Results 201 to 210 of about 6,871,211 (381)

Feature selection using tabu search method [PDF]

, 2002
Hongbin Zhang, Guangyu Sun
openalex   +1 more source

Feature selection in machine learning: A new perspective

Neurocomputing, 2018
Jie Cai, Jiawei Luo, Shulin Wang, Sheng Yang   +3 more
semanticscholar   +1 more source

Time, the final frontier

Molecular Oncology, EarlyView.
This article advocates integrating temporal dynamics into cancer research. Rather than relying on static snapshots, researchers should increasingly consider adopting dynamic methods—such as live imaging, temporal omics, and liquid biopsies—to track how tumors evolve over time.
Gautier Follain, Michal Dibus, Omkar Joshi, Guillaume Jacquemet   +3 more
wiley   +1 more source

Causality, Machine Learning, and Feature Selection: A Survey. [PDF]

Sensors (Basel)
Lamsaf A, Carrilho R, Neves JC, Proença H.   +3 more
europepmc   +1 more source

On Feature Selection with Measurement Cost and Grouped Features [PDF]

, 2002
Pavel Paclı́k, Robert P. W. Duin, G.M.P. van Kempen, Reinhard Kohlus   +3 more
openalex   +1 more source

MET and NF2 alterations confer primary and early resistance to first‐line alectinib treatment in ALK‐positive non‐small‐cell lung cancer

Molecular Oncology, EarlyView.
Alectinib resistance in ALK+ NSCLC depends on treatment sequence and EML4‐ALK variants. Variant 1 exhibited off‐target resistance after first‐line treatment, while variant 3 and later lines favored on‐target mutations. Early resistance involved off‐target alterations, like MET and NF2, while on‐target mutations emerged with prolonged therapy.
Jie Hu, Ning Ding, Xiaobo Xu, Yedan Chen, Yong Zhang, Jingwen Liu, Jiebai Zhou, Hairong Bao, Donghui Zhang, Yijun Song, Yang Shao, Yuanlin Song   +11 more
wiley   +1 more source

Multivariate filter methods for feature selection with the γ -metric. [PDF]

BMC Med Res Methodol
Ngo N, Michel P, Giorgi R.
europepmc   +1 more source

Classifier-Independent Feature Selection Based on Non-parametric Discriminant Analysis [PDF]

, 2002
Naoto Abe, Mineichi Kudo, Masaru Shimbo
openalex   +1 more source

Feature Selection for Clustering [PDF]

, 2009
Manoranjan Dash, Poon Wei Koot
openaire   +1 more source

Chemoresistome mapping in individual breast cancer patients unravels diversity in dynamic transcriptional adaptation

Molecular Oncology, EarlyView.
This study used longitudinal transcriptomics and gene‐pattern classification to uncover patient‐specific mechanisms of chemotherapy resistance in breast cancer. Findings reveal preexisting drug‐tolerant states in primary tumors and diverse gene rewiring patterns across patients, converging on a few dysregulated functional modules. Despite receiving the
Maya Dadiani, Gilgi Friedlander, Gili Perry, Nora Balint‐Lahat, Shlomit Gilad, Dana Morzaev‐Sulzbach, Anjana Shenoy, Noa Bossel Ben‐Moshe, Anya Pavlovsky, Rinat Bernstein‐Molho, Eytan Domany, Iris Barshack, Tamar Geiger, Bella Kaufman, Einav Nili Gal‐Yam   +14 more
wiley   +1 more source