Results 11 to 20 of about 40 (39)

A Machine Learning-Based Model to Predict Survival After Transarterial Chemoembolization for BCLC Stage B Hepatocellular Carcinoma. [PDF]

Front Oncol, 2021
Lin H, Zeng L, Yang J, Hu W, Zhu Y.
europepmc   +1 more source

Antibody Responses Against Plasmodium vivax TRAP Recombinant and Synthetic Antigens in Naturally Exposed Individuals From the Brazilian Amazon. [PDF]

Front Immunol, 2019
Matos ADS, Rodrigues-da-Silva RN, Soares IF, Baptista BO, de Souza RM, Bitencourt-Chaves L, Totino PRR, Sánchez-Arcila JC, Daniel-Ribeiro CT, López-Camacho C, Reyes-Sandoval A, Pratt-Riccio LR, Lima-Junior JDC.   +12 more
europepmc   +1 more source

TEMPI: probabilistic modeling time-evolving differential PPI networks with multiPle information. [PDF]

Bioinformatics, 2014
Kim Y, Jang JH, Choi S, Hwang D.
europepmc   +1 more source

RactIP: fast and accurate prediction of RNA-RNA interaction using integer programming. [PDF]

Bioinformatics, 2010
Kato Y, Sato K, Hamada M, Watanabe Y, Asai K, Akutsu T.   +5 more
europepmc   +1 more source

Applications of machine learning in time-domain fluorescence lifetime imaging: a review. [PDF]

Methods Appl Fluoresc
Gouzou D, Taimori A, Haloubi T, Finlayson N, Wang Q, Hopgood JR, Vallejo M.   +6 more
europepmc   +1 more source

Quality of Life After Cardiac Surgery: The QUACS Study and Benefit Prediction Model. [PDF]

Eur J Cardiothorac Surg
Nashef SAM, Willard J, Mills C, Chiu YD, Noyes P, Petch MC, Couturier DL, Large SR.   +7 more
europepmc   +1 more source

Distinct immunometabolic signatures in circulating immune cells define disease outcome in acute-on-chronic liver failure. [PDF]

Hepatology
Feio-Azevedo R, Boesch M, Radenkovic S, van Melkebeke L, Smets L, Wallays M, Boeckx B, Philips G, Prata de Oliveira J, Ghorbani M, Laleman W, Meersseman P, Wilmer A, Cassiman D, van Malenstein H, Triantafyllou E, Sánchez C, Aguilar F, Nevens F, Verbeek J, Moreau R, Arroyo V, Denadai Souza A, Clària J, Lambrechts D, Ghesquière B, Korf H, van der Merwe S.   +27 more
europepmc   +1 more source

The Golden Age of Thermally Activated Delayed Fluorescence Materials: Design and Exploitation. [PDF]

Chem Rev
Dos Santos JM, Hall D, Basumatary B, Bryden M, Chen D, Choudhary P, Comerford T, Crovini E, Danos A, De J, Diesing S, Fatahi M, Griffin M, Gupta AK, Hafeez H, Hämmerling L, Hanover E, Haug J, Heil T, Karthik D, Kumar S, Lee O, Li H, Lucas F, Mackenzie CFR, Mariko A, Matulaitis T, Millward F, Olivier Y, Qi Q, Samuel IDW, Sharma N, Si C, Spierling L, Sudhakar P, Sun D, Tankelevičiu Tė E, Duarte Tonet M, Wang J, Wang T, Wu S, Xu Y, Zhang L, Zysman-Colman E.   +43 more
europepmc   +1 more source

Machine learning for microbiologists

Nature Reviews Microbiology, 2023
Francesco Asnicar, Andrew Maltez Thomas, Andrea Passerini   +2 more
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