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Taehun Song,Moonsub Lee,Inhwan Bae,변주연,안영길,Young Hoon Kim,천영진 대한화학회 2021 Bulletin of the Korean Chemical Society Vol.42 No.3
Cyclin-dependent kinase (CDK) 9 is a protein kinase that plays a major regulatory role in the process of transcription, thereby representing an attractive target in cancer therapy. A series of novel, highly potent, selective derivatives (coined compounds 8?15) were designed, synthesized, and evaluated for their inhibitory effect on CDK functions using cancer cell lines. Here, we showed that our compound 8 exhibited a potent CDK9 inhibitory activity in ICR mice, with an IC50 value of 2.3 nM as well as favorable pharmacokinetic properties. Using an MV4-11 xenograft mouse model, compound 8 showed antitumor efficacy at a dose of 10 mg/kg; compound 8 treatment was well tolerated, with no adverse effects on body weight or animal health. Our in vitro and in vivo findings strongly suggest that compound 8 holds great promise for the development of highly potent CDK9 inhibitors in anticancer approaches.
Taehun An,Iickho Song,Seungwon Lee,Hwang-Ki Min IEEE 2014 IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS Vol.13 No.12
<P>We address detection schemes of spectrum sensing for cognitive radio with multiple receive antennas operating over a wideband channel composed of a multitude of subbands. By taking the observations in all subbands into consideration in the likelihood functions for sensing a subband, the test statistics of the proposed schemes are functions of the sample covariance matrix in the subband under consideration and that in the subband exhibiting the lowest power spectral density. The false alarm and detection probabilities of the proposed schemes are analyzed theoretically and confirmed via simulations when the numbers of observations are the same for all the subbands. It is shown through computer simulations that the proposed schemes can provide considerable performance gains over conventional schemes for wideband spectrum sensing when the observations are spatially correlated and temporally independent/dependent.</P>
Taehun An,Iickho Song,Hyoungmoon Kwon,Yun Hee Kim,Seokho Yoon,Jinsoo Bae IEEE 2009 IEEE Transactions on Vehicular Technology VT Vol.58 No.7
<P>In this paper, we propose a near maximum likelihood (ML) scheme for the decoding of multiple-input-multiple-output (MIMO) systems. By employing the metric-first search method, Schnorr-Euchner enumeration, and branch-length thresholds in a single frame systematically, the proposed technique provides efficiency that is higher than those of other conventional near-ML decoding schemes. From simulation results, it is confirmed that the proposed scheme has computational complexity lower than those of other near-ML decoders while maintaining the bit error rate (BER) very close to the ML performance. The proposed scheme, in addition, possesses the capability of allowing flexible tradeoffs between the computational complexity and BER performance.</P>
김태훈(Taehun Kim),김난이(Nani Kim),송지연(Jiyeon Song),정현진(Hyeonjin Jeong),이은민(Eunmin Lee) 한국자료분석학회 2023 Journal of the Korean Data Analysis Society Vol.25 No.4
본 연구는 기계학습 중 하나인 랜덤 포레스트 모델을 통해 성인의 우울을 예측하고자 시도되었다. 모델의 학습을 위한 연구 대상은 국민건강영양조사 8기(2019-2021) 자료 중 2주 이상의 우울감을 가진 대상자 1,086명, 가지고 있지 않은 대상자 8,826명으로 전체 9,896명으로 입력 변수는 20개였다. 본 연구의 모델 구축 및 평가를 위해 모든 코드는 Python 3.9.7로 작성되었으며, 통계 및 모델 구축을 위해 SciPy 1.614, ELI5, Scikit-learn 1.2.2, 패키지가 사용되었다. 분석은 학습에 사용될 원시 자료의 상관관계와 평균, 표준편차, 빈도, 비율, 그리고 모델의 예측에 영향을 주는 변수들의 값과 모델의 종합적 성능을 평가하였다. 연구결과 우울증 예측에 영향을 주는 요인들로 스트레스, 성별, 직업, 신체활동, 건강 상태가 확인되었으며, 가장 큰 영향을 주는 요인은스트레스(0.099±0.008; 0.081±0.008)였다. 모델의 전반적 성능(AUC)은 0.920(95% CI, 0.919–0.921)로 정확도는 0.921(95% CI, 0.920-0.922)로 나타났다. 구축된 모델은 우울증의 패턴을 찾아낼 수있었으며, 임상 현장에서 우울증 선별에 있어 신속하고 정확한 결정을 지원할 수 있을 것이다. This study attempted to predict depression in adults using a random forest model, a type of machine learning. The research subjects for training the model were 1,086 subjects with depression for more than 2 weeks and 8,826 subjects without depression, totaling 9,896 subjects from the 8th Korea National Health and Nutrition Examination Survey (2019-2021), and 20 input variables. For model building and evaluation in this study, all code was written in Python 3.9.7, and packages SciPy 1.614, ELI5, and Scikit-learn 1.2.2 were used for statistics and model building. The analysis evaluated the correlations, means, standard deviations, frequencies, proportions, and values of the variables affecting the prediction of the model and the overall performance of the model. The results showed that stress, gender, occupation, physical activity, and health status were identified as factors affecting the prediction of depression, with stress being the most influential (0.099±0.008; 0.081±0.008). The overall performance (AUC) of the model was 0.920 (95% CI, 0.919-0.921) with an accuracy of 0.921 (95% CI, 0.920-0.922). The built model was able to detect patterns of depression and could support rapid and accurate decisions in screening for depression in clinical settings.