AAIC Travel Fellowship Awarded to Michael Romano
Michael Romano, MD/PhD student in the Kolachalama Lab, has been awarded a travel fellowship for the 2021 Alzheimer’s Association International Conference (AAIC). AAIC is the largest international dementia conference where researchers and clinicians come together to share advances in treatment and prevention of Alzheimer’s Disease. The conference will take place in Denver, Colorado in the […]
Shangran Qiu Awarded the 2021 Alvaro Rocarro Memorial Prize
Congratulations to Shangran Qiu, Bioinformatics Analyst in the Kolachalama Lab, for receiving the 2021 Alvaro Rocarro Memorial Prize. This award is given to individuals by the Department of Physics at Boston University in recognition of outstanding research achievement.
Thesis Defense – Xingyi Shi
Congratulations to CBM PhD candidate Xingyi Shi, who passed her thesis defense, “Bronchial Gene Expression Associated with Airway Premalignancy and Lung Cancer Subtypes,” on Monday, June 21. Xingyi is a member of the Spira/Lenburg and Beane labs and defended virtually.
CBM Faculty Members Awarded CTSI 2021 Integrated Pilot Grants
Congratulations to Drs. Jennifer Beane, Joshua Campbell, Vijaya Kolachalama and Sarah Mazzilli for receiving Integrated Pilot Grants from BU Clinical & Translational Science Institute. Please see below for more information on their awards. Identifying Coding and Non-Coding Genomic Alterations Associated with Aggressive Prostate Cancer in African American Men Recipients: Christopher Heaphy, Joshua Campbell & Rachel […]
Deep learning-driven quantification of interstitial fibrosis in digitized kidney biopsies
(Source: The American Journal of Pathology) New publication from Dr. Vijaya Kolachalama, Assistant Professor of Medicine in the Section of Computational Biomedicine. See below for a brief summary and click the link to read the whole article. Interstitial fibrosis and tubular atrophy (IFTA) on a renal biopsy are strong indicators of disease chronicity and prognosis. […]
Subchondral bone length in knee osteoarthritis: A deep learning derived imaging measure and its association with radiographic and clinical outcomes
(Source: Wiley Online Library) New Publication from Dr. Vijaya Kochalama, Assistant Professor of Medicine in the Section of Computational Biomedicine. Please see below for a brief summary and click the link to read the full article. Objective Develop a bone shape measure that reflects the extent of cartilage loss and bone flattening in knee osteoarthritis […]
Thesis Defenses – Zhe Wang and Ke Xu
Congratulations to CBM PhD candidates Zhe Wang and Ke Xu, who both passed their thesis defenses on Thursday, April 8. Zhe Wang, of the Campbell lab, defended his thesis entitled“Enhancing Preprocessing and Clustering of Single-Cell RNA Sequencing Data” and Ke Xu, of the Spira/Lenburg lab, defended his thesis entitled “Airway Gene Expression Alterations in Association […]
Job Opportunity – Postdoctoral Associate
We are looking for a postdoctoral associate to develop methods to analyze NGS data including single-cell RNA sequencing and CITE-seq, whole exome/genome DNA sequencing, and bulk RNA sequencing in projects related to the genomic characterization of lung premalignant lesions in the “Pre-Cancer Genome Atlas” consortium funded by Human Tumor Atlas Network https://humantumoratlas.org/hta3/). Therefore, data analysis […]
Animalcules: interactive microbiome analytics and visualization in R
Source: BMC (Part of Springer Nature) New publication from Dr. Evan Johnson, Dr. Stefano Monti, Anthony Federico and collaborators – see below for the abstract and to read more. Background Microbial communities that live in and on the human body play a vital role in health and disease. Recent advances in sequencing technologies have enabled […]
Enhancing magnetic resonance imaging-driven Alzheimer’s disease classification performance using generative adversarial learning
Source: BMC (Part of Springer Nature) New publication from Dr. Vijaya Kolachalama and collaborators – see below for the abstract and to read more. Background Generative adversarial networks (GAN) can produce images of improved quality but their ability to augment image-based classification is not fully explored. We evaluated if a modified GAN can learn from […]