Indiana University-Purdue University Indianapolis (IUPUI) / The availability of highly-distributed computing compliments the proliferation of next generation sequencing (NGS) and genome-wide association studies (GWAS) datasets. These data sets are often complex, poorly annotated or require complex domain knowledge to sensibly manage. These novel datasets provide a rare, multi-dimensional omics (proteomics, transcriptomics, and genomics) view of a single sample or patient. Previously, biologists assumed a strict adherence to the central dogma: replication, transcription and translation. Recent studies in genomics and proteomics emphasize that this is not the case. We must employ big-data methodologies to not only understand the biogenesis of these molecules, but also their disruption in disease states. The Cancer Genome Atlas (TCGA) provides high-dimensional patient data and illustrates the trends that occur in expression profiles and their alteration in many complex disease states. I will ultimately create a bottom-up multi-omics approach to observe biological systems using big data techniques. I hypothesize that big data and systems biology approaches can be applied to public datasets to identify important subsets of genes in cancer phenotypes. By exploring these signatures, we can better understand the role of amplification and transcript alterations in cancer.

As a non-robotic minimally invasive surgery, endoscopic surgery is one of the widely used surgeries for the medical domain to reduce the risk of infection, incisions, and the discomfort of the patient. The endoscopic surgery procedure, also named surgical workflow in this work, can be divided into different sub-phases. During the procedure, the surgeon inserts a thin, flexible tube with a video camera through a small incision or a natural orifice like the mouth or nostrils. The surgeon can utilize tiny surgical instruments while viewing organs on the computer monitor through these tubes. The surgery only allows a limited number of instruments simultaneously appearing in the body, requiring a sufficient instrument preparation method. Therefore, surgical workflow anticipation, including surgical instrument and phase anticipation, is essential for an intra-operative decision-support system. It deciphers the surgeon's behaviors and the patient's status to forecast surgical instrument and phase occurrence before they appear, supporting instrument preparation and computer-assisted intervention (CAI) systems. In this work, we investigate an unexplored surgical workflow anticipation problem by proposing an Instrument Interaction Aware Anticipation Network (IIA-Net). Spatially, it utilizes rich visual features about the context information around the instrument, i.e., instrument interaction with their surroundings. Temporally, it allows for a large receptive field to capture the long-term dependency in the long and untrimmed surgical videos through a causal dilated multi-stage temporal convolutional network. Our model enforces an online inference with reliable predictions even with severe noise and artifacts in the recorded videos. Extensive experiments on Cholec80 dataset demonstrate the performance of our proposed method exceeds the state-of-the-art method by a large margin (1.40 v.s. 1.75 for inMAE and 2.14 v.s. 2.68 for eMAE).

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