Endometrial cancer (EC) is the third largest tumor of the female reproductive tract, with a high incidence and the population tend to become younger, which seriously threatens women's health. In recent years, the research on endometrial cancer has made great progress, including pathological mechanism, risk factors, and traditional surgical treatment. This article summarizes the progress of etiological research, the risk factors that cause endometrial cancer and the existing treatment methods, in order to provide value for the clinical research of endometrial cancer.
{"title":"The Scientific Research Progress of Endometrial Cancer","authors":"Sibang Chen","doi":"10.1145/3498731.3498759","DOIUrl":"https://doi.org/10.1145/3498731.3498759","url":null,"abstract":"Endometrial cancer (EC) is the third largest tumor of the female reproductive tract, with a high incidence and the population tend to become younger, which seriously threatens women's health. In recent years, the research on endometrial cancer has made great progress, including pathological mechanism, risk factors, and traditional surgical treatment. This article summarizes the progress of etiological research, the risk factors that cause endometrial cancer and the existing treatment methods, in order to provide value for the clinical research of endometrial cancer.","PeriodicalId":166893,"journal":{"name":"Proceedings of the 2021 10th International Conference on Bioinformatics and Biomedical Science","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125591936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. M. Ponsiglione, Teresa Angela Trunfio, Giovanni Rossi, A. Borrelli, Maria Romano
Knee arthroplasty is one of the most commonly performed procedures within a hospital. The progressive aging of the population and the spread of clinical conditions such as obesity will lead to an increasing use of this procedure. Therefore, being able to make the process related to this procedure more effective and efficient becomes strategic within hospitals, subject to increasingly stringent clinical and financial pressures. A useful parameter for this purpose is the length of stay (LOS), whose early prediction allows for better bed management and resource allocation, models patient expectations and facilitates discharge planning. In this work, the data of 124 patients who underwent knee surgery in the two-year period 2019-2020 at the San Giovanni di Dio and Ruggi d'Aragona university hospital were studied using multiple linear regression and machine learning algorithms in order to evaluate and predict how patient data affect LOS.
{"title":"Modelling the length of hospital stay after knee replacement surgery through Machine Learning and Multiple Linear Regression at “San Giovanni di Dio e Ruggi d'Aragona” University Hospital","authors":"A. M. Ponsiglione, Teresa Angela Trunfio, Giovanni Rossi, A. Borrelli, Maria Romano","doi":"10.1145/3498731.3498748","DOIUrl":"https://doi.org/10.1145/3498731.3498748","url":null,"abstract":"Knee arthroplasty is one of the most commonly performed procedures within a hospital. The progressive aging of the population and the spread of clinical conditions such as obesity will lead to an increasing use of this procedure. Therefore, being able to make the process related to this procedure more effective and efficient becomes strategic within hospitals, subject to increasingly stringent clinical and financial pressures. A useful parameter for this purpose is the length of stay (LOS), whose early prediction allows for better bed management and resource allocation, models patient expectations and facilitates discharge planning. In this work, the data of 124 patients who underwent knee surgery in the two-year period 2019-2020 at the San Giovanni di Dio and Ruggi d'Aragona university hospital were studied using multiple linear regression and machine learning algorithms in order to evaluate and predict how patient data affect LOS.","PeriodicalId":166893,"journal":{"name":"Proceedings of the 2021 10th International Conference on Bioinformatics and Biomedical Science","volume":"90 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134506375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In 2020, around ten million people worldwide were diagnosed with cancer. Being one of the leading causes of death, cancer contributes to a large portion of annual deaths globally. Among them, one of the most common cancers, colorectal cancer, caused around 935 000 deaths in 2020. Cancer is a genetic disease, caused by mutations in oncogenes and tumour suppressor genes. Finding effective diagnosis and treatment methods is one of the most pressing concerns regarding to biomedical science. In the past, the human intestinal microbiota, composed of a huge number of microorganisms including bacteria, fungi and viruses residing in the intestine, has not received much attention and was not considered a factor in disease development. However, increasing evidence have revealed their crucial roles in promoting and suppressing different diseases, including colorectal cancer (CRC), which that is a measure cause of death. The dysbiosis of the intestinal microbiota can result in infection of opportunistic bacteria, gastrointestinal malignancy, metabolic disorders, psychological diseases, and autoimmune diseases. The symbiosis of intestinal microbiota, in contrast, can alter these changes and increase host fitness. Many factors can alter the host's gut microbiota, including sex, age, diet, genetics, geographical conditions including climate and people living around you. This review discusses the different mechanisms of microbiota-induced carcinogenesis of CRC, as well as the potential application of the human intestinal microbiota.
