Improving rainfall forecast at the district scale over the eastern Indian region using deep neural network

Trivedi, Dhananjay; Sharma, Omveer; Pattnaik, Sandeep; Hazra, Vivekananda; Puhan, Niladri Bihari

doi:10.1007/s00704-023-04734-4

Cited by 2 publications

(1 citation statement)

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“…In our previous research, we discovered that SVM and RF were the most effective machine-learning algorithms in distinguishing between lithium responders and non-responders among BD patients [37,[67][68][69]. Inspired by the human brain, neural Networks (NN) excel in pattern recognition and learning complex nonlinear relationships, making them powerful yet resource-heavy and challenging to manage due to their propensity for over-fitting [70]. Each algorithm offers unique strengths and limitations, with the choice often depending on data characteristics and specific problem needs.…”

Section: Machine Learning Predictor Analysismentioning

confidence: 99%

Predicting Suicide Risk in Bipolar Disorder patients from Lymphoblastoid Cell Lines genetic signatures

Sharma,

Nayak,

Mizrahi

et al. 2024

Preprint

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This research investigates the genetic signatures associated with a high risk of suicide in Bipolar disorder (BD) patients through RNA sequencing analysis of lymphoblastoid cell lines (LCLs). By identifying differentially expressed genes (DEGs) and their enrichment in pathways and disease associations, we uncover insights into the molecular mechanisms underlying suicidal behavior. LCL gene expression analysis reveals significant enrichment in pathways related to primary immunodeficiency, ion channel, and cardiovascular defects. Notably, genes such asLCK,KCNN2, andGRIA1emerged as pivotal in these pathways, suggesting their potential roles as biomarkers. Machine learning models trained on a subset of the patients and then tested on other patients demonstrate high accuracy in distinguishing low and high-risk of suicide in BD patients. Moreover, the study explores the genetic overlap between suicide-related genes and several psychiatric disorders. This comprehensive approach enhances our understanding of the complex interplay between genetics and suicidal behavior, laying the groundwork for future prevention strategies.

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Section: Machine Learning Predictor Analysismentioning

confidence: 99%