to ours in terms of the number and types of genes present. And the pioneering technique Marsh developed to analyze their genetic activity is proving valuable for human health care research. Marsh and a business partner established a biotechnology company to make that technique available for such study. Specifically, Marsh's method uses next-generation genetic sequencing data to measure how cells control the way genes are turned on or off, a process known as DNA methylation.