The computer program forecasts individual strands of DNA building blocks (A, T, C, and G) needed to create desired patterns of gene expression in various types of cells.
A biotech startup that was spun off from UC San Diego uses deep learning to screen for potential antibiotic candidates as a TB outbreak spreads in Kansas.
PD-1 inhibitors and other immune checkpoint inhibitors can be used to treat dozens of different forms of cancer.
A novel method predicts how a certain DNA sequence will organize itself in the cell nucleus in a matter of minutes as opposed to days.
Physicians were required to decide on cancer patients’ treatments, initially without the aid of technology and later with AI’s support.
A recent partnership between Georgia Tech and Northwestern University has created a new high-performance organic electrochemical neuron that responds in the same frequency range as human neurons, opening up new possibilities for the area.
The group tested over 20 million individual cells using both self-supervised learning techniques, then contrasted the outcomes with those of traditional learning techniques.
Researchers from the Technical University of Denmark and the UW Medicine Institute for Protein Design are spearheading a computational biology initiative to find more effective antivenom treatments.
More potent AI models and the amassing of vast amounts of cell data in recent years are beginning to turn biology into a more predictive science.
The researchers tested five different LLMs and discovered that GPT-4 performed the best, finding common functions of curated gene sets from a popular genomics database with an accuracy rate of 73%.
