Author

K. A. Dill

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Biophysics· 2 Biochemistry· 1 Cancer Biology· 1

Signature Assays, Organisms & Methods

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Approach:statistical model· 3 Assay:bulk RNA-seq· 2 Research Focus:differential expression· 2 Research Focus:drug response / drug discovery· 2 Approach:benchmark study· 1 Research Focus:benchmarking· 1 Organism:C. elegans· 1 Research Focus:cell type annotation· 1

Frequent Co-Authors

C. Weistuch· 2 shared A. de Graff· 1 shared A. Kryshtafovych· 1 shared A. P. Joseph· 1 shared A. Patwardhan· 1 shared A. Perez· 1 shared A. R. Tannenbaum· 1 shared A. Singharoy· 1 shared

Activity by Year

2020-2023 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 23 mutual co-author links

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2 shared papersBiophysics

C. Weistuch

Mutual co-authors in cluster

Author profile →

4 papers

Recent·Most cited

A computational procedure for predicting excipient effects on protein-protein affinities

G. L. Dignon, K. A. Dill

The paper develops a computational model to predict how excipients affect protein-protein affinities by combining MD simulations and thermodynamic theory.

bioRxiv · Dec 22, 2023Biophysics

Transcriptional drift in aging cells: A global de-controller

T. Matsuzaki, C. Weistuch, A. de Graff, K. A. Dill, G. Balazsi

The paper identifies transcriptional drift in aging C. elegans worms, showing upregulated sensory proteins and downregulated growth/metabolism genes, and proposes a mechanistic model involving ligand binding.

bioRxiv · Nov 21, 2023 · PreprintBiophysics

Functional transcriptional signatures for tumor-type-agnostic phenotype prediction

C. Weistuch, K. A. Murgas, J. Zhu, L. Norton, K. A. Dill, A. R. Tannenbaum, J. O. Deasy

The paper identifies six transcriptomic archetypes from normal tissues that correlate with cancer patient survival and drug sensitivity, suggesting cancer adopts normal tissue transcriptional features.

bioRxiv · Apr 12, 2023 · PreprintCancer Biology

Outcomes of the 2019 EMDataResource model challenge: validation of cryo-EM models at near-atomic resolution

C. L. Lawson, A. Kryshtafovych, P. D. Adams, P. Afonine, M. L. Baker, B. A. Barad, P. Bond, T. Burnley, R. Cao, J. Cheng, G. Chojnowski, K. Cowtan, K. A. Dill, F. DiMaio, D. Farrell, , Wan

The paper evaluates cryo-EM model quality using benchmark maps, showing high accuracy and reproducibility across 13 teams and recommending multiple metrics for model assessment.

bioRxiv · Jun 12, 2020Biochemistry