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For: Tekin Erguzel T, Tas C, Cebi M. A wrapper-based approach for feature selection and classification of major depressive disorder–bipolar disorders. Computers in Biology and Medicine 2015;64:127-37. [DOI: 10.1016/j.compbiomed.2015.06.021] [Cited by in Crossref: 30] [Cited by in F6Publishing: 18] [Article Influence: 4.3] [Reference Citation Analysis]
Number Citing Articles
1 Piri J, Mohapatra P. An analytical study of modified multi-objective Harris Hawk Optimizer towards medical data feature selection. Comput Biol Med 2021;135:104558. [PMID: 34182329 DOI: 10.1016/j.compbiomed.2021.104558] [Reference Citation Analysis]
2 Valenzuela O, Jiang X, Carrillo A, Rojas I. Multi-Objective Genetic Algorithms to Find Most Relevant Volumes of the Brain Related to Alzheimer's Disease and Mild Cognitive Impairment. Int J Neur Syst 2018;28:1850022. [DOI: 10.1142/s0129065718500223] [Cited by in Crossref: 17] [Cited by in F6Publishing: 2] [Article Influence: 4.3] [Reference Citation Analysis]
3 Liu Y, Guo B, Zou X, Li Y, Shi S. Machine learning assisted materials design and discovery for rechargeable batteries. Energy Storage Materials 2020;31:434-50. [DOI: 10.1016/j.ensm.2020.06.033] [Cited by in Crossref: 45] [Cited by in F6Publishing: 10] [Article Influence: 22.5] [Reference Citation Analysis]
4 Jamal S, Ali W, Nagpal P, Grover S, Grover A. Computational models for the prediction of adverse cardiovascular drug reactions. J Transl Med 2019;17:171. [PMID: 31118067 DOI: 10.1186/s12967-019-1918-z] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 1.7] [Reference Citation Analysis]
5 Saif Alghawli A, Taloba AI, Liu H. An Enhanced Ant Colony Optimization Mechanism for the Classification of Depressive Disorders. Computational Intelligence and Neuroscience 2022;2022:1-12. [DOI: 10.1155/2022/1332664] [Reference Citation Analysis]
6 Liu C, Wang W, Zhao Q, Shen X, Konan M. A new feature selection method based on a validity index of feature subset. Pattern Recognition Letters 2017;92:1-8. [DOI: 10.1016/j.patrec.2017.03.018] [Cited by in Crossref: 36] [Cited by in F6Publishing: 11] [Article Influence: 7.2] [Reference Citation Analysis]
7 Greco C, Matarazzo O, Cordasco G, Vinciarelli A, Callejas Z, Esposito A. Discriminative Power of EEG-Based Biomarkers in Major Depressive Disorder: A Systematic Review. IEEE Access 2021;9:112850-70. [DOI: 10.1109/access.2021.3103047] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 4.0] [Reference Citation Analysis]
8 Bhadra S, Kumar CJ. An insight into diagnosis of depression using machine learning techniques: a systematic review. Curr Med Res Opin 2022;:1-62. [PMID: 35129401 DOI: 10.1080/03007995.2022.2038487] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
9 Feng X, Hao X, Shi R, Xia Z, Huang L, Yu Q, Zhou F. Detection and Comparative Analysis of Methylomic Biomarkers of Rheumatoid Arthritis. Front Genet 2020;11:238. [PMID: 32292416 DOI: 10.3389/fgene.2020.00238] [Cited by in Crossref: 2] [Article Influence: 1.0] [Reference Citation Analysis]
10 Hui KH, Ooi CS, Lim MH, Leong MS, Al-Obaidi SM. An improved wrapper-based feature selection method for machinery fault diagnosis. PLoS One 2017;12:e0189143. [PMID: 29261689 DOI: 10.1371/journal.pone.0189143] [Cited by in Crossref: 21] [Cited by in F6Publishing: 4] [Article Influence: 4.2] [Reference Citation Analysis]
11 Uyulan C, de la Salle S, Erguzel TT, Lynn E, Blier P, Knott V, Adamson MM, Zelka M, Tarhan N. Depression Diagnosis Modeling With Advanced Computational Methods: Frequency-Domain eMVAR and Deep Learning. Clin EEG Neurosci 2021;:15500594211018545. [PMID: 34080925 DOI: 10.1177/15500594211018545] [Reference Citation Analysis]
