Search Results - "Rampasek, Ladislav"

TensorFlow: Biology's Gateway to Deep Learning? by Rampasek, Ladislav, Goldenberg, Anna

“…TensorFlow is Google's recently released open-source software for deep learning. What are its applications for computational biology?…”
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Assessing therapy response in patient-derived xenografts by Ortmann, Janosch, Rampášek, Ladislav, Tai, Elijah, Mer, Arvind Singh, Shi, Ruoshi, Stewart, Erin L, Mascaux, Celine, Fares, Aline, Pham, Nhu-An, Beri, Gangesh, Eeles, Christopher, Tkachuk, Denis, Ho, Chantal, Sakashita, Shingo, Weiss, Jessica, Jiang, Xiaoqian, Liu, Geoffrey, Cescon, David W, O'Brien, Catherine A, Guo, Sheng, Tsao, Ming-Sound, Haibe-Kains, Benjamin, Goldenberg, Anna

“…Quantifying response to drug treatment in mouse models of human cancer is important for treatment development and assignment, yet remains a challenging task…”
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Dr.VAE: improving drug response prediction via modeling of drug perturbation effects by Rampášek, Ladislav, Hidru, Daniel, Smirnov, Petr, Haibe-Kains, Benjamin, Goldenberg, Anna

“…Abstract Motivation Individualized drug response prediction is a fundamental part of personalized medicine for cancer. Great effort has been made to discover…”
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Machine learning approaches to drug response prediction: challenges and recent progress by Adam, George, Rampášek, Ladislav, Safikhani, Zhaleh, Smirnov, Petr, Haibe-Kains, Benjamin, Goldenberg, Anna

“…Cancer is a leading cause of death worldwide. Identifying the best treatment using computational models to personalize drug response prediction holds great…”
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Learning from Everyday Images Enables Expert-like Diagnosis of Retinal Diseases by Rampasek, Ladislav, Goldenberg, Anna

“…Kermany et al. report an application of a neural network trained on millions of everyday images to a database of thousands of retinal tomography images that…”
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Cell-free DNA fragment-size distribution analysis for non-invasive prenatal CNV prediction by Arbabi, Aryan, Rampášek, Ladislav, Brudno, Michael

“…Non-invasive detection of aneuploidies in a fetal genome through analysis of cell-free DNA circulating in the maternal plasma is becoming a routine clinical…”
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Probabilistic method for detecting copy number variation in a fetal genome using maternal plasma sequencing by Rampášek, Ladislav, Arbabi, Aryan, Brudno, Michael

“…The past several years have seen the development of methodologies to identify genomic variation within a fetus through the non-invasive sequencing of maternal…”
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Intertumoral Heterogeneity within Medulloblastoma Subgroups by Cavalli, Florence M.G., Remke, Marc, Rampasek, Ladislav, Peacock, John, Shih, David J.H., Luu, Betty, Garzia, Livia, Torchia, Jonathon, Nor, Carolina, Morrissy, A. Sorana, Agnihotri, Sameer, Thompson, Yuan Yao, Kuzan-Fischer, Claudia M., Farooq, Hamza, Isaev, Keren, Daniels, Craig, Cho, Byung-Kyu, Kim, Seung-Ki, Wang, Kyu-Chang, Lee, Ji Yeoun, Grajkowska, Wieslawa A., Perek-Polnik, Marta, Vasiljevic, Alexandre, Faure-Conter, Cecile, Jouvet, Anne, Giannini, Caterina, Nageswara Rao, Amulya A., Li, Kay Ka Wai, Ng, Ho-Keung, Eberhart, Charles G., Pollack, Ian F., Hamilton, Ronald L., Gillespie, G. Yancey, Olson, James M., Leary, Sarah, Weiss, William A., Lach, Boleslaw, Chambless, Lola B., Thompson, Reid C., Cooper, Michael K., Vibhakar, Rajeev, Hauser, Peter, van Veelen, Marie-Lise C., Kros, Johan M., French, Pim J., Ra, Young Shin, Kumabe, Toshihiro, López-Aguilar, Enrique, Zitterbart, Karel, Sterba, Jaroslav, Finocchiaro, Gaetano, Massimino, Maura, Van Meir, Erwin G., Osuka, Satoru, Shofuda, Tomoko, Klekner, Almos, Zollo, Massimo, Leonard, Jeffrey R., Rubin, Joshua B., Jabado, Nada, Albrecht, Steffen, Mora, Jaume, Van Meter, Timothy E., Jung, Shin, Moore, Andrew S., Hallahan, Andrew R., Chan, Jennifer A., Tirapelli, Daniela P.C., Carlotti, Carlos G., Fouladi, Maryam, Pimentel, José, Faria, Claudia C., Saad, Ali G., Massimi, Luca, Liau, Linda M., Wheeler, Helen, Nakamura, Hideo, Elbabaa, Samer K., Perezpeña-Diazconti, Mario, Chico Ponce de León, Fernando, Robinson, Shenandoah, Zapotocky, Michal, Lassaletta, Alvaro, Huang, Annie, Hawkins, Cynthia E., Tabori, Uri, Bouffet, Eric, Bartels, Ute, Dirks, Peter B., Rutka, James T., Bader, Gary D., Reimand, Jüri, Goldenberg, Anna, Ramaswamy, Vijay, Taylor, Michael D.