{"title":"A review of Colorectal Cancer and Intestinal Microbiota","authors":"J. Tian","doi":"10.1145/3498731.3498757","DOIUrl":"https://doi.org/10.1145/3498731.3498757","url":null,"abstract":"In 2020, around ten million people worldwide were diagnosed with cancer. Being one of the leading causes of death, cancer contributes to a large portion of annual deaths globally. Among them, one of the most common cancers, colorectal cancer, caused around 935 000 deaths in 2020. Cancer is a genetic disease, caused by mutations in oncogenes and tumour suppressor genes. Finding effective diagnosis and treatment methods is one of the most pressing concerns regarding to biomedical science. In the past, the human intestinal microbiota, composed of a huge number of microorganisms including bacteria, fungi and viruses residing in the intestine, has not received much attention and was not considered a factor in disease development. However, increasing evidence have revealed their crucial roles in promoting and suppressing different diseases, including colorectal cancer (CRC), which that is a measure cause of death. The dysbiosis of the intestinal microbiota can result in infection of opportunistic bacteria, gastrointestinal malignancy, metabolic disorders, psychological diseases, and autoimmune diseases. The symbiosis of intestinal microbiota, in contrast, can alter these changes and increase host fitness. Many factors can alter the host's gut microbiota, including sex, age, diet, genetics, geographical conditions including climate and people living around you. This review discusses the different mechanisms of microbiota-induced carcinogenesis of CRC, as well as the potential application of the human intestinal microbiota.","PeriodicalId":166893,"journal":{"name":"Proceedings of the 2021 10th International Conference on Bioinformatics and Biomedical Science","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134144352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jingtao Chen, Zeng Lin, Shoujun Zhou, Tiexiang Wen, Quan Zeng
Objective: The deformation field inside the soft tissue is useful to predict and track the specific target of needle insertion. Finite element (FE) provides a sensorless way to reconstruct the deformation field inside soft tissue. However, the time-consuming model meshing makes it difficult to automate the reconstruction during needle insertion operation. The purpose of this work is to present a numerical method that can automatically reconstruction of deformation field of large-deformed soft tissue during needle insertion. Methods: Reproducing kernel particle method (RKPM) was used to reconstruct the deformation and stress field of soft tissue with real-time acquired displacement and force boundary conditions. The tissue crack was simulated by employing a node split mechanism. The validation experiment involves puncturing a silicone phantom with a robotic arm integrated with a needle. Results: The reconstructed displacements approach the experimental measurements with the average error of 0.15mm, 0.30mm, 0.63mm, and 0.55mm respectively at 12mm, 24mm, 36mm, and 40mm insertion depths. The reconstructed data have respectively 88.9%, 50%, 16.7%, and 27.8% nodes with an absolute error of less than 0.3mm (2 pixels). The stress relaxation of the silicon model has been revealed and be used to qualitatively explain the reconstruction error. Von-mises stress field has been also presented and registered into the X-ray image. Conclusion: The proposed meshfree-based method has acceptable accuracy for reconstructing the deformation field inside the large-deformed organ.
