A Deep Learning Approach to Causal Inference in Human Genomics using Counterfactual Reasoning DOI Creative Commons
Tshepo Kitso Gobonamang

Опубликована: Июль 17, 2023

In this paper, we delve into the intricate realm of human genomics, presenting a novel design that leverages deep learning and counterfactual reasoning for causal inference. We postulate mutations occurring within DNA sequences have potential to instigate diseases by interrupting essential biological processes, hypothesis fundamentally drives research. To test this, undertaken meticulous extraction key attributes from range databases hosted National Center Biotechnology Information (NCBI). These are subsequently processed using one-hot encoding, technique effectively transforms categorical variables form could be provided machine algorithms. A sophisticated model is then utilized ascertain accuracy hypothesis. The output, depicted as graph, elucidates relationships interactions between in question, providing graphical representation proposed Our research suggests strategic modifications sequence or alterations set induce significant changes processes. This, turn, can lead structure function proteins, cornerstone cellular operations. also underline importance statements formulating hypotheses driving intelligent behavior. Despite their untestable nature inherent subjectivity, these counterfactuals serve powerful tools comprehending predicting outcomes. implications extend beyond academic interest. It provides pathway deeper understanding genomics holds promise development targeted therapies genetic diseases. fosters possibility personalized medicine therapeutic strategies alter course disease at level, potentially revolutionizing healthcare.

Язык: Английский

MFI2 upregulation promotes malignant progression through EGF/FAK signaling in oral cavity squamous cell carcinoma DOI Creative Commons
Wei‐Chen Yen, Kai‐Ping Chang, Cheng‐Yi Chen

и другие.

Cancer Cell International, Год журнала: 2023, Номер 23(1)

Опубликована: Июнь 12, 2023

Abstract Oral squamous cell carcinoma (OSCC) is the predominant histological type of head and neck (HNSCC). By comparing differentially expressed genes (DEGs) in OSCC-TCGA patients with copy number variations (CNVs) that we identify OSCC-OncoScan dataset, herein identified 37 dysregulated candidate genes. Among these potential genes, 26 have been previously reported as proteins or HNSCC. 11 novel candidates, overall survival analysis revealed melanotransferrin (MFI2) most significant prognostic molecular patients. Another independent Taiwanese cohort confirmed higher MFI2 transcript levels were significantly associated poor prognosis. Mechanistically, found knockdown reduced viability, migration invasion via modulating EGF/FAK signaling OSCC cells. Collectively, our results support a mechanistic understanding role for promoting invasiveness OSCC.

Язык: Английский

Процитировано

1
The potential mechanism of WT1‐associated protein‐induced N‐6‐methyladenosine modification of colony‐stimulating factor 2 in the progression of oral squamous cell carcinoma by JAK/STAT3 pathway regulation DOI

Ruobing Peng,

Shengjun Jiang,

Zhongzhi Jin

и другие.

European Journal Of Oral Sciences, Год журнала: 2024, Номер 132(4)

Опубликована: Июнь 3, 2024

Abstract Colony‐stimulating factor 2 (CSF2) plays a regulatory role in numerous cancers. However, there is needed to investigate the of CSF2 oral squamous cell carcinoma (OSCC) malignant phenotype and specific mechanisms N‐6‐methyladenosine (m6A) modification. Therefore, we investigated mechanism m6A‐modified by WT1‐associated protein (WTAP) OSCC via qRT–PCR, western blot, WTAP overexpression OSCC. In panel OSCCs, Kaplan–Meier plot analysis indicated that high expression was associated with poorer prognosis. Cell functional experiments revealed enrichment promoted proliferation migration cells activating JAK/STAT3 pathway, whereas reduced resulted decline blocking pathway. This study also confirmed enhanced m6A level facilitated silencing blocked invasive reversed malignancy induced overexpression. Overall, this demonstrated mediates modification which an oncogenic development can be target for treatment patients

Язык: Английский

Процитировано

0
Dobinin K Displays Antiplasmodial Activity through Disruption of Plasmodium falciparum Mitochondria and Generation of Reactive Oxygen Species DOI Creative Commons
He Sun, B Liu,

Longfei He

и другие.