“…While molecular subgrouping has revolutionized medulloblastoma classification, the extent of heterogeneity within subgroups is unknown. Similarity network…”
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RNA motif search with data-driven element ordering by Rampášek, Ladislav, Jimenez, Randi M, Lupták, Andrej, Vinař, Tomáš, Brejová, Broňa

“…In this paper, we study the problem of RNA motif search in long genomic sequences. This approach uses a combination of sequence and structure constraints to…”
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Latent-Variable Models for Drug Response Prediction and Genetic Testing by Rampasek, Ladislav

“…High-throughput DNA sequencing and related biotechnologies revolutionized our understanding of human genomics and diseases with genetic component, particularly…”
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MB-87INTEGRATED GENOMICS REVEALS NOVEL SUBTYPES OF MEDULLOBLASTOMA SUBGROUPS by Cavalli, Florence M.G., Remke, Marc, Reimand, Jüri, Rampasek, Ladislav, Goldenberg, Anna, Taylor, Michael, Ramaswamy, Vijay

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Hierarchical Graph Neural Nets can Capture Long-Range Interactions by Rampasek, Ladislav, Wolf, Guy

“…Graph neural networks (GNNs) based on message passing between neighboring nodes are known to be insufficient for capturing long-range interactions in graphs…”
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Dropout Feature Ranking for Deep Learning Models by Chang, Chun-Hao, Rampasek, Ladislav, Goldenberg, Anna

“…Deep neural networks (DNNs) achieve state-of-the-art results in a variety of domains. Unfortunately, DNNs are notorious for their non-interpretability, and…”
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Machine learning approaches to drug response prediction: challenges and recent progress by Adam, George, Rampášek, Ladislav, Safikhani, Zhaleh, Smirnov, Petr, Haibe-Kains, Benjamin, Goldenberg, Anna

“…Cancer is a leading cause of death worldwide. Identifying the best treatment using computational models to personalize drug response prediction holds great…”
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Dr.VAE: Drug Response Variational Autoencoder by Rampasek, Ladislav, Hidru, Daniel, Smirnov, Petr, Haibe-Kains, Benjamin, Goldenberg, Anna

“…We present two deep generative models based on Variational Autoencoders to improve the accuracy of drug response prediction. Our models, Perturbation…”
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Hierarchical graph neural nets can capture long-range interactions by Rampášek, Ladislav, Wolf, Guy

“…Graph neural networks (GNNs) based on message passing between neighboring nodes are known to be insufficient for capturing long-range interactions in graphs…”
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Attending to Graph Transformers by Müller, Luis, Galkin, Mikhail, Morris, Christopher, Rampášek, Ladislav

“…Recently, transformer architectures for graphs emerged as an alternative to established techniques for machine learning with graphs, such as (message-passing)…”
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Graph Positional and Structural Encoder by Cantürk, Semih, Liu, Renming, Lapointe-Gagné, Olivier, Létourneau, Vincent, Wolf, Guy, Beaini, Dominique, Rampášek, Ladislav

“…Positional and structural encodings (PSE) enable better identifiability of nodes within a graph, rendering them essential tools for empowering modern GNNs, and…”
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Discovery of RNA motifs using a computational pipeline that allows insertions in paired regions and filtering of candidate sequences by Jimenez, Randi M, Rampášek, Ladislav, Brejová, Broňa, Vinař, Tomáš, Lupták, Andrej

“…The enormous impact of noncoding RNAs on biology and biotechnology has motivated the development of systematic approaches to their discovery and…”
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Long Range Graph Benchmark by Dwivedi, Vijay Prakash, Rampášek, Ladislav, Galkin, Mikhail, Parviz, Ali, Wolf, Guy, Luu, Anh Tuan, Beaini, Dominique

“…Graph Neural Networks (GNNs) that are based on the message passing (MP) paradigm generally exchange information between 1-hop neighbors to build node…”
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