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Full-Text Articles in Life Sciences
Quantifying Resting-State Functional Connectivity In Critically Brain-Injured Patients: A Graph-Theoretical Approach With Fnirs, Ira Gupta
Electronic Thesis and Dissertation Repository
Assessment of consciousness in behaviourally unresponsive patients with critical brain injuries continues to be a challenge. There remains a need for robust tools that can accurately characterize preserved cortical function and predict patient outcomes. In the present study, functional near-infrared spectroscopy is employed in conjunction with graph theory and machine learning to quantify resting-state functional connectivity in 16 acutely brain-injured patients and 23 healthy controls. Results revealed significant channel-level differences between the groups for three graph metrics, including degree, clustering coefficient, and local efficiency. Further investigation using machine learning algorithms revealed that these metrics can be used to distinguish between …
Using Machine Learning To Identify Neural Mechanisms Underlying The Development Of Cognition In Children And Adolescents With Adhd, Brian Pho
Electronic Thesis and Dissertation Repository
Childhood and adolescence are marked by improvements to cognition and by the emergence of neurodevelopmental disorders such as attention deficit hyperactivity disorder (ADHD). What neural mechanisms are associated with cognitive development in ADHD? In this study, I applied machine learning models to functional connectivity profiles to identify patterns of network connectivity that predict various cognitive abilities in a group of participants ages 6 to 16 with ADHD. The models successfully predicted IQ, visual spatial, verbal comprehension, and fluid reasoning in children ages 6 to 11, but not adolescents. Furthermore, the models identified connections with the default mode, memory retrieval, and …
Mhcherrypan, A Novel Model To Predict The Binding Affinity Of Pan-Specific Class I Hla-Peptide, Xuezhi Xie
Mhcherrypan, A Novel Model To Predict The Binding Affinity Of Pan-Specific Class I Hla-Peptide, Xuezhi Xie
Electronic Thesis and Dissertation Repository
The human leukocyte antigen (HLA) system or complex plays an essential role in regulating the immune system in humans. Accurate prediction of peptide binding with HLA can efficiently help to identify those neoantigens, which potentially make a big difference in immune drug development. HLA is one of the most polymorphic genetic systems in humans, and thousands of HLA allelic versions exist. Due to the high polymorphism of HLA complex, it is still pretty difficult to accurately predict the binding affinity. In this thesis, we presented a new algorithm to combine convolutional neural network and long short-term memory to solve this …
Dna Sequence Classification: It’S Easier Than You Think: An Open-Source K-Mer Based Machine Learning Tool For Fast And Accurate Classification Of A Variety Of Genomic Datasets, Stephen Solis-Reyes
Dna Sequence Classification: It’S Easier Than You Think: An Open-Source K-Mer Based Machine Learning Tool For Fast And Accurate Classification Of A Variety Of Genomic Datasets, Stephen Solis-Reyes
Electronic Thesis and Dissertation Repository
Supervised classification of genomic sequences is a challenging, well-studied problem with a variety of important applications. We propose an open-source, supervised, alignment-free, highly general method for sequence classification that operates on k-mer proportions of DNA sequences. This method was implemented in a fully standalone general-purpose software package called Kameris, publicly available under a permissive open-source license. Compared to competing software, ours provides key advantages in terms of data security and privacy, transparency, and reproducibility. We perform a detailed study of its accuracy and performance on a wide variety of classification tasks, including virus subtyping, taxonomic classification, and human haplogroup assignment. …
Classifying Music Perception And Imagination Using Eeg, Avital Sternin
Classifying Music Perception And Imagination Using Eeg, Avital Sternin
Electronic Thesis and Dissertation Repository
This study explored whether we could accurately classify perceived and imagined musical stimuli from EEG data. Successful EEG-based classification of what an individual is imagining could pave the way for novel communication techniques, such as brain-computer interfaces. We recorded EEG with a 64-channel BioSemi system while participants heard or imagined different musical stimuli. Using principal components analysis, we identified components common to both the perception and imagination conditions however, the time courses of the components did not allow for stimuli classification. We then applied deep learning techniques using a convolutional neural network. This technique enabled us to classify perception of …