Supplementary file 1_Probiotic modulation of maternal gut and milk microbiota and potential implications for infant microbial development in the perinatal period.docx by Kian Deng Tye (14200517)

“…Predictive genus clusters associated with probiotics demonstrated robust classification performance, suggesting functional shifts in microbial communities with potential implications for immune programming in early life.…”

Table 1_Genetic analysis of non-syndromic peg lateralis using whole-exome sequencing.xlsx by Junglim Choi (22045838)

“…In-silico mutation impact analysis was performed using Polymorphism Phenotyping v2, sorting intolerant from the tolerant, and integrated score of co-evolution and conservation algorithms.</p>Results<p>We identified a heterozygous allele for RP11-131H24.4 and OTOP1, which encodes the otopetrin-1 protein, a proton channel, in all 20 individuals. …”

Supplementary file 1_Genetic analysis of non-syndromic peg lateralis using whole-exome sequencing.docx by Junglim Choi (22045838)

“…In-silico mutation impact analysis was performed using Polymorphism Phenotyping v2, sorting intolerant from the tolerant, and integrated score of co-evolution and conservation algorithms.</p>Results<p>We identified a heterozygous allele for RP11-131H24.4 and OTOP1, which encodes the otopetrin-1 protein, a proton channel, in all 20 individuals. …”

Data Sheet 1_Deciphering novel mitochondrial signatures: multi-omics analysis uncovers cross-disease markers and oligodendrocyte pathways in Alzheimer’s disease and glioblastoma.do... by Xuan Xu (781842)

“…Key hub genes, such as HS6ST3 and TUBB2B, were identified across different cellular subpopulations, highlighting a cell-specific co-expression network linked to mitochondrial function.…”

Table 1_Deciphering novel mitochondrial signatures: multi-omics analysis uncovers cross-disease markers and oligodendrocyte pathways in Alzheimer’s disease and glioblastoma.xlsx by Xuan Xu (781842)

“…Key hub genes, such as HS6ST3 and TUBB2B, were identified across different cellular subpopulations, highlighting a cell-specific co-expression network linked to mitochondrial function.…”

Uncertainty and Novelty in Machine Learning by Derek Scott Prijatelj (20364288)

“…This demonstrates identifying information in finite steps to asymptotic statistics and PAC-learning, where we recover identification within finite observations at the cost of uncertainty and error.…”

MCCN Case Study 2 - Spatial projection via modelled data by Donald Hobern (21435904)

“…This study demonstrates: 1) Description of spatial assets using STAC, 2) Loading heterogeneous data sources into a cube, 3) Spatial projection in xarray using different algorithms offered by the <a href="https://pypi.org/project/PyKrige/" rel="nofollow" target="_blank">pykrige</a> and <a href="https://pypi.org/project/rioxarray/" rel="nofollow" target="_blank">rioxarray</a> packages.…”

Data Sheet 1_Machine learning models integrating intracranial artery calcification to predict outcomes of mechanical thrombectomy.pdf by Guangzong Li (16696443)

“…Eleven ML algorithms were trained and validated using Python, and external validation and performance evaluations were conducted. …”

Data Sheet 1_Unsupervised method for representation transfer from one brain to another.docx by Daiki Nakamura (20349885)

“…<p>Although the anatomical arrangement of brain regions and the functional structures within them are similar across individuals, the representation of neural information, such as recorded brain activity, varies among individuals owing to various factors. …”

Data Sheet 1_Multi-omics identification of a polyamine metabolism related signature for hepatocellular carcinoma and revealing tumor microenvironment characteristics.pdf by Yuexi Yu (21158744)

“…Immune cell infiltration was analyzed using the CIBERSORT algorithm. Finally, RT-qPCR experiments were conducted to validate the expression of key genes.…”

Table 1_Integrated transcriptomic and network analysis reveals candidate immune–metabolic biomarkers in children with the inattentive type of ADHD.xlsx by Qiaoyan Shao (22357861)

“…Candidate genes were identified by integrating differential expression analysis with weighted gene co-expression network analysis (WGCNA) modules. …”

Image 1_Integrated transcriptomic and network analysis reveals candidate immune–metabolic biomarkers in children with the inattentive type of ADHD.tif by Qiaoyan Shao (22357861)

“…Candidate genes were identified by integrating differential expression analysis with weighted gene co-expression network analysis (WGCNA) modules. …”

Table 1_Bridging aging, immunity, and atherosclerosis: novel insights into senescence-related genes.xlsx by Yan Lu (32611)

“…Subsequently, differential expression analysis, weighted gene co-expression network analysis, accompanied by 3 machine learning algorithms, LASSO, SVM and RF, were performed to identify diagnostic genes. …”

Table 2_Integrated transcriptomic and network analysis reveals candidate immune–metabolic biomarkers in children with the inattentive type of ADHD.xlsx by Qiaoyan Shao (22357861)

“…Candidate genes were identified by integrating differential expression analysis with weighted gene co-expression network analysis (WGCNA) modules. …”

Table 1_Multi-omics identification of a polyamine metabolism related signature for hepatocellular carcinoma and revealing tumor microenvironment characteristics.xlsx by Yuexi Yu (21158744)

“…Immune cell infiltration was analyzed using the CIBERSORT algorithm. Finally, RT-qPCR experiments were conducted to validate the expression of key genes.…”

Table 2_Multi-omics identification of a polyamine metabolism related signature for hepatocellular carcinoma and revealing tumor microenvironment characteristics.xlsx by Yuexi Yu (21158744)

“…Immune cell infiltration was analyzed using the CIBERSORT algorithm. Finally, RT-qPCR experiments were conducted to validate the expression of key genes.…”

DataSheet1_Identification and validation of interferon-stimulated gene 15 as a biomarker for dermatomyositis by integrated bioinformatics analysis and machine learning.zip by Xingwang Wang (2439220)

“…Advanced machine learning methods were utilized to further pinpoint hub genes. Weighted gene co‐expression network analysis (WGCNA) was also conducted to discover key gene modules. …”

Image 2_Single-cell sequencing reveals the role of aggrephagy-related patterns in tumor microenvironment, prognosis and immunotherapy in endometrial cancer.jpeg by Yuquan Yuan (10570747)

“…However, aggrephagy functions within the tumor microenvironment (TME) in endometrial cancer (EC) remain to be elucidated.…”

Image 1_Single-cell sequencing reveals the role of aggrephagy-related patterns in tumor microenvironment, prognosis and immunotherapy in endometrial cancer.jpeg by Yuquan Yuan (10570747)

“…However, aggrephagy functions within the tumor microenvironment (TME) in endometrial cancer (EC) remain to be elucidated.…”

Image 3_Single-cell sequencing reveals the role of aggrephagy-related patterns in tumor microenvironment, prognosis and immunotherapy in endometrial cancer.jpeg by Yuquan Yuan (10570747)

“…However, aggrephagy functions within the tumor microenvironment (TME) in endometrial cancer (EC) remain to be elucidated.…”