{"title":"A Meshfree Method for Deformation Field Reconstruction of Soft Tissue in Needle Insertion","authors":"Jingtao Chen, Zeng Lin, Shoujun Zhou, Tiexiang Wen, Quan Zeng","doi":"10.1145/3498731.3498738","DOIUrl":"https://doi.org/10.1145/3498731.3498738","url":null,"abstract":"Objective: The deformation field inside the soft tissue is useful to predict and track the specific target of needle insertion. Finite element (FE) provides a sensorless way to reconstruct the deformation field inside soft tissue. However, the time-consuming model meshing makes it difficult to automate the reconstruction during needle insertion operation. The purpose of this work is to present a numerical method that can automatically reconstruction of deformation field of large-deformed soft tissue during needle insertion. Methods: Reproducing kernel particle method (RKPM) was used to reconstruct the deformation and stress field of soft tissue with real-time acquired displacement and force boundary conditions. The tissue crack was simulated by employing a node split mechanism. The validation experiment involves puncturing a silicone phantom with a robotic arm integrated with a needle. Results: The reconstructed displacements approach the experimental measurements with the average error of 0.15mm, 0.30mm, 0.63mm, and 0.55mm respectively at 12mm, 24mm, 36mm, and 40mm insertion depths. The reconstructed data have respectively 88.9%, 50%, 16.7%, and 27.8% nodes with an absolute error of less than 0.3mm (2 pixels). The stress relaxation of the silicon model has been revealed and be used to qualitatively explain the reconstruction error. Von-mises stress field has been also presented and registered into the X-ray image. Conclusion: The proposed meshfree-based method has acceptable accuracy for reconstructing the deformation field inside the large-deformed organ.","PeriodicalId":166893,"journal":{"name":"Proceedings of the 2021 10th International Conference on Bioinformatics and Biomedical Science","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114934410","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Microbial technology has been widely used in various industries, to study the application of microbial technology in nanomaterial patches. The nanotube arrays were synthesized by microbial technology, and the nanopatch particles were further prepared with a JEM-2010 transmission electron microscope (Joep, Japan) operating at a voltage set to 200 kV.Test the performance of nanomaterials with application of microbial technology in nanomaterial patches according to the results of the electrocatalytic activity test, Au-TiO2 nanocomposites have obvious electrocatalytic activity in oxygen reduction, develop the photocatalytic performance test, further study the photocatalytic activity of nanocomposites with different Au loading,The activity order was concluded to be Au-TiO2 (4.5%) > Au-TiO2 (3.4%) > Au-TiO2 (1.4%) > Au-TiO2 (6.4%) > TiO2.
{"title":"Application of microbial technology to nanomaterial patches","authors":"Rui-Rui Su","doi":"10.1145/3498731.3498763","DOIUrl":"https://doi.org/10.1145/3498731.3498763","url":null,"abstract":"Microbial technology has been widely used in various industries, to study the application of microbial technology in nanomaterial patches. The nanotube arrays were synthesized by microbial technology, and the nanopatch particles were further prepared with a JEM-2010 transmission electron microscope (Joep, Japan) operating at a voltage set to 200 kV.Test the performance of nanomaterials with application of microbial technology in nanomaterial patches according to the results of the electrocatalytic activity test, Au-TiO2 nanocomposites have obvious electrocatalytic activity in oxygen reduction, develop the photocatalytic performance test, further study the photocatalytic activity of nanocomposites with different Au loading,The activity order was concluded to be Au-TiO2 (4.5%) > Au-TiO2 (3.4%) > Au-TiO2 (1.4%) > Au-TiO2 (6.4%) > TiO2.","PeriodicalId":166893,"journal":{"name":"Proceedings of the 2021 10th International Conference on Bioinformatics and Biomedical Science","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129883069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Obstructive Sleep Apnea (OSA) is a sleep-breathing disorder accompanied by multiple complications, and often associates with autonomic dysfunction. Sample entropy based on Gramian Angular Summation Field image (CSpEn2D) for OSA autonomic nervous system (ANS) study and analysis. We used 60 ECG records from the Physionet database. Low frequency to high frequency power (LF/HF) ratio could not distinguish normal OSA group from moderate OSA group, while CSpEn2D could significantly distinguish normal OSA group, mild-moderate OSA group and severe OSA group (P < 0.05). In terms of disease screening, the accuracy of CSpEn2D was 90.0% higher than that of LF/HF. At the same time, the CSpEn2D and apnea hypoventilation index (AHI) correlation significantly stronger (|R| = 0.727, p = 0). Hence, the CSpEn2D takes in a certain degree of clinical application prospects, and It is an effective indicator of OSA single feature screening.