Molecules, Год журнала: 2024, Номер 29(19), С. 4759 - 4759

Опубликована: Окт. 8, 2024

Dobinin K is a novel eudesmane sesquiterpenoids compound isolated from the root of

Язык: Английский

Процитировано

0
CancerHubs: a systematic data mining and elaboration approach for identifying novel cancer-related protein interaction hubs DOI Creative Commons
Ivan Ferrari, Federica De Grossi,

Giancarlo Lai

и другие.

Briefings in Bioinformatics, Год журнала: 2024, Номер 26(1)

Опубликована: Ноя. 22, 2024

Conventional approaches to predict protein involvement in cancer often rely on defining either aberrant mutations at the single-gene level or correlating/anti-correlating transcript levels with patient survival. These are typically conducted independently and focus one a time, overlooking nucleotide substitutions outside of coding regions mutational co-occurrences genes within same interaction network. Here, we present CancerHubs, method that integrates unbiased data, clinical outcome predictions interactomics define novel cancer-related hubs. Through this approach, identified TGOLN2 as putative broad tumour suppressor EFTUD2 multiple myeloma oncogene.

Язык: Английский

Процитировано

0
A Deep Learning Approach to Causal Inference in Human Genomics using Counterfactual Reasoning DOI Creative Commons
Tshepo Kitso Gobonamang, Dimane Mpoeleng

Опубликована: Июль 17, 2023

<p>In this paper, we delve into the intricate realm of human genomics, presenting a novel design that leverages deep learning and counterfactual reasoning for causal inference. We postulate mutations occurring within DNA sequences have potential to instigate diseases by interrupting essential biological processes, hypothesis fundamentally drives research.</p> <p>To test this, undertaken meticulous extraction key attributes from range databases hosted National Center Biotechnology Information (NCBI). These are subsequently processed using one-hot encoding, technique effectively transforms categorical variables form could be provided machine algorithms.</p> <p>A sophisticated model is then utilized ascertain accuracy hypothesis. The output, depicted as graph, elucidates relationships interactions between in question, providing graphical representation proposed hypothesis.</p> <p>Our research suggests strategic modifications sequence or alterations set induce significant changes processes. This, turn, can lead structure function proteins, cornerstone cellular operations.</p> <p>We also underline importance statements formulating hypotheses driving intelligent behavior. Despite their untestable nature inherent subjectivity, these counterfactuals serve powerful tools comprehending predicting outcomes.</p> <p>The implications extend beyond academic interest. It provides pathway deeper understanding genomics holds promise development targeted therapies genetic diseases. fosters possibility personalized medicine therapeutic strategies alter course disease at level, potentially revolutionizing healthcare.</p>

Язык: Английский

Процитировано

0
A Deep Learning Approach to Causal Inference in Human Genomics using Counterfactual Reasoning DOI Creative Commons
Tshepo Kitso Gobonamang

Опубликована: Июль 17, 2023

In this paper, we delve into the intricate realm of human genomics, presenting a novel design that leverages deep learning and counterfactual reasoning for causal inference. We postulate mutations occurring within DNA sequences have potential to instigate diseases by interrupting essential biological processes, hypothesis fundamentally drives research. To test this, undertaken meticulous extraction key attributes from range databases hosted National Center Biotechnology Information (NCBI). These are subsequently processed using one-hot encoding, technique effectively transforms categorical variables form could be provided machine algorithms. A sophisticated model is then utilized ascertain accuracy hypothesis. The output, depicted as graph, elucidates relationships interactions between in question, providing graphical representation proposed Our research suggests strategic modifications sequence or alterations set induce significant changes processes. This, turn, can lead structure function proteins, cornerstone cellular operations. also underline importance statements formulating hypotheses driving intelligent behavior. Despite their untestable nature inherent subjectivity, these counterfactuals serve powerful tools comprehending predicting outcomes. implications extend beyond academic interest. It provides pathway deeper understanding genomics holds promise development targeted therapies genetic diseases. fosters possibility personalized medicine therapeutic strategies alter course disease at level, potentially revolutionizing healthcare.

Язык: Английский

Процитировано

0