Search Results for author: Daisuke Kihara

Found 12 papers, 7 papers with code

ACC-UNet: A Completely Convolutional UNet model for the 2020s

1 code implementation25 Aug 2023 Nabil Ibtehaz, Daisuke Kihara

This decade is marked by the introduction of Vision Transformer, a radical paradigm shift in broad computer vision.

Image Segmentation Medical Image Segmentation +1

On the Importance of Asymmetry for Siamese Representation Learning

1 code implementation CVPR 2022 Xiao Wang, Haoqi Fan, Yuandong Tian, Daisuke Kihara, Xinlei Chen

Many recent self-supervised frameworks for visual representation learning are based on certain forms of Siamese networks.

Representation Learning

Using Steered Molecular Dynamic Tension for Assessing Quality of Computational Protein Structure Models

no code implementations22 Oct 2021 Lyman Monroe, Daisuke Kihara

We used in total 4, 929 structure models for 24 target proteins from the Critical Assessment of Techniques of Structure Prediction (CASP) and investigated if the magnitude of the break force, i. e., the force required to rearrange the model structure, from the force profile was sufficient information for selecting near-native models.

Application of Sequence Embedding in Protein Sequence-Based Predictions

no code implementations14 Oct 2021 Nabil Ibtehaz, Daisuke Kihara

In sequence-based predictions, conventionally an input sequence is represented by a multiple sequence alignment (MSA) or a representation derived from MSA, such as a position-specific scoring matrix.

Multiple Sequence Alignment Position

An expanded evaluation of protein function prediction methods shows an improvement in accuracy

1 code implementation3 Jan 2016 Yuxiang Jiang, Tal Ronnen Oron, Wyatt T Clark, Asma R Bankapur, Daniel D'Andrea, Rosalba Lepore, Christopher S Funk, Indika Kahanda, Karin M Verspoor, Asa Ben-Hur, Emily Koo, Duncan Penfold-Brown, Dennis Shasha, Noah Youngs, Richard Bonneau, Alexandra Lin, Sayed ME Sahraeian, Pier Luigi Martelli, Giuseppe Profiti, Rita Casadio, Renzhi Cao, Zhaolong Zhong, Jianlin Cheng, Adrian Altenhoff, Nives Skunca, Christophe Dessimoz, Tunca Dogan, Kai Hakala, Suwisa Kaewphan, Farrokh Mehryary, Tapio Salakoski, Filip Ginter, Hai Fang, Ben Smithers, Matt Oates, Julian Gough, Petri Törönen, Patrik Koskinen, Liisa Holm, Ching-Tai Chen, Wen-Lian Hsu, Kevin Bryson, Domenico Cozzetto, Federico Minneci, David T Jones, Samuel Chapman, Dukka B K. C., Ishita K Khan, Daisuke Kihara, Dan Ofer, Nadav Rappoport, Amos Stern, Elena Cibrian-Uhalte, Paul Denny, Rebecca E Foulger, Reija Hieta, Duncan Legge, Ruth C Lovering, Michele Magrane, Anna N Melidoni, Prudence Mutowo-Meullenet, Klemens Pichler, Aleksandra Shypitsyna, Biao Li, Pooya Zakeri, Sarah ElShal, Léon-Charles Tranchevent, Sayoni Das, Natalie L Dawson, David Lee, Jonathan G Lees, Ian Sillitoe, Prajwal Bhat, Tamás Nepusz, Alfonso E Romero, Rajkumar Sasidharan, Haixuan Yang, Alberto Paccanaro, Jesse Gillis, Adriana E Sedeño-Cortés, Paul Pavlidis, Shou Feng, Juan M Cejuela, Tatyana Goldberg, Tobias Hamp, Lothar Richter, Asaf Salamov, Toni Gabaldon, Marina Marcet-Houben, Fran Supek, Qingtian Gong, Wei Ning, Yuanpeng Zhou, Weidong Tian, Marco Falda, Paolo Fontana, Enrico Lavezzo, Stefano Toppo, Carlo Ferrari, Manuel Giollo, Damiano Piovesan, Silvio Tosatto, Angela del Pozo, José M Fernández, Paolo Maietta, Alfonso Valencia, Michael L Tress, Alfredo Benso, Stefano Di Carlo, Gianfranco Politano, Alessandro Savino, Hafeez Ur Rehman, Matteo Re, Marco Mesiti, Giorgio Valentini, Joachim W Bargsten, Aalt DJ van Dijk, Branislava Gemovic, Sanja Glisic, Vladmir Perovic, Veljko Veljkovic, Nevena Veljkovic, Danillo C Almeida-e-Silva, Ricardo ZN Vencio, Malvika Sharan, Jörg Vogel, Lakesh Kansakar, Shanshan Zhang, Slobodan Vucetic, Zheng Wang, Michael JE Sternberg, Mark N Wass, Rachael P Huntley, Maria J Martin, Claire O'Donovan, Peter N. Robinson, Yves Moreau, Anna Tramontano, Patricia C Babbitt, Steven E Brenner, Michal Linial, Christine A Orengo, Burkhard Rost, Casey S Greene, Sean D Mooney, Iddo Friedberg, Predrag Radivojac

To review progress in the field, the analysis also compared the best methods participating in CAFA1 to those of CAFA2.

Quantitative Methods

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