阻塞性睡眠呼吸暂停(OSA)是一种伴有多种并发症的睡眠呼吸障碍，常伴有自主神经功能障碍。基于Gramian角和场图像(CSpEn2D)的样本熵用于OSA自主神经系统(ANS)的研究与分析。我们使用了60条来自Physionet数据库的心电图记录。低频与高频功率(LF/HF)比值不能区分正常OSA组和中度OSA组，而CSpEn2D能显著区分正常OSA组、轻中度OSA组和重度OSA组(P < 0.05)。在疾病筛查方面，CSpEn2D的准确率比LF/HF高90.0%。同时，CSpEn2D与呼吸暂停低通气指数(AHI)相关性显著增强(|R| = 0.727, p = 0)，因此CSpEn2D具有一定的临床应用前景，是OSA单一特征筛查的有效指标。
{"title":"Obstructive Sleep Apnea Heart Rate Variability Analysis using Gramian Angular Field images and Two-dimensional Sample Entropy","authors":"Lan Tang, Guanzheng Liu","doi":"10.1145/3498731.3498744","DOIUrl":"https://doi.org/10.1145/3498731.3498744","url":null,"abstract":"Obstructive Sleep Apnea (OSA) is a sleep-breathing disorder accompanied by multiple complications, and often associates with autonomic dysfunction. Sample entropy based on Gramian Angular Summation Field image (CSpEn2D) for OSA autonomic nervous system (ANS) study and analysis. We used 60 ECG records from the Physionet database. Low frequency to high frequency power (LF/HF) ratio could not distinguish normal OSA group from moderate OSA group, while CSpEn2D could significantly distinguish normal OSA group, mild-moderate OSA group and severe OSA group (P < 0.05). In terms of disease screening, the accuracy of CSpEn2D was 90.0% higher than that of LF/HF. At the same time, the CSpEn2D and apnea hypoventilation index (AHI) correlation significantly stronger (|R| = 0.727, p = 0). Hence, the CSpEn2D takes in a certain degree of clinical application prospects, and It is an effective indicator of OSA single feature screening.","PeriodicalId":166893,"journal":{"name":"Proceedings of the 2021 10th International Conference on Bioinformatics and Biomedical Science","volume":"123 14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126431522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
I. Loperto, R. Alfano, A. Scala, Giuseppe Ferrucci, A. Borrelli, Teresa Angela Trunfio, P. Gargiulo
The CoViD-19 pandemic imposed severe social containment measures in all sectors on countries around the world. This led to a general reorganization of the health sector involving all medical specialties. In addition, the growing influx of CoViD-19 patients in serious or critical conditions has led to a reallocation of healthcare personnel and a block of elective procedures deemed deferrable. Therefore, the surgical departments are those that have most undergone a modification of the activity normally provided. In fact, various protocols have been adopted to help doctors identify those cases in which a delay in surgery could cause serious damage to patients and on which it is necessary to intervene, thus also improving the appropriateness of admission. This study was conducted in the Complex Operative Unit (C.O.U.) of Otorhinolaryngology of the University Hospital of Salerno (Italy) "San Giovanni di Dio e Ruggi d'Aragona". Data were collected on patients who entered hospital in 2019 and 2020. Statistical analysis and logistic regression were used to quantify the effect of CoViD-19 on C.O.U.. To do this, the year 2019 was used as a reference of the normal activity of the department and compared with what was achieved in 2020, in the midst of the pandemic. Logistic regression performed on these data showed an increase in length of hospital stay (LOS) and diagnostic related group (DRG) weight in 2020 thus showing increased appropriateness of care offered.
{"title":"The Impact of CoViD-19 on hospital activities: the case of the C.O.U. Otorhinolaryngology","authors":"I. Loperto, R. Alfano, A. Scala, Giuseppe Ferrucci, A. Borrelli, Teresa Angela Trunfio, P. Gargiulo","doi":"10.1145/3498731.3498756","DOIUrl":"https://doi.org/10.1145/3498731.3498756","url":null,"abstract":"The CoViD-19 pandemic imposed severe social containment measures in all sectors on countries around the world. This led to a general reorganization of the health sector involving all medical specialties. In addition, the growing influx of CoViD-19 patients in serious or critical conditions has led to a reallocation of healthcare personnel and a block of elective procedures deemed deferrable. Therefore, the surgical departments are those that have most undergone a modification of the activity normally provided. In fact, various protocols have been adopted to help doctors identify those cases in which a delay in surgery could cause serious damage to patients and on which it is necessary to intervene, thus also improving the appropriateness of admission. This study was conducted in the Complex Operative Unit (C.O.U.) of Otorhinolaryngology of the University Hospital of Salerno (Italy) \"San Giovanni di Dio e Ruggi d'Aragona\". Data were collected on patients who entered hospital in 2019 and 2020. Statistical analysis and logistic regression were used to quantify the effect of CoViD-19 on C.O.U.. To do this, the year 2019 was used as a reference of the normal activity of the department and compared with what was achieved in 2020, in the midst of the pandemic. Logistic regression performed on these data showed an increase in length of hospital stay (LOS) and diagnostic related group (DRG) weight in 2020 thus showing increased appropriateness of care offered.","PeriodicalId":166893,"journal":{"name":"Proceedings of the 2021 10th International Conference on Bioinformatics and Biomedical Science","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133924024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
