265 citations
- University of WashingtonUS61 papers
- Cape Town HVTN Immunology Laboratory / Hutchinson Centre Research Institute of South AfricaZA22 papers
- University of California, BerkeleyUS11 papers
- University of California, Los AngelesUS10 papers
- Cancer Research CenterUS9 papers
- Cancer Research And BiostatisticsUS7 papers
- Howard Hughes Medical InstituteUS7 papers
- University of Wisconsin–MadisonUS7 papers
- Max Planck Institute for Dynamics and Self-OrganizationDE6 papers
- University of California, IrvineUS6 papers
- University of AlbertaCA5 papers
- University of PennsylvaniaUS5 papers
14 papers · 1 filter
Robust Estimation and Variable Selection for the Accelerated Failure Time Model
Yi Li, Muxuan Liang, Lu Mao +1
This paper considers robust modeling of the survival time for cancer patients. Accurate prediction can be helpful for developing therapeutic and care strategies. We propose a unifi…
Optimal evolutionary control for artificial selection on molecular phenotypes
Armita Nourmohammad, Ceyhun Eksin
Controlling an evolving population is an important task in modern molecular genetics, including directed evolution for improving the activity of molecules and enzymes, in breeding…
Information-geometric optimization with natural selection
Jakub Otwinowski, Colin LaMont
Evolutionary algorithms, inspired by natural evolution, aim to optimize difficult objective functions without computing derivatives. Here we detail the relationship between populat…
On Data Enriched Logistic Regression
Cheng Zheng, Sayan Dasgupta, Yuxiang Xie +2
Biomedical researchers usually study the effects of certain exposures on disease risks among a well-defined population. To achieve this goal, the gold standard is to design a trial…
On generative models of T-cell receptor sequences
Giulio Isacchini, Zachary Sethna, Yuval Elhanati +3
T-cell receptors (TCR) are key proteins of the adaptive immune system, generated randomly in each individual, whose diversity underlies our ability to recognize infections and mali…
When Two are Better than One: Modeling the Mechanisms of Antibody Mixtures
Tal Einav, Jesse D Bloom
It is difficult to predict how antibodies will behave when mixed together, even after each has been independently characterized. Here, we present a statistical mechanical model for…