12 Singh J, Hamid MA. Cognitive Computing in Mental Healthcare: a Review of Methods and Technologies for Detection of Mental Disorders. Cogn Comput. [DOI: 10.1007/s12559-022-10042-2] [Reference Citation Analysis]
13 Colombo F, Calesella F, Mazza MG, Melloni EMT, Morelli MJ, Scotti GM, Benedetti F, Bollettini I, Vai B. Machine learning approaches for prediction of bipolar disorder based on biological, clinical and neuropsychological markers: a systematic review and meta-analysis. Neurosci Biobehav Rev 2022;:104552. [PMID: 35120970 DOI: 10.1016/j.neubiorev.2022.104552] [Reference Citation Analysis]
14 Luján MÁ, Torres AM, Borja AL, Santos JL, Sotos JM. High-Precise Bipolar Disorder Detection by Using Radial Basis Functions Based Neural Network. Electronics 2022;11:343. [DOI: 10.3390/electronics11030343] [Reference Citation Analysis]
15 Jaddi NS, Saniee Abadeh M. DNA methylation-based age prediction using cell separation algorithm. Comput Biol Med 2020;121:103747. [PMID: 32339093 DOI: 10.1016/j.compbiomed.2020.103747] [Cited by in Crossref: 3] [Article Influence: 1.5] [Reference Citation Analysis]
16 Elizabeth Jesi V, Aslam SM. An intelligent disease prediction and monitoring system using feature selection, multi-neural network and fuzzy rules. Neural Comput & Applic. [DOI: 10.1007/s00521-022-07527-4] [Reference Citation Analysis]
17 Ferreri F, Bourla A, Mouchabac S, Karila L. e-Addictology: An Overview of New Technologies for Assessing and Intervening in Addictive Behaviors. Front Psychiatry 2018;9:51. [PMID: 29545756 DOI: 10.3389/fpsyt.2018.00051] [Cited by in Crossref: 41] [Cited by in F6Publishing: 27] [Article Influence: 10.3] [Reference Citation Analysis]
18 Librenza-Garcia D, Kotzian BJ, Yang J, Mwangi B, Cao B, Pereira Lima LN, Bermudez MB, Boeira MV, Kapczinski F, Passos IC. The impact of machine learning techniques in the study of bipolar disorder: A systematic review. Neurosci Biobehav Rev 2017;80:538-54. [PMID: 28728937 DOI: 10.1016/j.neubiorev.2017.07.004] [Cited by in Crossref: 76] [Cited by in F6Publishing: 49] [Article Influence: 15.2] [Reference Citation Analysis]
19 Wang X, Yan Y, Ma X. Feature Selection Method Based on Differential Correlation Information Entropy. Neural Process Lett 2020;52:1339-58. [DOI: 10.1007/s11063-020-10307-7] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
20 Li M, Das T, Deng W, Wang Q, Li Y, Zhao L, Ma X, Wang Y, Yu H, Li X, Meng Y, Palaniyappan L, Li T. Clinical utility of a short resting-state MRI scan in differentiating bipolar from unipolar depression. Acta Psychiatr Scand 2017;136:288-99. [PMID: 28504840 DOI: 10.1111/acps.12752] [Cited by in Crossref: 41] [Cited by in F6Publishing: 36] [Article Influence: 8.2] [Reference Citation Analysis]
21 Ferreri F, Bourla A, Peretti CS, Segawa T, Jaafari N, Mouchabac S. How New Technologies Can Improve Prediction, Assessment, and Intervention in Obsessive-Compulsive Disorder (e-OCD): Review. JMIR Ment Health 2019;6:e11643. [PMID: 31821153 DOI: 10.2196/11643] [Cited by in Crossref: 6] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
22 Mishra G, Ananth V, Shelke K, Sehgal D, Deepak J. Classification of anti hepatitis peptides using Support Vector Machine with hybrid Ant Colony OptimizationThe Luxembourg database of trichothecene type B F. graminearum and F. culmorum producers. Bioinformation 2016;12:12-4. [PMID: 27212838 DOI: 10.6026/97320630012012] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 0.5] [Reference Citation Analysis]
23 Passos IC, Ballester PL, Barros RC, Librenza-Garcia D, Mwangi B, Birmaher B, Brietzke E, Hajek T, Lopez Jaramillo C, Mansur RB, Alda M, Haarman BCM, Isometsa E, Lam RW, McIntyre RS, Minuzzi L, Kessing LV, Yatham LN, Duffy A, Kapczinski F. Machine learning and big data analytics in bipolar disorder: A position paper from the International Society for Bipolar Disorders Big Data Task Force. Bipolar Disord 2019;21:582-94. [PMID: 31465619 DOI: 10.1111/bdi.12828] [Cited by in Crossref: 27] [Cited by in F6Publishing: 20] [Article Influence: 9.0] [Reference Citation Analysis]