E. Montella, A. Scala, Maddalena Di Lillo, M. Lamberti, L. Donisi, M. Triassi, Martina Profeta
Healthcare Associated Infections (HAIs) has significant consequences both on the quality and the economy of the nation's healthcare system. Numerous factors influence the HAIs contraction during hospitalization. Is it possible to identify the principal risk factors leading to HAIs and try to avoid its contraction? In this work we answer this question by correlating patients’ gender, age, McCabe score and the eventual use of urinary catheter, central intravascular catheter and peripheral intravenous catheter with the probability to contract HAIs, by using the machine learning technique. Data of 226 patients hospitalized in 2019 were collected at the University Hospital “Federico II” in Naples in the clinical medicine area. Descriptive statistics was performed and logistic regression was used to test the association between HAIs, and the different risk factors under study. Results show that the variables influencing HAIs contraction were the McCabe score, the clinical use of a central intravascular catheter and the hospitalization at the infectious diseases department.
{"title":"Impact of hospital infections in the clinical medicine area of “Federico II” University Hospital of Naples assessed by means of statistical analysis and logistic regression","authors":"E. Montella, A. Scala, Maddalena Di Lillo, M. Lamberti, L. Donisi, M. Triassi, Martina Profeta","doi":"10.1145/3498731.3498764","DOIUrl":"https://doi.org/10.1145/3498731.3498764","url":null,"abstract":"Healthcare Associated Infections (HAIs) has significant consequences both on the quality and the economy of the nation's healthcare system. Numerous factors influence the HAIs contraction during hospitalization. Is it possible to identify the principal risk factors leading to HAIs and try to avoid its contraction? In this work we answer this question by correlating patients’ gender, age, McCabe score and the eventual use of urinary catheter, central intravascular catheter and peripheral intravenous catheter with the probability to contract HAIs, by using the machine learning technique. Data of 226 patients hospitalized in 2019 were collected at the University Hospital “Federico II” in Naples in the clinical medicine area. Descriptive statistics was performed and logistic regression was used to test the association between HAIs, and the different risk factors under study. Results show that the variables influencing HAIs contraction were the McCabe score, the clinical use of a central intravascular catheter and the hospitalization at the infectious diseases department.","PeriodicalId":166893,"journal":{"name":"Proceedings of the 2021 10th International Conference on Bioinformatics and Biomedical Science","volume":"556 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117320023","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Lung cancer and its various types are a leading cause of death across the globe. Many studies have pointed out that microRNAs (miRNAs) dysregulation can be a useful marker for variety of cancers, including lung cancer. Successful treatment of all cancers depends on clinical expertise, treatment resources, and the stage at the time of diagnosis. Therefore, we made an effort to find a novel miRNA expression marker to determine the stage of lung adenocarcinoma (LUAD). In this manuscript, we proposed a stack ensemble method for classifying early and advanced stage LUAD using miRNA expression data. In our benchmark dataset, 445 were early-stage, and 114 were advanced-stage LUAD patients. The benchmark dataset was imbalanced, so to balance our dataset, we used Synthetic Minority Over Sampling Technique (SMOTE). We then divided the balanced LUAD patient’s dataset into training dataset (80%) and testing dataset (20%). Random Forest (RF) technique was implemented for the selection of best optimal features (miRNA sequence expression) out of 1880 miRNAs, followed by machine learning (ML) Stack ensemble method to classify the early and advanced stage LUAD. Compared to the traditional ML classifier used as a baseline, the stack ensemble method classified the early and advanced stage LUAD more efficiently with 99% accuracy. The proposed method’s precision for early-stage LUAD was 92% and for advance stage LUAD 84%. Similarly, the recall of the proposed method for early and advanced stage LUAD was 82% and 93%, respectively. The F1-Score of the proposed method for early and advanced stage LUAD was 87% and 88%, respectively. To conclude, the results obtained clearly showed the effectiveness of ensemble method for the classification of early and advanced stage LUAD using miRNA expression data. The top 10 miRNAs sequences identified by the model can help make the best treatment decisions for early and advanced stage LUAD to increase the chances of survival.
