|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Early complications after one hundred and forty-four consercutive hip revisions with impacted morselized allograft bone and cement显示文摘 | Ewald O Isam A | 2002 | J Bone Joint Surg(AM)2002，84，8： | 1 |
| 2 | Effects of implantation of hypertrophied androgenic glands on sexual characters and physiology of the reproductive system in the female red claw crayfish,Cherax quadricarinatus显示文摘 | Isam K Tal K Uri A | 2001 | General and Comparative Endocrinology2001，，121： | 1 |
| 3 | Multicomponent corrosion inhibitor system for recirculating cooling water systems/based on nitrite, molybdate, and inorganic phosphorate显示文摘 | Borno A Isam M Khraishi M | 1989 | Corrosion1989，45，12： | 1 |
| 4 | Genetic relationships and reproductive isolation mechanism among the Fejervarya limnocharis complex from Indonesia(Java)and other Asian countries显示文摘 | Djong H.T Isam M.M Nishioka M Matsui M Ota H Kuramoto M Khan M.M.R Alam M.S De Silva A Khonsue W Sumida M | | 0，，： | 1 |
| 5 | Active cancellation system for radar cross section reduction显示文摘 | Isam A O and Alrasoul J A | 2013 | International Journal of Education and Research2013，7，1： | 1 |
| 6 | Role of survivin, whose gene is mapped to 17q25, in human neuroblastoma and identification of a novel dominant-negative isoform, survivin-beta/2B 显示文摘 | Isam A Kageyama H Hashizume K | 2000 | Med Pediatr Oncol2000，35，6： | 1 |
| 7 | Preparation and characterization ofpoly (methyl methacrylate)grafted sago starch using potassium persulfates as redax initiator显示文摘 | Isam Y Qudsieh A Fakhrul-Razi Suleyman A Muyibi | | 0，，： | 1 |
| 8 | Dynamic network models and driver information systems显示文摘 | Ben A M De P A Isam K | 1991 | Transportation Research:Part A1991，25，5： | 1 |
| 9 | Dynamic network models and driver information systems显示文摘 | MOSHE B A ANDRE D P ISAM K | 1991 | Transportation Research Part A General1991，25，5： | 1 |
| 10 | Multicomponent corrosion inhibitor system for recirculating cooling water systems/based on nitrite,molybdate,and inorganic phosphorate显示文摘 | Isam M Khraishi M | 1989 | Corrosion1989，45，12： | 1 |
| 11 | Active cancellation system for radar cross section reduction显示文摘 | Isam A O Abd A Jabar A | 2013 | International Journal of Education and Research2013，7，1： | 1 |
| 12 | A Novel Multi-Stage Bispectral Deep Learning Method for Protein Family Classification显示文摘Complex proteins are needed for many biological activities.Folding amino acid chains reveals their properties and functions.They support healthy tissue structure,physiology,and homeostasis.Precision medicine and treatments require quantitative protein identification and function.Despite technical advances and protein sequence data exploration,bioinformatics’“basic structure”problem—the automatic deduction of a protein’s properties from its amino acid sequence—remains unsolved.Protein function inference from amino acid sequences is the main biological data challenge.This study analyzes whether raw sequencing can characterize biological facts.A massive corpus of protein sequences and the Globin-like superfamily’s related protein families generate a solid vector representation.A coding technique for each sequence in each family was devised using two representations to identify each amino acid precisely.A bispectral analysis converts encoded protein numerical sequences into images for better protein sequence and family discrimination.Training and validation employed 70%of the dataset,while 30%was used for testing.This paper examined the performance of multistage deep learning models for differentiating between sixteen protein families after encoding and representing each encoded sequence by a higher spectral representation image(Bispectrum).Cascading minimized false positive and negative cases in all phases.The initial stage focused on two classes(six groups and ten groups).The subsequent stages focused on the few classes almost accurately separated in the first stage and decreased the overlapping cases between families that appeared in single-stage deep learning classification.The single-stage technique had 64.2%+/-22.8%accuracy,63.3%+/-17.1%precision,and a 63.2%+/19.4%F1-score.The two-stage technique yielded 92.2%+/-4.9%accuracy,92.7%+/-7.0%precision,and a 92.3%+/-5.0%F1-score.This work provides balanced,reliable,and precise forecasts for all families in all measures.It ensured that the new model was resilient to family variances and provided high-scoring results. | Amjed Al Fahoum Ala’a Zyout Hiam Alquran Isam Abu-Qasmieh | 2023 | Computers, Materials & Continua2023，，7： | 0 |
| 13 | An Innovative Bispectral Deep Learning Method for Protein Family Classification显示文摘Proteins are essential for many biological functions.For example,folding amino acid chains reveals their functionalities by maintaining tissue structure,physiology,and homeostasis.Note that quantifiable protein characteristics are vital for improving therapies and precision medicine.The automatic inference of a protein’s properties from its amino acid sequence is called“basic structure”.Nevertheless,it remains a critical unsolved challenge in bioinformatics,although with recent technological advances and the investigation of protein sequence data.Inferring protein function from amino acid sequences is crucial in biology.This study considers using raw sequencing to explain biological facts using a large corpus of protein sequences and the Globin-like superfamily to generate a vector representation.The power of two representations was used to identify each amino acid,and a coding technique was established for each sequence family.Subsequently,the encoded protein numerical sequences are transformed into an image using bispectral analysis to identify essential characteristics for discriminating between protein sequences and their families.A deep Convolutional Neural Network(CNN)classifies the resulting images and developed non-normalized and normalized encoding techniques.Initially,the dataset was split 70/30 for training and testing.Correspondingly,the dataset was utilized for 70%training,15%validation,and 15%testing.The suggested methods are evaluated using accuracy,precision,and recall.The non-normalized method had 70%accuracy,72%precision,and 71%recall.68%accuracy,67%precision,and 67%recall after validation.Meanwhile,the normalized approach without validation had 92.4%accuracy,94.3%precision,and 91.1%recall.Validation showed 90%accuracy,91.2%precision,and 89.7%recall.Note that both algorithms outperform the rest.The paper presents that bispectrum-based nonlinear analysis using deep learning models outperforms standard machine learning methods and other deep learning methods based on convolutional architecture.They offered the best inference performance as the proposed approach improves categorization and prediction.Several instances show successful multi-class prediction in molecular biology’s massive data. | Isam Abu-Qasmieh Amjed Al Fahoum Hiam Alquran Ala’a Zyout | 2023 | Computers, Materials & Continua2023，，5： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员