24 Hossain MN, Uddin MH, Thapa K, Al Zubaer MA, Islam MS, Lee J, Park J, Yang SH. Detecting Cognitive Impairment Status Using Keystroke Patterns and Physical Activity Data among the Older Adults: A Machine Learning Approach. J Healthc Eng 2021;2021:1302989. [PMID: 34966518 DOI: 10.1155/2021/1302989] [Reference Citation Analysis]
25 Wan Z, Zhang H, Huang J, Zhou H, Yang J, Zhong N. Single-Channel EEG-Based Machine Learning Method for Prescreening Major Depressive Disorder. Int J Info Tech Dec Mak 2019;18:1579-603. [DOI: 10.1142/s0219622019500342] [Cited by in Crossref: 4] [Article Influence: 1.3] [Reference Citation Analysis]
26 Liu Y, Wu J, Avdeev M, Shi S. Multi‐Layer Feature Selection Incorporating Weighted Score‐Based Expert Knowledge toward Modeling Materials with Targeted Properties. Adv Theory Simul 2020;3:1900215. [DOI: 10.1002/adts.201900215] [Cited by in Crossref: 39] [Cited by in F6Publishing: 28] [Article Influence: 19.5] [Reference Citation Analysis]
27 Jaddi NS, Saniee Abadeh M. Cell separation algorithm with enhanced search behaviour in miRNA feature selection for cancer diagnosis. Information Systems 2022;104:101906. [DOI: 10.1016/j.is.2021.101906] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
28 Alejo R, Monroy-de-jesús J, Ambriz-polo JC, Pacheco-sánchez JH. An improved dynamic sampling back-propagation algorithm based on mean square error to face the multi-class imbalance problem. Neural Comput & Applic 2017;28:2843-57. [DOI: 10.1007/s00521-017-2938-3] [Cited by in Crossref: 6] [Cited by in F6Publishing: 2] [Article Influence: 1.2] [Reference Citation Analysis]
29 Uyulan C, Ergüzel TT, Unubol H, Cebi M, Sayar GH, Nezhad Asad M, Tarhan N. Major Depressive Disorder Classification Based on Different Convolutional Neural Network Models: Deep Learning Approach. Clin EEG Neurosci 2021;52:38-51. [PMID: 32491928 DOI: 10.1177/1550059420916634] [Cited by in Crossref: 7] [Cited by in F6Publishing: 5] [Article Influence: 3.5] [Reference Citation Analysis]
30 Peng D, Yao Z. Neuroimaging Advance in Depressive Disorder. In: Fang Y, editor. Depressive Disorders: Mechanisms, Measurement and Management. Singapore: Springer; 2019. pp. 59-83. [DOI: 10.1007/978-981-32-9271-0_3] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
31 Trambaiolli LR, Biazoli CE. Resting-state global EEG connectivity predicts depression and anxiety severity. Annu Int Conf IEEE Eng Med Biol Soc 2020;2020:3707-10. [PMID: 33018806 DOI: 10.1109/EMBC44109.2020.9176161] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
32 Jaddi NS, Saniee Abadeh M. Gene selection of non-small cell lung cancer data for adjuvant chemotherapy decision using cell separation algorithm. Appl Intell 2020;50:3822-36. [DOI: 10.1007/s10489-020-01740-1] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
33 Masud MT, Mamun MA, Thapa K, Lee DH, Griffiths MD, Yang SH. Unobtrusive monitoring of behavior and movement patterns to detect clinical depression severity level via smartphone. J Biomed Inform 2020;103:103371. [PMID: 31935462 DOI: 10.1016/j.jbi.2019.103371] [Cited by in Crossref: 8] [Cited by in F6Publishing: 5] [Article Influence: 4.0] [Reference Citation Analysis]
34 Ibrahim AM, Tawhid M, Ward RK. A binary water wave optimization for feature selection. International Journal of Approximate Reasoning 2020;120:74-91. [DOI: 10.1016/j.ijar.2020.01.012] [Cited by in Crossref: 16] [Cited by in F6Publishing: 3] [Article Influence: 8.0] [Reference Citation Analysis]