{"title":"Stacking Ensemble Method for Early and Advanced Stage Lung Adenocarcinoma Classification Based on miRNA Expression","authors":"Adeel Khan, N. He, Irfan Tariq, Zhiyang Li","doi":"10.1145/3498731.3498742","DOIUrl":"https://doi.org/10.1145/3498731.3498742","url":null,"abstract":"Lung cancer and its various types are a leading cause of death across the globe. Many studies have pointed out that microRNAs (miRNAs) dysregulation can be a useful marker for variety of cancers, including lung cancer. Successful treatment of all cancers depends on clinical expertise, treatment resources, and the stage at the time of diagnosis. Therefore, we made an effort to find a novel miRNA expression marker to determine the stage of lung adenocarcinoma (LUAD). In this manuscript, we proposed a stack ensemble method for classifying early and advanced stage LUAD using miRNA expression data. In our benchmark dataset, 445 were early-stage, and 114 were advanced-stage LUAD patients. The benchmark dataset was imbalanced, so to balance our dataset, we used Synthetic Minority Over Sampling Technique (SMOTE). We then divided the balanced LUAD patient’s dataset into training dataset (80%) and testing dataset (20%). Random Forest (RF) technique was implemented for the selection of best optimal features (miRNA sequence expression) out of 1880 miRNAs, followed by machine learning (ML) Stack ensemble method to classify the early and advanced stage LUAD. Compared to the traditional ML classifier used as a baseline, the stack ensemble method classified the early and advanced stage LUAD more efficiently with 99% accuracy. The proposed method’s precision for early-stage LUAD was 92% and for advance stage LUAD 84%. Similarly, the recall of the proposed method for early and advanced stage LUAD was 82% and 93%, respectively. The F1-Score of the proposed method for early and advanced stage LUAD was 87% and 88%, respectively. To conclude, the results obtained clearly showed the effectiveness of ensemble method for the classification of early and advanced stage LUAD using miRNA expression data. The top 10 miRNAs sequences identified by the model can help make the best treatment decisions for early and advanced stage LUAD to increase the chances of survival.","PeriodicalId":166893,"journal":{"name":"Proceedings of the 2021 10th International Conference on Bioinformatics and Biomedical Science","volume":"356 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123105866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Gabriel Luis de Araújo e Freitas, Cristiano J. M. R. Mendes, Vinicius P. Goncalves
Compressive Sensing (CS) algorithms are widely adopted for the reconstruction of Magnetic Resonance images (MRI). Owing to differences in the nature of the measurements acquisition processes, these techniques are still not often employed for X-ray Computed Tomography (CT) imaging. However, CS has the potential of reducing the amount of emitted radiation during the CT acquisition process. This study establishes a structure, based on one-dimensional reconstructions, to build CT images using numerical optimization with direct methods, as opposed to traditional indirect methods, such as Conjugate Gradient. The structure was evaluated with regard to its ideal measurements and obtained better results, in terms of signal-to-noise ratio, with respect the reconstruction based on a Filtered Back Projection (FBP) algorithm.
{"title":"The Formation of Computed Tomography Images from Compressed Sampled One-dimensional Reconstructions","authors":"Gabriel Luis de Araújo e Freitas, Cristiano J. M. R. Mendes, Vinicius P. Goncalves","doi":"10.1145/3498731.3498733","DOIUrl":"https://doi.org/10.1145/3498731.3498733","url":null,"abstract":"Compressive Sensing (CS) algorithms are widely adopted for the reconstruction of Magnetic Resonance images (MRI). Owing to differences in the nature of the measurements acquisition processes, these techniques are still not often employed for X-ray Computed Tomography (CT) imaging. However, CS has the potential of reducing the amount of emitted radiation during the CT acquisition process. This study establishes a structure, based on one-dimensional reconstructions, to build CT images using numerical optimization with direct methods, as opposed to traditional indirect methods, such as Conjugate Gradient. The structure was evaluated with regard to its ideal measurements and obtained better results, in terms of signal-to-noise ratio, with respect the reconstruction based on a Filtered Back Projection (FBP) algorithm.","PeriodicalId":166893,"journal":{"name":"Proceedings of the 2021 10th International Conference on Bioinformatics and Biomedical Science","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124086889","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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