Adversarial Vulnerability of Deep Learning Models in Analyzing Next Generation Sequencing Data. - Amiel Meiseles; Ishai Rosenberg; Yair Motro; Lior Rokach; Jacob Moran-Gilad (BIBM, 2020)

Source Model Selection for Deep Learning in the Time Series Domain. - Amiel Meiseles; Lior Rokach (IEEE Access, 2020)

Explainable machine learning for chronic lymphocytic leukemia treatment prediction using only inexpensive tests. - Amiel Meiseles; Denis Paley; Mira Ziv; Yarin Hadid; Lior Rokach; Tamar Tadmor (Comput. Biol. Medicine, 2022)

Latent SHAP: Toward Practical Human-Interpretable Explanations. - Ron Bitton; Alon Malach; Amiel Meiseles; Satoru Momiyama; Toshinori Araki; Jun Furukawa 001; Yuval Elovici; Asaf Shabtai (CoRR, 2022)

Vulnerability of pangolin SARS-CoV-2 lineage assignment to adversarial attack. - Amiel Meiseles; Yair Motro; Lior Rokach; Jacob Moran-Gilad (Artif. Intell. Medicine, 2023)

Iterative Feature eXclusion (IFX): Mitigating feature starvation in gradient boosted decision trees. - Amiel Meiseles; Lior Rokach (Knowl. Based Syst., 2024)

Toward practical human-interpretable explanations. - Alon Malach; Amiel Meiseles; Ron Bitton; Satoru Momiyama; Toshinori Araki; Jun Furukawa 001; Yuval Elovici; Asaf Shabtai (Mach. Learn., 2025)