TY - GEN
T1 - MoTERNN
T2 - 20th RECOMB Satellite Conference on Comparative Genomics, RECOMB-CG 2023
AU - Edrisi, Mohammadamin
AU - Ogilvie, Huw A.
AU - Li, Meng
AU - Nakhleh, Luay
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - With the advent of single-cell DNA sequencing, it is now possible to infer the evolutionary history of thousands of tumor cells obtained from a single patient. This evolutionary history, which takes the shape of a tree, reveals the mode of evolution of the specific cancer under study and, in turn, helps with clinical diagnosis, prognosis, and therapeutic treatment. In this study we focus on the question of determining the mode of evolution of tumor cells from their inferred evolutionary history. In particular, we employ recursive neural networks that capture tree structures to classify the evolutionary history of tumor cells into one of four modes—linear, branching, neutral, and punctuated. We trained our model, MoTERNN, using simulated data in a supervised fashion and applied it to a real phylogenetic tree obtained from single-cell DNA sequencing data. MoTERNN is implemented in Python and is publicly available at https://github.com/NakhlehLab/MoTERNN.
AB - With the advent of single-cell DNA sequencing, it is now possible to infer the evolutionary history of thousands of tumor cells obtained from a single patient. This evolutionary history, which takes the shape of a tree, reveals the mode of evolution of the specific cancer under study and, in turn, helps with clinical diagnosis, prognosis, and therapeutic treatment. In this study we focus on the question of determining the mode of evolution of tumor cells from their inferred evolutionary history. In particular, we employ recursive neural networks that capture tree structures to classify the evolutionary history of tumor cells into one of four modes—linear, branching, neutral, and punctuated. We trained our model, MoTERNN, using simulated data in a supervised fashion and applied it to a real phylogenetic tree obtained from single-cell DNA sequencing data. MoTERNN is implemented in Python and is publicly available at https://github.com/NakhlehLab/MoTERNN.
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U2 - 10.1007/978-3-031-36911-7_15
DO - 10.1007/978-3-031-36911-7_15
M3 - Conference contribution
AN - SCOPUS:85169024821
SN - 9783031369100
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 232
EP - 247
BT - Comparative Genomics - 20th International Conference, RECOMB-CG 2023, Proceedings
A2 - Jahn, Katharina
A2 - Vinař, Tomáš
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 14 April 2023 through 15 April 2023
ER -