Lifeboat Foundation: Safeguarding Humanity
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Category: robotics/AI – Page 211

AI could revolutionize early skin cancer detection, treatment

Health care providers can use small devices to hover over moles or lesions and immediately check for common skin cancers, such as melanoma and basal cell carcinoma.

The most significant benefit is that health care professionals who do not specialize in dermatology could perform these checks during a routine visit, making early detection easier and quicker.

Skin cancer is the most common form of cancer in the United States, with one in five Americans expected to be affected in their lifetime, according to the City of Hope Cancer Center.

Recognizing rare microorganisms with an AI-based tool

Identifying rare microorganisms in microbiome data just got easier. A team of researchers from Portugal and Canada has developed a new tool that uses machine learning to automatically detect rare biosphere in ecological datasets.

The aim is to quickly, autonomously and unsupervisedly identify rare microorganisms in microbiome datasets. This new tool, named ulrb, responds to a long-standing challenge in : distinguishing rare microorganisms from the most abundant in natural environments.

The new methodology and the new ulrb software have now been published in the study “Definition of the microbial rare biosphere through unsupervised machine learning” in the journal Communications Biology.

The 2025 AI Index Report

At Stanford HAI, we believe AI is poised to be the most transformative technology of the 21st century. But its benefits won’t be evenly distributed unless we guide its development thoughtfully. The AI Index offers one of the most comprehensive, data-driven views of artificial intelligence. Recognized as a trusted resource by global media, governments, and leading companies, the AI Index equips policymakers, business leaders, and the public with rigorous, objective insights into AI’s technical progress, economic influence, and societal impact.

A geological timescale for bacterial evolution and oxygen adaptation

Microbial life has dominated Earth’s history but left a sparse fossil record, greatly hindering our understanding of evolution in deep time. However, bacterial metabolism has left signatures in the geochemical record, most conspicuously the Great Oxidation Event (GOE). We combine machine learning and phylogenetic reconciliation to infer ancestral bacterial transitions to aerobic lifestyles, linking them to the GOE to calibrate the bacterial time tree. Extant bacterial phyla trace their diversity to the Archaean and Proterozoic, and bacterial families prior to the Phanerozoic. We infer that most bacterial phyla were ancestrally anaerobic and adopted aerobic lifestyles after the GOE. However, in the cyanobacterial ancestor, aerobic metabolism likely predated the GOE, which may have facilitated the evolution of oxygenic photosynthesis.

From social to biological networks: New algorithm uncovers key proteins in human disease

Researchers at Ben-Gurion University of the Negev have developed a machine-learning algorithm that could enhance our understanding of human biology and disease. The new method, Weighted Graph Anomalous Node Detection (WGAND), takes inspiration from social network analysis and is designed to identify proteins with significant roles in various human tissues.

Proteins are essential molecules in our bodies, and they interact with each other in , known as (PPI) networks. Studying these networks helps scientists understand how proteins function and how they contribute to health and disease.

Prof. Esti Yeger-Lotem, Dr. Michael Fire, Dr. Jubran Juman, and Dr. Dima Kagan developed the algorithm to analyze these PPI networks to detect “anomalous” proteins—those that stand out due to their unique pattern of weighted interactions. This implies that the amount of the protein and its protein interactors is greater in that particular network, allowing them to carry out more functions and drive more processes. This also indicates the great importance that these proteins have in a particular network, because the body will not waste energy on their production for no reason.

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