Performance of various deep learning models from PyNoetic’s classification module on the Motor Imagery decoding task. by Gursimran Singh (575288)

Subjects:

Illustration of recording paradigm with PyNoetic’s Stimuli generation and recording module. by Gursimran Singh (575288)

Subjects:

A typical BCI system depicting the flow and processing of EEG data. by Gursimran Singh (575288)

Subjects:

Contrast enhancement of digital images using dragonfly algorithm by Soumyajit Saha (19726163)

“…Comparisons with state-of-art methods ensure the superiority of the proposed algorithm. The Python implementation of the proposed approach is available in this <a href="https://github.com/somnath796/DA_contrast_enhancement" target="_blank">Github repository</a>.…”

Example of a typical workflow to construct a network using NeKo. by Marco Ruscone (11747264)

Comparative analysis of WordNet replacement impact on data distribution. by Enock Niyonkuru (21246879)

LBQANA python code + Merged Gene Expression Dataset from GSE10810, GSE17907, GSE20711, GSE42568, GSE45827, and GSE61304 for Breast Cancer Biomarker Discovery by M RN (9866504)

Dataset of networks used in assessing the Troika algorithm for clique partitioning and community detection by Samin Aref (4683934)

“…Each network is provided in .gml format or .pkl format which can be read into a networkX graph object using standard functions from the networkX library in Python. For accessing other networks used in the study, please refer to the article for references to the primary sources of those network data.…”

Image 1_Identification of signature genes and subtypes for heart failure diagnosis based on machine learning.tif by Yanlong Zhang (3159840)

“…In addition, we assessed the different subtypes of heart failure through unsupervised clustering, and investigations were conducted on the differences in the immunological microenvironment, improved functions, and pathways among these subtypes. …”

Data Sheet 1_Identification of signature genes and subtypes for heart failure diagnosis based on machine learning.xlsx by Yanlong Zhang (3159840)

“…In addition, we assessed the different subtypes of heart failure through unsupervised clustering, and investigations were conducted on the differences in the immunological microenvironment, improved functions, and pathways among these subtypes. …”

Table 4_DEPDC1B, CDCA2, APOBEC3B, and TYMS are potential hub genes and therapeutic targets for diagnosing dialysis patients with heart failure.xlsx by Wenwu Tang (5493086)

“…Introduction<p>Heart failure (HF) has a very high prevalence in patients with maintenance hemodialysis (MHD). …”

Table 1_DEPDC1B, CDCA2, APOBEC3B, and TYMS are potential hub genes and therapeutic targets for diagnosing dialysis patients with heart failure.xlsx by Wenwu Tang (5493086)

“…Introduction<p>Heart failure (HF) has a very high prevalence in patients with maintenance hemodialysis (MHD). …”

Image 1_DEPDC1B, CDCA2, APOBEC3B, and TYMS are potential hub genes and therapeutic targets for diagnosing dialysis patients with heart failure.pdf by Wenwu Tang (5493086)

“…Introduction<p>Heart failure (HF) has a very high prevalence in patients with maintenance hemodialysis (MHD). …”

Table 2_DEPDC1B, CDCA2, APOBEC3B, and TYMS are potential hub genes and therapeutic targets for diagnosing dialysis patients with heart failure.xlsx by Wenwu Tang (5493086)

“…Introduction<p>Heart failure (HF) has a very high prevalence in patients with maintenance hemodialysis (MHD). …”

Table 3_DEPDC1B, CDCA2, APOBEC3B, and TYMS are potential hub genes and therapeutic targets for diagnosing dialysis patients with heart failure.xlsx by Wenwu Tang (5493086)

“…Introduction<p>Heart failure (HF) has a very high prevalence in patients with maintenance hemodialysis (MHD). …”

Table 5_DEPDC1B, CDCA2, APOBEC3B, and TYMS are potential hub genes and therapeutic targets for diagnosing dialysis patients with heart failure.xlsx by Wenwu Tang (5493086)

“…Introduction<p>Heart failure (HF) has a very high prevalence in patients with maintenance hemodialysis (MHD). …”

Supplementary file 1_RadioMe: an automated home-based radio, music playlist, and diary reminder system: Report on recruitment, music compilation, and listening, and preliminary tes... by Alexander Street (16905678)

“…</p>Aims<p>RadioMe was a project designed for people living at home with dementia to build a system to help them maintain the highest quality of life there for as long as possible, with three functional components: 1. Streaming radio; 2. Providing pre-recorded spoken diary reminders; 3. interrupting the radio with pre-compiled playlists when a wrist-worn heart rate (HR) monitor detects stress. …”

Table 1_Identification and validation of key biomarkers of the glycolysis-ketone body metabolism in heart failure based on multi-omics and machine learning.xlsx by Na Xiao (327834)

“…Candidate genes were refined using machine learning algorithms (LASSO regression and Boruta), with functional enrichment assessed via Gene Set Enrichment Analysis (GSEA). …”

Image 1_Identification and validation of key biomarkers of the glycolysis-ketone body metabolism in heart failure based on multi-omics and machine learning.pdf by Na Xiao (327834)

“…Candidate genes were refined using machine learning algorithms (LASSO regression and Boruta), with functional enrichment assessed via Gene Set Enrichment Analysis (GSEA). …”

Image2_Identification of novel biomarkers, shared molecular signatures and immune cell infiltration in heart and kidney failure by transcriptomics.tif by Qingqing Long (845156)

“…Introduction<p>Heart failure (HF) and kidney failure (KF) are closely related conditions that often coexist, posing a complex clinical challenge. …”